Dissertations / Theses on the topic 'Swarm based design'

Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 141-149).
Submitted to the Program in Media Arts and Sciences, School of Architecture and Planning, on December 8, 2017 in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Media, Arts and Sciences at the Massachusetts Institute of Technology Throughout history, Nature has always been part of the discourse in Design theory and practice. The Digital Age in Design brings about new computational tools, redefining the role of Nature in Design. In this thesis, I aim to expand the role of Nature in Design and digital fabrication by investigating distributed fabrication strategies for the production of constructs that are, at once, large in scale and materially tunable towards swarm-based design. Digital fabrication approaches can be classified with respect to two basic attributes: (1) the degree of material tailorability, and (2) the level of collaboration between fabrication units. Conventional manufacturing is typically confined to only one of these attribute axes, with certain approaches utilizing complex tunable materials but virtually no collaboration, and others assembling pre-fabricated building blocks with high levels of intercommunication between fabrication units. A similar pattern is mirrored in biological systems: silkworms, for example, deposit a multifunctional tunable material with minimal communication between organisms; while ants, bees and termites operate as multi-agent communicative entities assembling larger constructs out of simple, unifunctional, 'generic' materials. The purpose of this thesis is to depart from these uniaxial manufacturing approaches and develop a novel swarm-inspired distributed digital fabrication method capable of producing tunable multifunctional materials that is also collaborative. This research merges fiber-based digital fabrication and swarm-based logic to produce a system capable of digitally fabricating complex objects and large-scale architectural components through a novel multi-robotic fabrication paradigm. I hypothesize that this design approach-its theoretical foundations, methodological set up and related tools and technologies-will ultimately enable the design of large-scale structures with high spatial resolution in manufacturing that, like biological swarms, can tune their material make-up relative to their environment during the process of construction. Building on the insights derived from case study projects, fabricating with silkworms, ants, and bees, I demonstrate the design and deployment of a multi-robotic system erecting a 4.5-meter tall structure from fiber composites This thesis addresses the current limitations of digital fabrication, namely: (a) the material limitation, through automated digital fabrication of structural multi-functional materials; (b) the gantry limitation, through the construction of large components from a swarm of cooperative small scale robots; and (c) the method limitation, through digital construction methods that are not limited to layered manufacturing, but also support free-form printing (i.e. 3D-printing without support materials), CNC woven constructions and digitally aggregated constructions.
by Markus Kayser.
Ph. D.

La ricerca proposta ha l’obiettivo di indagare le potenzialità espressive e tettoniche generate attraverso la simulazione digitale delle dinamiche processuali ricorrenti nei sistemi complessi. Sono state, in particolare, sviluppate strategie di design computazionale al fine di integrare all’interno dell’algoritmo multi-agent specifiche qualità spaziali e di intrinseca efficienza costruttiva per favorire la progettazione a scala architettonica. Attraverso la ridistribuzione del controllo in moltitudini di unità autonome operative è possibile, infatti, orientare la progettazione dello spazio verso gradi di complessità crescente e di elevata risoluzione formale. Il sistema generato compie dinamicamente una riorganizzazione topologica sulla base delle relazioni locali e degli stimoli ambientali sviluppando comportamenti emergenti di organizzazione macro-superficiale. L’incorporamento di un modello spaziale locale all’interno di ogni agente, contribuisce alla generazione di morfologie particolarmente rigorose e fortemente risolute a fronte di condizionamenti extra-sistemici. In termini di progettazione, tale approccio permette di sviluppare strutture complesse, fortemente eterogenee e ridondanti, in grado di garantire continuità superficiale e strutturalità attraverso reticoli di micro-membrature reciprocamente connesse. L’intrinseca flessibilità morfologica esprime le proprie potenzialità nell’ambito architettonico individuato (complesso termale inserito in contesto naturale) grazie alla generazione di superfici continue nello spazio in grado di integrare molteplici istanze funzionali attraverso l’emergenza di comportamenti spaziali adattivi. La modulazione differenziata degli effetti espressivi superficiali deriva, infatti, dalla gestione di parametri processuali che influenzano la formazione dei pattern organizzativi e, coerentemente con essa, l’adozione di specifiche strategie in ambito costruttivo.

博士
大同大學
機械工程學系(所)
99
In this dissertation, two novel approaches to swarm intelligence-based methodology for optimal design of continuum structural topology and truss structure are presented. One is the ant colony algorithm mimicking the behavior of real ant colonies, and the other is the particle swarm optimization algorithm mimicking the social behavior of bird flocking. In terms of optimal design of structure topology, ant colony algorithm and binary particle swarm optimization algorithm were implemented for finding optimal solutions to multi-model structural problems. Four well-studies benchmark examples in continuum structural topology optimization problems were used to evaluate the proposed approach. The results indicate the effectiveness of the proposed algorithm. And, in terms of optimal design of truss structure, truss structure optimization considering topology, sizing, and shaping simultaneously. A two-stage ant algorithm, consisting of the ant colony algorithm and API(after "apicails" in Pachycondyla apicails) algorithm and a two-stage particle swarm optimization algorithm, consisting of the binary particle swarm optimization and the attractive and repulsive particle swarm optimization were proposed in this thesis for finding optimal truss structure. First, ant colony algorithm and binary particle swarm optimization were used to optimize the topology of truss, and then API algorithm and attractive and repulsive particle swarm optimization ware used to optimize the size and shape of truss. To confirm the effectiveness of the proposed method, several well-know truss optimization problem were used to evaluate the proposed approach. The results indicated that the proposed algorithm have better performance than those reported in the literature.

