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Relevant bibliographies by topics / Model based adaptive controller

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Academic literature on the topic 'Model based adaptive controller'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Model based adaptive controller"

1

Hassan, Aliaa Adnan Flaih, Ekhlas Hameed Karam, and Muaayed F. Al-Rawi. "Model based adaptive controller with grasshopper optimization algorithm for upper-limb rehabilitation robot." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 723–31. https://doi.org/10.11591/ijeecs.v26.i2.pp723-731.

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Model based adaptive controllers (MBACs) are considered one of the most common adaptive controllers that are used with robotic systems due to their ensuring nonlinear robust scheme with global asymptotic stability for controlling nonlinear systems. However, this controller requires precise mathematical models of the controlled systems. In this paper, an optimal model-based adaptive controller (OMBAC) is suggested for controlling a two-link upper limb rehabilitation robot. This controller, in the presence of model uncertainties, can guarantee the robustness of the rehabilitation robot. Although the OMBAC is an adaptive and model-based controller, some of its parameters need to be determined precisely. In this paper, these parameters are determined by the grasshopper optimization algorithm (GOA). The Lyapunov method is used to analyze the stability assurance of controlled rehabilitation. The results of the simulation for two tested trajectories (linear and nonlinear trajectories) demonstrate the efficiency of the suggested OMBAC with fast settling time, minimum error steady state, and very small overshoot.

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Adnan, Aliaa, Ekhlas H. Karam, and Muaayed F. Al-Rawi. "Model based adaptive controller with grasshopper optimization algorithm for upper-limb rehabilitation robot." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 723. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp723-731.

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<span>Model based adaptive controllers (MBACs) are considered one of the most common adaptive controllers that are used with robotic systems due to their ensuring nonlinear robust scheme with global asymptotic stability for controlling nonlinear systems. However, this controller requires precise mathematical models of the controlled systems. In this paper, an optimal model-based adaptive controller (OMBAC) is suggested for controlling a two-link upper limb rehabilitation robot. This controller, in the presence of model uncertainties, can guarantee the robustness of the rehabilitation robot. Although the OMBAC is an adaptive and model-based controller, some of its parameters need to be determined precisely. In this paper, these parameters are determined by the grasshopper optimization algorithm (GOA). The Lyapunov method is used to analyze the stability assurance of controlled rehabilitation. The results of the simulation for two tested trajectories (linear and nonlinear trajectories) demonstrate the efficiency of the suggested OMBAC with fast settling time, minimum error steady state, and very small overshoot.</span>

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Yang, Xinhao, and Ze Li. "Congestion Control Based on Multiple Model Adaptive Control." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/714320.

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The congestion controller based on the multiple model adaptive control is designed for the network congestion in TCP/AQM network. As the conventional congestion control is sensitive to the variable network condition, the adaptive control method is adopted in our congestion control. The multiple model adaptive control is introduced in this paper based on the weight calculation instead of the parameter estimation in past adaptive control. The model set is composed by the dynamic model based on the fluid flow. And three “local” congestion controllers are nonlinear output feedback controller based on variable RTT, H2output feedback controller, and proportional-integral controller, respectively. Ns-2 simulation results in section 4 indicate that the proposed algorithm restrains the congestion in variable network condition and maintains a high throughput together with a low packet drop ratio.

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Zuo, Yuefei, Shushu Zhu, Yebing Cui, Chuang Liu, and Xiaogang Lin. "Adaptive PI Controller for Speed Control of Electric Drives Based on Model Reference Adaptive Identification." Electronics 13, no. 6 (2024): 1067. http://dx.doi.org/10.3390/electronics13061067.

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In this paper, to achieve auto-setting of PI controller gains when mechanical parameters are unknown, two adaptive PI controllers for speed control of electric drives are developed based on model reference adaptive identification. The adaptive linear neuron (ADALINE) neural network is used to interpret the proposed adaptive PI controller. The effect of the low-pass filter used for the feedback speed and the Coulomb friction torque on parameter identification is analysed, and a new motion equation using filtered speed is given. Additionally, a parameter identification method based on unipolar speed reference is provided. The two proposed adaptive PI controllers and the conventional PI controller are compared based on the high-precision digital simulation using MATLAB/Simulink (version R2023a). The simulation results show that both of the two proposed adaptive PI controllers are able to identify mechanical parameters, but the adaptive PI-1 controller outperforms the adaptive PI-2 controller due to its better noise attenuation performance.

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LEE, T. H., J. X. XU, and M. WANG. "A model-based adaptive sliding controller." International Journal of Systems Science 27, no. 1 (1996): 129–40. http://dx.doi.org/10.1080/00207729608929195.

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Mirwald, Jonas, Johannes Ultsch, Ricardo de Castro, and Jonathan Brembeck. "Learning-Based Cooperative Adaptive Cruise Control." Actuators 10, no. 11 (2021): 286. http://dx.doi.org/10.3390/act10110286.

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Traffic congestion and the occurrence of traffic accidents are problems that can be mitigated by applying cooperative adaptive cruise control (CACC). In this work, we used deep reinforcement learning for CACC and assessed its potential to outperform model-based methods. The trade-off between distance-error minimization and energy consumption minimization whilst still ensuring operational safety was investigated. Alongside a string stability condition, robustness against burst errors in communication also was incorporated, and the effect of preview information was assessed. The controllers were trained using the proximal policy optimization algorithm. A validation by comparison with a model-based controller was performed. The performance of the trained controllers was verified with respect to the mean energy consumption and the root mean squared distance error. In our evaluation scenarios, the learning-based controllers reduced energy consumption in comparison to the model-based controller by 17.9% on average.