碩士
國立高雄海洋科技大學
輪機工程研究所
94
In this paper, a particle swarm optimization (PSO) based linear-quadratic (LQ) state-feedback regulator is investigated. The parameters of LQ regulators are determined by PSO method. A practical example of a rotating inverse pendulum is provided to demonstrate the effectiveness of the PSO-based LQ regulators. The performance of rotating inverse pendulum controlled by PSO-based LQ regulators is more ideal than the performance of rotating inverse pendulum controlled by Traditional LQ regulators. The goal of this study, stabilized the system performance with unstable operation point, can be achieved by using the proposed controller.

碩士
東海大學
資訊工程與科學系
94
Computer games have highly interactive ability and can integrate various media. Playing computer games have become people’s popular entertainment. Computer games have truly image and sound effect can give the player a rich game experence. But the technology of computer graphics alreay reach a bottleneck in the recent years. So many computer games developers have paid their attention to the AI of game characteristic. They hope the smart and various AI can make the computer game more interesting. The most computer game today use the rule-base design approach because of the simple and easy to implement. But, if the player find the weak point of the computer character, nothing can stop the player to win the game. If the computer character can learn from mistake, there may be a solution of this problem. Some scholar try to implement some learning algorithm to the computer game. But we found it needs large computation and collecting the train data sometimes are difficult. And we found the team work is easily to be found in today’s computer games. So we try to give a new approach to the team play computer game’s AI. For the application of computer game AI, the computation must be quick and stable. Particle Swarm Optimization(PSO) is a new optimization and machine learning technology in Artificial Intelligence. PSO is easy to emplement and there are few parameters to adjust. So we try to implement the PSO as the learning algorithm of computer game characteristic. But according to PSO, there is no coordination between each particle. So it can only create a powerful single character. So we propose a new learning strategy placing the emphasis on the team learning. In summery, this paper proposes a novel method based on PSO to help behavior design in computer games. Compare with the traditional PSO, proposed method can create more efficient team. And there is no need of large computation and training date, which suit the application of computer game. This new mechanism can help AI developer adjust the behavioral parameters which can save the testing time of different combination of parameter. In the experimental results, the proposed mechanism was embedded to design the team bots that indeed presents more changeable and the stable learning characteristic in the Quake III team play mode : Catch the flag.

碩士
國立高雄應用科技大學
電子工程系
97
This paper aims to design a digital filter via Particle Swarm Optimization (PSO). Emulating the collective behavior of creature, the algorithm avoids the local optimal problem and has high convergence speed to optimize the stopband attenuation of the digital filter from the searching domain. Both low pass filter and high pass filter are designed with PSO and Frequency Sampling Method (FSM). Simulation results show that the performance of the proposed method is better than that of Genetic algorithm (GA).

碩士
樹德科技大學
電腦與通訊研究所
95
Digital filter design is an integral part of the DSP field. Two types of filter structures are the finite impulse response (FIR) filter and the infinite impulse response (IIR) filter, respectively. For a given filtering characteristic, FIR filter may require many system terms to achieve the desired characteristic, whereas IIR filter generally needs fewer terms to achieve the same goal. Furthermore, the FIR filter is inherently guaranteed to be stable, but the stability for the IIR filter depends highly on the choices of filter parameters. The main contribution of this thesis is to apply the optimal search algorithm, particle swarm optimization (PSO), to the design of digital FIR filter. Three different kinds of filter designs are considered in the thesis. First, we apply the PSO algorithm to estimate the optimal coefficients of digital FIR filter. In this case, the order of FIR filter is assumed to be previously known. Second, a higher-order digital differentiator design is proposed via the same PSO algorithm. Four cases of linear phase FIR filters can be designed to match the prescribed differentiation frequency response of digital differentiator. We finally extend the filter design method from one dimension to two dimensions. According to the symmetry and/or anti-symmetry of its two-dimensional impulse responses in both directions and filter lengths, it can be divided into sixteen filter types. Each of them can be taken to design certain desired frequency response in two-dimensional cases by the proposed PSO algorithm.

碩士
國立宜蘭大學
電機工程學系碩士班
95
This paper presents the particle swarm optimization (PSO) based approach to design fuzzy control system is proposed for nano-positioning system. First, the Bouc-Wen model describes the non-linear hysteresis curve of a piezoelectric actuator. Then, the Takagi-Sugeno (T-S) fuzzy model is applied to approximate the non-linear nano-positioning system. Last, the parallel distributed compensation (PDC) is designed to control the piezoelectric actuator, and use linear matrix inequality (LMI) approach to check the stability of fuzzy controller. The parameters of the fuzzy control system are determined by the particle swarm optimization approach to find the best state feedback gain.

碩士
國立高雄海洋科技大學
輪機工程研究所
99
In this research, the modified particle swarm optimization method (PSO) is investigated. The traditional particle swarm optimization method was bringing the local minimum characteristic. Consequently, the genetic algorithm (GA) is cooperating with the particle swarm optimization method to avoid the local minimum characteristic. The basic elements, including distribution, selection and mutation will count into the particle swarm optimization method based on constrict factor. There are three benchmark problems and two control systems be used to verify the proposed method. Some computer simulations are provided to illustrate the advances, better than traditional GA and PSO, of our main ideas.

碩士
國立臺灣大學
電信工程學研究所
101
Quadrature Mirror Filter banks is an important topic of wavelet theory, image processes and data compression; Traditionally a linear-phase finite impulse response(LP-FIR) low-pass filter and high-pass filter are used to construct QMF, recently several reports suggest that implement QMF by using IIR digital allpass filter, this structure could solve some problem such as amplitude distortion, also, be able to achieve the same specifications as using FIR filter with less filter order. Under this structure, the design problem we face is a highly nonlinear optimization problem. In general, there are two traditional algorithms to solve this problem. The first one is called enumeration, it lists all the feasible solutions in the search space and test them, select the best solution from all the candidates. The advantage of enumeration is that it is guaranteed to find the global optimum; however, it costs too much search time and computation loading. The second is linearized algorithm, which linearized the nonlinear problem so that less computation time and loading are needed, at the same time, it is easier trapped in local optimum. Based on the above concept, in this paper we propose a type of evolution algorithm—Particles Swarm Optimization (PSO) algorithm to be optimizer, which is expected be balanced between enumeration and linearized algorithm, not to trapped in local optimum and find a better solution than the solution solved by linearized algorithm.

碩士
國立高雄應用科技大學
電子工程系
97
In this thesis, the weights of the artificial neural networks (ANN) are trained by modified adaptive particle swarm optimization (MAPSO). Back-propagation (BP) is an approximate steepest descent algorithm, but BP often finds the local optimal solution not the global optimal solution, because of the initial weights. The particle swarm optimization (PSO) method is one of the most powerful methods for letting the entire individuals move to the target, hence it can avoid to get the local optimal solution. However, the parameters, which greatly influence the algorithm stability and performance, are selected by depending on the designers’ experience. MAPSO is hereby presented with exponential decrease weights. Finally, the demonstrated examples are presented to illustrate the better performance of the proposed methodology (MAPSO-ANN).

碩士
國立高雄應用科技大學
電子工程系
97
This paper aims to design a communication channel equalizer based on Particle Swarm Optimization (PSO). Due to the features of swarm searching, PSO can use the few parameter hypotheses and processes and the rapid convergence. Communication systems mainly guarantee that the information can be delivered from transmitter to the receiver. However, due to the unideal channel effect via multi-path problem, applying an equalizer to recover the received signal should be considered. In this paper, we utilize PSO algorithm to construct the limited pulse wave response (Finite Impulse Response, FIR) equalizer and infinite pulse wave response (Infinite Impulse Response, IIR) one. Then, the analysis of the characteristics of the proposed equalizer verifies the effectiveness of the proposed method.

碩士
國立高雄海洋科技大學
輪機工程研究所
95
In this paper, a modified fuzzy gain scheduling PID controller via particle swarm optimization (PSO) method is investigated. Unlike traditional try and error method or other tuning methods, some important tunable variables of proposed controller are determined by PSO method, i.e., the boundary and slope of membership functions. The gain margin and oscillation frequency determined by traditional Ziegler-Nichols method are modified by proposed PSO method in the same time to improve the controlled system performance. Several ship handling examples are provided, namely, course keeping and course changing, to demonstrate the advantages of the proposed method.

碩士
國立臺北科技大學
電機工程系研究所
102
In this study, the direct torque control (DTC) is used as a main control structure of induction motor drive. Comparing to field-oriented control, this structure has no decoupling with fast system responses, simple structure and low computational complexity. This study is using speed estimator to achieve speed sensor-less control in order to prevent structure damaging of induction motor drive as well as to reduce cost. The particle swarm optimization PI controller (PSOPIC) is designed as speed controller by using proportional-integral controller with particle swarm optimization (PSO) algorithm. The speed controller can fine-tune the PI parameter online. Despite of system ability improvement and dynamic performance enhancement, the design resolves drawbacks of traditional fixed-parameter PI controller. In addition, dynamic performance of motor is affected by temperature effect of stator resistance. A PSO stator resistance estimator is able to fine-tune stator resistance in real-time to acquire precise flux linkage. To generate the reference of stator voltage vector, Fuzzy torque controller (FTC) and fuzzy flux controller (FFC) are designed to improve the flux response and reduce the torque ripple for better system dynamic performance. With PSOPIC speed controller, PSOSRE, FTC and FFC are integrated into DTC system of induction motor within the range of motor speed varying from 36 rpm to 1800 rpm with 8 Nm load, the simulation and experimental results concluded the proposed solution has excellent control capability and significant improvement in speed dynamic response.

碩士
國立雲林科技大學
電機工程系
105
In this thesis, utilize a trigonometric polynomial basis functions link artificial neural network (FLANN) structure particle swarm optimization (PSO) is used in the design of a channel equalizer to compensate signal distortion during transmission. PSO in the convergence process, will store the maximum value that they have experienced, call “local optimal”. All the maximum value of “local optimal” to share the message after the know “global optimal”, will also be recorded for the memory of the group, by sharing the memory of the message. That is, by “local best and “global best” comparison, to adjust the convergence direction and the next step of convergence speed. Iteration calculus search to find the best solution. From the experimental results show, we can know under the same conditions FLANN(PSO) has faster speed of convergence in network training stage than FLANN(LMS). For example, in the case of CH1,NL1, to converge in 10e-4 as a benchmark, FLANN (PSO) converges about 15,000, while FLANN (LMS) converges about 30000. In the nonlinear distortion and inter-interference (ISI) channel environment, the learning factor =0.006 and SNR at 14 dB BER to observe, at 10e-4 , FLANN(PSO) is approximately 12.7 dB, FLANN (LMS) is approximately 13 dB. It can be learned that PSO is superior to LMS in terms of performance.

碩士
國立中央大學
電機工程學系
106
This thesis proposes an improved particle swarm algorithm called Rank Switching Particle Swarm Optimization (RSLPSO), which uses a ranking mechanism and a switching mechanism to select the suitable update formula at each iteration time, effectively speed up the convergence of particles, and will reduce the amount of computation, and add a learning mechanism, so that communication between particles can be more frequent, effective experience exchanges, increase the diversity of particles, making particles not easily fall into the local optimum. In this dissertation, 16 test functions are used to test the performance of the proposed improved particle swarm algorithm. The test results show that the improved method proposed in this paper can perform well under most of the test functions. Finally, it is applied to the parameter search of the AC motor controller to find the best controller parameters needed by the motor at each sampling time in order to improve the motor's performance. From the motor's response graph, it can be analyzed the proposed particle swarm algorithm can effectively find the best controller parameters.

碩士
國立臺灣海洋大學
通訊與導航工程學系
104
This thesis focuses on the design of Chaotic Secure Communication Systems. Through the multi-objectives particle swarm algorithm, multi-objectives of the chaotic synchronization control system using the derived control gains can be achieved simultaneously. Multi-objective function will be the combination of chaos synchronization errors and control effort of the synchronization system. Then the chaos synchronization system and encryption system will be integrated to be a chaotic secure communication system. Finally, Matlab, circuit simulation software Multisim, and hardware implementation will be employed to verify the feasibility of and chaotic secure communication systems.

博士
長庚大學
電機工程學系
99
The aim of this dissertation is to address the decentralized formation problems for multiple robots by a fuzzy sliding-mode controller. Before starting the main subjects, a planetary type inverted pendulum and a ball-and-beam system are applied to investigate the closed-loop stability related to the choice of fuzzy parameters. The parameters of controllers can be further optimized by a fuzzy ant colony optimization algorithm. Afterwards, the adopted fuzzy sliding-mode controller is extended to a multi-robot system to achieve desired cooperative tasks. To perform a formation control and guarantee the system robustness, a novel formation algorithm combining the concepts of graph theory and fuzzy sliding-model control is presented. The parameters of the proposed controller are shown to be dependent on communication topology. In addition, based on Lyapunov stability theorem not only the system stability can be guaranteed, but all robots would be also toward their desired positions. Simulation results are provided to demonstrate the effectiveness of the provided control scheme. In addition, an experimental setup using e-puck robots is built up for multi-robot formations. Compared to conventional formation algorithms, it shows that real-time experiment results empirically support the promising performance of desire.

碩士
國立高雄應用科技大學
資訊工程系
99
In the communication systems, applying an equalizer to recover the received signal should be considered. Inter-symbol interference(ISI) is an important factor which affects the performance of communication systems. An equalizer can effectively eliminate ISI so that the originally transmitted symbols can be recoverd correctly at the receiver. In this thesis, we propose Particle Swarm Optimization(PSO) and Particle Swarm Optimization with Time-Varying Acceleration Coefficient(PSO-TVAC). PSO can use the few parameter hypotheses and rapid convergence. Inertia weight of PSO-TVAC gives balance between global and local searching, and benefits the convergence accuracy. We uses algorithm to update and obtain the optimized equalizer weights. The analysis of the characteristics of the proposed equalizer verifies the effectiveness of the proposed method.

This thesis is concerned with the development of a novel discrete particle swarm optimization (PSO) technique and its application to the discrete optimization of digital filter frequency response characteristics on the one hand, and the high-level synthesis of bit-parallel digital filter data-paths on the other. Two different techniques are presented for the optimization of sharp-transition band frequency response masking (FRM) digital filters, one of which is based on the conventional finite impulse-response (FIR) digital subfilters, and a new hardware-efficient approach which is based on utilizing infinite impulse-response (IIR) digital subfilters. It is shown that further hardware efficiency can be achieved by realizing the IIR interpolation subfilters by using the bilinear-LDI approach. The corresponding discrete PSO is carried out over the canonical signed digit (CSD) multiplier coefficient space for direct mapping to digital hardware considering simultaneous magnitude and group-delay frequency response characteristics. A powerful encoding scheme is developed for the high-level synthesis of digital filters based on discrete PSO, which preserves the data dependency relationships in the digital filter data-paths. In addition, a constrained discrete PSO is developed to overcome the limitations which would manifest themselves if the conventional PSO were to be used. Several examples are presented to demonstrate the application of discrete PSO to the design, high-level synthesis and optimization of digital filters.
Communications

碩士
朝陽科技大學
資訊工程系碩士班
94
In this thesis, we propose two new learning algorithms to design the TSK-type neuro-fuzzy networks. Though, there has been a great deal of interest in the use of the immune systems and algorithms as inspiration for computer science and engineering, in the fundamental methodologies it is not dramatic. In order to enhance the IA performance, we propose the efficient immune-based particle swarm optimization (IPSO) and the immune-based symbiotic particle swarm optimization (ISPSO) with TSK-type neuro-fuzzy networks for solving the identification, prediction and face detection problems. The proposed IPSO is combining the IA and PSO to perform parameter learning. In order to avoid trapping in a local optimal solution and to ensure the searching capability of near global optimal solution, mutation plays an important role in IPSO. Therefore, we employed the merits of PSO to improve mutation mechanism. The PSO algorithm has proved to be very effective for solving global optimization. It is not only a recently invented high-performance optimizer that is very easy to understand and implement, but also requires less computational bookkeeping and generally only a few lines of code. However, the parameters (ω, r1 and r2) of PSO are the key factors to affect the convergence. In fact, parameters of PSO cannot ensure the optimization’s ergodicity entirely in phase space because they are absolutely random in the traditional PSO. Moreover, we propose another measure which introducing chaotic mapping with certainty, ergodicity and the stochastic property into PSO so as to improve the global convergence. In addition to modify the PSO, we improve the IA structure. Unlike the IA that uses each individual (antibodies) in a population as a full solution to a problem, symbiotic evolution assumes that each individual in a population represents only a partial solution to a problem; complex solutions combine several individuals in the population.

博士
國立中山大學
機械與機電工程學系研究所
106
The main goal of this study is to find the optimal control parameters based on the stochastic inertia weight particle swarm optimization (SIWPSO) and radial basis function neural network (RBFNN) algorithm. First, a graphical approach is used to determine the stability region and set the parameters of the proportional integral derivative (PID) controller to achieve an arbitrary-order time delay system. Then, the SIWPSO algorithm is used to find the optimal control parameters from the stability region. To obtain the fitness curve for the optimal control parameters from the results of the SIWPSO algorithm, the RBFNN algorithm is applied to optimize the operating curve of the PID control parameters. This study presents two cases of integer order PID (IOPID) control systems for horizontal-axis wind turbines. The first case is the power control problem in the drive train control of the wind turbine system. The second case is the fore-aft modal deflection control problem of the pitch angle controller for the wind turbine system. Then, this study presents two cases of fractional order PID (FOPID) control systems with time delay. The first case is the attitude control of a bias-momentum satellite. The second case is a fractional order control system with time delay. To emphasize that the SIWPSO-RBFNN method can be implemented in real systems, a digital signal processor (DSP) system is used to verify the reliability of the proposed method. The transfer function model, which is obtained from a brushless direct current (BLDC) motor, is used in the integer order proportional-integral (IOPI) controller design with time delay. The results of the simulations and experiments indicate that the proposed method, which finds the optimal IOPI control gains, has good time responses in different time delay conditions.

碩士
國立臺灣海洋大學
系統工程暨造船學系
99
In this research, a numerical population-based optimization algorithm is developed for optimization problems, the algorithm is established based on the particle swarm optimization incorporated with the grid method. To verify the proposed algorithm, we selected 11 benchmark functions, with variables interacted or non- interacted. Meantime, there are three different number of dimensions for each tested functions are considered, they are 30, 50 and 100 respectively. The results obtained by the proposed method are shown much improved than those from the particle swarm optimization. Further, two engineering optimization design problems, a complicated gear problem and a plane-truss structure problem are solved by the proposed method. The results determined demonstrated the proposed method provide the solutions the same as the best ones obtained by other different approaches. Finally, an optimization design of springs of a slider cell-phone is designed with 8 variables considered. The proposed method is applied on the mechanism to determine the combination of the eight variables to reduce the stress concentration as much as possible.

博士
國立臺北科技大學
機電科技研究所
98
In this thesis, a novel photovoltaic (PV) maximum power point tracking (MPPT) based on biological swarm chasing behavior is proposed to increase the MPPT performance for a module-integrated PV power system. Each PV module is viewed as a particle, and as a result, the maximum power point is viewed as the moving target. Thus, every PV module can chase the maximum power point (MPP) automatically. A 525W prototype constructed by three paralled-connected 175W PV modules is implemented to assess the MPPT performance. Comparing with a typical perturb and observe (P&O) MPPT method, the MPPT efficiency is improved about 12.19% in transient state by the proposed MPPT as theoretical prediction.

碩士
淡江大學
電機工程學系碩士班
99
This paper presents a hardware/software co-design method for implenting a hybrid object tracking system based on particle filter and Particle Swarm Optimization via System on Program Chip (SOPC) technique. Practice on the system using the proposed switching method When the particle filter lost the tracking because object moving too fast,it will switch to PSO to do a global search. When the PSO to tracking the object, it will switch to the particle filter to do fast tracking. Considering both the execution speed and design flexibility, we use a NIOS II processor to calculate weight for each particle and a hardware accelerator to update particles. As a result, execution efficiency of the proposed hardware/software co-design method of particle filter and Particle Swarm Optimization is significantly improved while maintaining design flexibility for various applications. To demonstrate the performance of the proposed approach, a real-time object tracking system is established and presented in this paper. Experimental results have demonstrated the proposed method have satisfactory results in real-time tracking of objects in video sequences.

碩士
國立臺灣師範大學
應用電子科技學系
102
This thesis presents a hybrid algorithm incorporating Particle Swarm Optimization (PSO) and Particle Filter (PF) for multiple-object tracking to improve the system performance. Based on the System on a Programmable Chip (SOPC) technique, we use hardware/software (HW/SW) co-design method to implement the hybrid algorithm on the FPGA circuit. As a result, the tracking efficiency can be greatly improved, while maintaining design flexibility for various applications. To further improve the performance of the multiple-object tracking system, full hardware implementation of the tracking system can be realized once the prototype testing of the system is completed.

碩士
國立彰化師範大學
工業教育與技術學系
102
The purpose of this study focuses on the optimal design of the shortest path programming and multi-point minimum position error of five-axis robot. The first stage, multi-group ant colony algorithm was applied to calculate the shortest path with many different path points, and program the priority moving order of it. The second stage, the minimum position error from point to point was calculated by using hybrid quantum particle swarm algorithm according to. In this study, the points of the fifteen random space positions were constructed first to be considered as the target of path programming. And used the operation which motor spins when five-axis robot moved to separate point according to the design parameters of the position error for further forecasting the optimal combination of parameters Multi-group ant colony algorithm was used in this study to forecast the shortest path programming. To avoid trapping into the local solution in the process of calculation, a hybrid quantum particle swarm algorithm with mutation operator was performed to determine the feasible solution of minimum position error. The results of simulation analysis showed that the path programming and position error can be improved. The shortest path programming can be reduced by twenty-four percentage to twenty-nine percentage; and the error of minimum position can be reduced by five to seven-bits after the decimal point.

In recent years, use of composites is increasing in most fields of engineering such as aerospace, automotive, civil construction, marine, prosthetics, etc., because of its light weight, very high specific strength and stiffness, corrosion resistance, high thermal resistance etc. It can be seen that the specific strength of fibers are many orders more compared to metals. Thus, laminated fiber reinforced plastics have emerged to be attractive materials for many engineering applications. Though the uses of composites are enormous, there is always an element of fuzziness in the design of composites. Composite structures are required to be designed to resist high stresses. For this, one requires a reliable failure criterion. The anisotropic behaviour of composites makes it very difficult to formulate failure criteria and experimentally verify it, which require one to perform necessary bi-axial tests and plot the failure envelopes. Failure criteria are usually based on certain assumption, which are some times questionable. This is because, the failure process in composites is quite complex. The failure in a composite is normally based on initiating failure mechanisms such as fiber breaks, fiber compressive failure, matrix cracks, matrix crushing, delamination, disbonds or a combination of these. The initiating failure mechanism is the one, which is/are responsible for initiating failure in a laminated composites. Initiating failure mechanisms are generally dependant on the type of loading, geometry, material properties, condition of manufacture, boundary conditions, weather conditions etc. Since, composite materials exhibit directional properties, their applications and failure conditions should be properly examined and in addition to this, robust computational tools have to be exploited for the design of structural components for efficient utilisation of these materials. Design of structural components requires reliable failure criteria for the safe design of the components. Several failure criteria are available for the design of composite laminates. None of the available anisotropic strength criteria represents observed results sufficiently accurate to be employed confidently by itself in design. Most of the failure criteria are validated based on the available uniaxial test data, whereas, in practical situations, laminates are subjected to at least biaxial states of stresses. Since, the generation of biaxial test data are very difficult and time consuming to obtain, it is indeed a necessity to develop computational tools for modelling the biaxial behavior of the composite laminates. Understanding of the initiating failure mechanisms and the development of reliable failure criteria is an essential prerequisite for effective utilization of composite materials. Most of the failure criteria, considers the uniaxial test data with constant shear stress to develop failure envelopes, but in reality, structures are subjected to biaxial normal stresses as well as shear stresses. Hence, one can develop different failure envelopes depending upon the percentage of the shear stress content. As mentioned earlier, safe design of the composite structural components require reliable failure criterion. Currently two broad approaches, namely, (1) Damage Tolerance Based Design and (2)Failure Criteria Based Design are in use for the design of laminated structures in aerospace industry. Both approaches have some limitations. The damage tolerance based design suffers from a lack of proper definition of damage and the inability of analytical tools to handle realistic damage. The failure criteria based design, although relatively, more attractive in view of the simplicity, it forces the designer to use unverified design points in stress space, resulting in unpredictable failure conditions. Generally, failure envelopes are constructed using 4 or 5 experimental constants. In this type of approach, small experimental errors in these constants lead to large shift in the failure boundaries raising doubts about the reliability of the boundary in some segments. Further, they contain segments which have no experimental support and so can lead to either conservative or nonconservative designs. Conservative design leads to extra weight, a situation not acceptable in aerospace industry. Whereas, a nonconservative design, is obviously prohibitive, as it implies failure. Hence, both the damage tolerance based design and failure criteria based design have limitations. A new method, which combines the advantages of both the approaches is desirable. This issue is also thoroughly debated in many international conference on composites. Several pioneers in the composite industry indicated the need for further research work in the development of reliable failure criteria. Hence, this is motivated to carry out research work for the development of new failure criterion for the design of composite structures. Several experts meetings held world wide towards the assessment of existing failure theories and computer codes for the design of composite structures. One such meeting is the experts meeting held at United Kingdom in 1991.This meeting held at St. Albans(UK) on ’Failure of Polymeric Composites and Structures: Mechanisms and Criteria for the Prediction of Performance’, in 1991 by UK Science & Engineering Council and UK Institute of Mechanical Engineers. After thorough deliberations it was concluded that 1. There is no universal definition of failure of composites. 2. There is little or lack of faith in the failure criteria that are in current use and 3. There is a need to carry out World Wide Failure Exercise(WWFE) Based on the experts suggestions, Hinton and Soden initiated the WWFE in consultation with Prof.Bryan Harris (Editor, Journal of Composite Science and Tech-nology)to have a program to get comparative assessment of existing failure criteria and codes with following aims 1. Establish the current level of maturity of theories for predicting the failure response of fiber reinforced plastic(FRP)laminates. 2. Closing the knowledge gap between theoreticians and design practitioners in this field. 3. Stimulating the composites’ community into providing design engineers with more robust and accurate failure prediction methods, and the confidence to use them. The organisers invited pioneers in the composite industry for the program of WWFE. Among the pioneer in the composite industry Professor Hashin declined to participate in the program and had written a letter to the organisers saying that, My only work in this subject relates to failure criteria of unidirectional fiber composites, not to laminates. I do not believe that even the most complete information about failure of single plies is sufficient to predict the failure of a laminate, consisting of such plies. A laminate is a structure which undergoes a complex damage process (mostly of cracking) until it finally fails. The analysis of such a process is a prerequisite for failure analysis. ”While significant advances have been made in this direction we have not yet arrived at the practical goal of failure prediction”. Another important conference held in France in 1999, Composites for the next Millennium (Proceedingof Symposium in honor of S.W.Tsaion his 70th Birth Day Torus, France, July 2-3, 1999, pp.19.) also concludedon similar line to the meeting held at UK in 1991. Paul A Lagace and S. Mark Spearing, have pointed out that, by referring to the article on ’Predicting Failure in Composite Laminates: the background to the exercise’, by M.J.Hinton & P.D.Soden, Composites Science and Technology, Vol.58, No.7(1998), pp.1005. ”After Over thirty years of work ’The’ composite failure criterion is still an elusive entity”. Numerous researchers have produced dozens of approaches. Hundreds of papers, manuscripts and reports were written and presentations made to address the latest thoughts, add data to accumulated knowledge bases and continue the scholarly debate. Thus, the out come of these experts meeting, is that, there is a need to develop new failure theories and due to complexities associated with experimentation, especially getting bi-axial data, computational methods are the only viable alternative. Currently, biaxial data on composites is very limited as the biaxial testing of laminates is very difficult and standardization of biaxial data is yet to be done. All these experts comments and suggestions motivated us to carry out research work towards the development of new failure criterion called ’Failure Mechanism Based Failure Criterion’ based on initiating failure mechanisms. The objectives of the thesis are 1. Identification of the failure mechanism based failure criteria for the specific initiating failure mechanism and to assign the specific failure criteria for specific initiating failure mechanism, 2. Use of the ’failure mechanism based design’ method for composite pressurant tanks and to evaluate it, by comparing it with some of the standard ’failure criteria’ based designs from the point of view of overall weight of the pressurant tank, 3. Development of new failure criterion called ’Failure Mechanism Based Failure Criterion’ without shear stress content and the corresponding failure envelope, 4. Development of different failure envelopes with the effect of shear stress depending upon the percentage of shear stress content and 5. Design of composite laminates with the Failure Mechanism Based Failure Criterion using optimization techniques such as Genetic Algorithms(GA) and Vector Evaluated Particle Swarm Optimization(VEPSO) and the comparison of design with other failure criteria such as Tsai-Wu and Maximum Stress failure criteria. The following paragraphs describe about the achievement of these objectives. In chapter 2, a rectangular panel subjected to boundary displacements is used as an example to illustrate the concept of failure mechanism based design. Composite Laminates are generally designed using a failure criteria, based on a set of standard experimental strength values. Failure of composite laminates involves different failure mechanisms depending upon the stress state and so different failure mechanisms become dominant at different points on the failure envelope. Use of a single failure criteria, as is normally done in designing laminates, is unlikely to be satisfactory for all combination of stresses. As an alternate use of a simple failure criteria to identify the dominant failure mechanism and the design of the laminate using appropriate failure mechanism based criteria is suggested in this thesis. A complete 3-D stress analysis has been carried out using a general purpose NISA Finite Element Software. Comparison of results using standard failure criteria such as Maximum Stress, Maximum Strain, Tsai-Wu, Yamada-Sun, Maximum Fiber Strain, Grumman, O’brien, & Lagace, indicate substantial differences in predicting the first ply failure. Results for Failure Load Factors, based on the failure mechanism based approach are included. Identification of the failure mechanism at highly stressed regions and the design of the component, to withstand an artificial defect, representative this failure mechanism, provides a realistic approach to achieve necessary strength without adding unnecessary weight to the structure. It is indicated that the failure mechanism based design approach offers a reliable way of assessing critically stressed regions to eliminate the uncertainties associated with the failure criteria. In chapter 3, the failure mechanism based design approach has been applied to a composite pressurant tanks of upper stages of launch vehicles and propulsion systems of space crafts. The problem is studied using the failure mechanism based design approach, by introducing an artificial matrix crack representative of the initiating failure mechanism in the highly stressed regions and the strain energy release rate (SERR) are calculated. The total SERR value is obtained as 3330.23 J/m2, which is very high compared to the Gc(135 J/m2) value, which means the crack will grow further. The failure load fraction at which the crack has a tendency to grow is estimated to be 0.04054.Results indicates that there are significant differences in the failure load fraction for different failure criteria.Comparison with Failure Mechanism Based Criterion (FMBC) clearly indicates matrix cracks occur at loads much below the design load yet fibers are able to carrythe design load. In chapter 4, a Failure Mechanism Based Failure Criterion(FMBFC)has been proposed for the development of failure envelope for unidirectional composite plies. A representative volume element of the laminate under local loading is micromechanically modelled to predict the experimentally determined strengths and this model is then used to predict points on the failure envelope in the neighborhood of the experimental points. The NISA finite element software has been used to determine the stresses in the representative volume element. From these micro-stresses, the strength of the lamina is predicted. A correction factor is used to match the prediction of the present model with the experimentally determined strength so that, the model can be expected to provide accurate prediction of the strength in the neighborhood of the experimental points. A procedure for the construction of the failure envelope in the stress space has been outlined and the results are compared with the some of the standard and widely used failure criteria in the composite industry. Comparison of results with the Tsai-Wu failure criterion shows that there are significant differences, particularly in the third quadrant, when the ply is under bi-axial compressive loading. Comparison with maximum stress criterion indicates better correlation. The present failure mechanism based failure criterion approach opens a new possibility of constructing reliable failure envelopes for bi-axial loading applications, using the standard uniaxialtest data. In chapter 5, the new failure criterion for laminated composites developed based on initiating failure mechanism as mentioned in chapter 4 for without shear stress condition is extended to obtain the failure envelopes with the shear stress condition. The approach is based on Micromechanical analysis of composites, wherein a representative volume consists of a fiber surrounded by matrix in appropriate volume fraction and modeled using 3-D finite elements to predict the strengths.In this chapter, different failure envelopes are developed by varying shear stress say from 0% of shear strength to 100% of shear strength in steps of 25% of shear strength. Results obtained from this approach are compared with Tsai-Wu and Maximum stress failure criteria. The results show that the predicted strengths match more closely with maximum stress criterion. Hence, it can be concluded that influence of shear stress on the failure of the lamina is of little consequence as far as the prediction of strengths in laminates. In chapter 6, the failure mechanism based failure criterion, developed by the authors is used for the design optimization of the laminates and the percentage savings in total weight of the laminate is presented. The design optimization of composite laminates are performed using Genetic Algorithms. The genetic algorithm is one of the robust tools available for the optimum design of composite laminates. The genetic algorithms employ techniques originated from biology and dependon the application of Darwin’s principle of survival of the fittest. When a population of biological creatures is permitted to evolve over generations, individual characteristics that are beneficial for survival have a tendency to be passed on to future generations, since individuals carrying them get more chances to breed. In biological populations, these characteristics are stored in chromosomal strings. The mechanics of natural genetics is derived from operations that result in arranged yet randomized exchange of genetic information between the chromosomal strings of the reproducing parents and consists of reproduction, cross over, mutation, and inversion of the chromosomal strings. Here, optimization of the weight of the composite laminates for given loading and material properties is considered. The genetic algorithms have the capability of selecting choice of orientation, thickness of single ply, number of plies and stacking sequence of the layers. In this chapter, minimum weight design of composite laminates is presented using the Failure Mechanism Based(FMB), Maximum Stress and Tsai-Wu failure criteria. The objective is to demonstrate the effectiveness of the newly proposed FMB Failure Criterion(FMBFC) in composite design. The FMBFC considers different failure mechanisms such as fiber breaks, matrix cracks, fiber compressive failure, and matrix crushing which are relevant for different loadin gconditions. FMB and Maximum Stress failure criteria predicts byupto 43 percent savings in weight of the laminates compared to Tsai-Wu failure criterion in some quadrants of the failure envelope. The Tsai-Wu failure criterion over predicts the weight of the laminate by up to 86 percent in the third quadrant of the failure envelope compared to FMB and Maximum Stress failure criteria, when the laminate is subjected to biaxial compressive loading. It is found that the FMB and Maximum Stress failure criteria give comparable weight estimates. The FMBFC can be considered for use in the strength design of composite structures. In chapter 7, Particle swarm optimization is used for design optimization of composite laminates. Particle swarm optimization(PSO)is a novel meta-heuristic inspired by the flocking behaviour of birds. The application of PSO to composite design optimization problems has not yet been extensively explored. Composite laminate optimization typically consists in determining the number of layers, stacking sequence and thickness of ply that gives the desired properties. This chapter details the use of Vector Evaluated Particle Swarm Optimization(VEPSO) algorithm, a multi-objective variant of PSO for composite laminate design optimization. VEPSO is a modern coevolutionary algorithm which employs multiple swarms to handle the multiple objectives and the information migration between these swarms ensures that a global optimum solution is reached. The current problem has been formulated as a classical multi-objective optimization problem, with objectives of minimizing weight of the component for a required strength and minimizing the totalcost incurred, such that the component does not fail. In this chapter, an optimum configuration for a multi-layered unidirectional carbon/epoxy laminate is determined using VEPSO. The results are presented for various loading configurations of the composite structures. The VEPSO predicts the same minimum weight optimization and percentage savings in weight of the laminate when compared to GA for all loading conditions.There is small difference in results predicted by VEPSO and GA for some loading and stacking sequence configurations, which is mainly due to random selection of swarm particles and generation of populations re-spectively.The difference can be prevented by running the same programme repeatedly. The Thesis is concluded by highlighting the future scope of several potential applications based on the developments reported in the thesis.

碩士
國立彰化師範大學
工業教育與技術學系
103
The goal of this research is chiefly for analysis and design of air-core linear servo motor with trapezoidal permanent magnets by combining grey-based Taguchi methods and a culture-based quantum behavior particle swarm optimization algorithm (CBQPSO). In the research process, the algorithm that combines the mutation operator, elitist strategy, non-dominated sorting and crowding distance can obtain the Pareto optimal front to determine and preserve the diversity of solutions. This project will be divided into two steps to perform. The first step considers the design parameters including the dimension of the topline, baseline and height of trapezoidal permanent magnetic poles in stator, the air-gap, the width of coil wound, the height of coil wound and the coil diameter of mover. The significant design parameters of four objective functions including the maximum thrust density, minimum temperature, minimum volume and minimum current of motor are determined by grey-based Taguchi methods. They are the baseline of trapezoidal permanent magnets, the width of coil wound, the height of coil wound and the diameters of coils. The second step is that the analytical models of four objective functions with significant design parameters are constructed by Response Surface Methodology (RSM). The culture-based quantum behavior particle swarm optimization algorithm is used to determined the Pareto optimal solutions of four objective functions. The research results show that the thrust density is increased by 10.02%, the temperature is reduced by 9.86%, the current per unit area of coil wound is roughly reduced by 23.22%, and the volume of motor body is reduced by 4.48%, compared with the non-optimization motor. Also, this project performs the experimental verification of the simulation according to the significant design parameters from the first step. The verification results show that the proposed approach in this project effectively improves the performance of Trapezoid Ironless Permanent Magnet Linear Motors.

碩士
國立臺灣大學
應用數學科學研究所
105
Since the experimental costs are increasing, optimal experimental design has been widely used nowadays for a more efficient process. Using some algorithms to generate optimal designs for different specific models is more effective than finding analytical results for complicated models. Therefore, we use particle swarm optimization (PSO) to generate the best result for model selected by users. Here we built up a web-based interactive platform for practitioners, which contains different types of optimal designs for different experimental problems, and the users don''t need any programming skills but can operate easily. We will demonstrate the usage of PSO in solving different types of optimal designs through the website.

碩士
國立高雄應用科技大學
電子工程系碩士班
104
Nowadays, along with the dramatically development of computer sciences, artificial optimization algorithms have been playing a very important role in automatic control engineering as well as in the industrial fields. In fact, one of the most popular methods in designing controllers is classical approaches. However, using these conventional methods not only take much time for tuning controller parameters, but also become a difficult task for highly nonlinear systems. In order to overcome these drawbacks, some artificial optimization algorithms have been developing and being very promising solutions for nonlinear problems. This thesis proposes some hybrid controllers based on swarm intelligence optimization algorithms, including fireworks algorithm (FWA), modified particle swarm optimization (MPSO) and genetic algorithm (GA). Four hybrid control configurations are formed from controllers, namely fractional-order PID, linear quadratic regulator, fast output sampling sliding mode controller and baseline sliding mode controller. The controller parameters are optimized by above swarm intelligence algorithms for improving system performance. The performance of the proposed controllers is verified on a nonlinear inverted pendulum-cart system, which is known as one of the most classical and difficult problem in the field of the control engineering. The simulation process is carried out using MATLAB/Simulink. The results are compared with two published methods. The comparison results show a better performance of the proposed controllers.