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Li, Xian-Sheng, Yuan-Yuan Ren, and Xue-Lian Zheng. "Model-Free Adaptive Control for Tank Truck Rollover Stabilization." Mathematical Problems in Engineering 2021 (August 20, 2021): 1–16. http://dx.doi.org/10.1155/2021/8417071.

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Influenced by lateral liquid sloshing in partially filled tanks, tank vehicles are apt to encounter with rollover accidents. Due to its strong nonlinearity and loading state uncertainty, it has great challenges in tank vehicle active control. Based on the model-free adaptive control (MFAC) theory, the roll stability control problem of tank trucks with different tank shapes and liquid fill percentages is explored. First, tank trucks equipped with cylinder or elliptical cylinder tanks are modelled, and vehicle dynamics is analyzed. This dynamic model is used to provide I/O data in the controlled system. Next, the control objective of tank vehicle rollover stabilization is analyzed and the controlled variable is selected. Subsequently, differential braking and active front steering controller are designed by MFAC algorithm. Finally, the effectiveness of the designed controllers is verified by simulation, and difference between the controllers is analyzed. The controller designed by MFAC algorithm is proven to be adaptive to vehicle loading and driving states. The controlled system has great robustness.

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Chou, Sidney. "Controller Tuning Based on Stochastic Control Theory." Journal of Dynamic Systems, Measurement, and Control 110, no. 1 (1988): 100–104. http://dx.doi.org/10.1115/1.3152638.

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A practical controller tuning method is proposed for selecting controller gains in the face of design difficulties such as poor repeatability, long delay, nonlinearity, conflicting control objectives, model inaccuracy, and system complexity. Unlike many adaptive schemes striving to acquire knowledge about the system being controlled, the proposed approach is aimed at designing nonadaptive, or at best, gain scheduling controllers in a quantitative, systematic way while meeting design specifications.

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Humaidi, Amjad, and Mustafa Hameed. "Development of a New Adaptive Backstepping Control Design for a Non-Strict and Under-Actuated System Based on a PSO Tuner." Information 10, no. 2 (2019): 38. http://dx.doi.org/10.3390/info10020038.

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In this work, a new adaptive block-backstepping control design algorithm was developed for an under-actuated model (represented by a ball–arc system) to enhance the transient and steady-state behaviors and to improve the robustness characteristics of the controlled system against parameter variation (load change and model uncertainty). For this system, the main mission of the proposed controller is to simultaneously hold the ball at the top of the arc and retain the cart at the required position. The stability of a controlled system based on the proposed adaptive controller was analyzed, and its globally asymptotic stability was proven based on the Lyapunov theorem. A comparative study of adaptive and non-adaptive block-backstepping controllers was conducted in relation to the transient, steady-state, and robustness characteristics. The effectiveness of the controller was verified via simulation within a MATLAB/SIMULINK environment. The simulated results show that the proposed adaptive control strategy could successfully stabilize the under-actuated ball–arc system, regardless of both the regulation problem and the tracking problem. This provides a better dynamic performance and a better load rejection capability, and it performs well in solving the uncertainty problem in the model parameter.

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Kasparian, Vicken, and Celal Batur. "Model reference based neural network adaptive controller." ISA Transactions 37, no. 1 (1998): 21–39. http://dx.doi.org/10.1016/s0019-0578(98)00002-0.

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Dissertations / Theses on the topic "Model based adaptive controller"

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Wu, Yue. "The design and application of a new type of adaptive fuzzy model-based controller." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432574.

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Couch, Jeremy Robert. "An ECMS-Based Controller for the Electrical System of a Passenger Vehicle." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366076350.

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Shamsudin, Syariful Syafiq. "The Development of Neural Network Based System Identification and Adaptive Flight Control for an AutonomousHelicopter System." Thesis, University of Canterbury. Mechanical Engineering Department, 2013. http://hdl.handle.net/10092/8803.

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This thesis presents the development of self adaptive flight controller for an unmanned helicopter system under hovering manoeuvre. The neural network (NN) based model predictive control (MPC) approach is utilised in this work. We use this controller due to its ability to handle system constraints and the time varying nature of the helicopter dynamics. The non-linear NN based MPC controller is known to produce slow solution convergence due to high computation demand in the optimisation process. To solve this problem, the automatic flight controller system is designed using the NN based approximate predictive control (NNAPC) approach that relies on extraction of linear models from the non-linear NN model at each time step. The sequence of control input is generated using the prediction from the linearised model and the optimisation routine of MPC subject to the imposed hard constraints. In this project, the optimisation of the MPC objective criterion is implemented using simple and fast computation of the Hildreth's Quadratic Programming (QP) procedure. The system identification of the helicopter dynamics is typically performed using the time regression network (NNARX) with the input variables. Their time lags are fed into a static feed-forward network such as the multi-layered perceptron (MLP) network. NN based modelling that uses the NNARX structure to represent a dynamical system usually requires a priori knowledge about the model order of the system. Low model order assumption generally leads to deterioration of model prediction accuracy. Furthermore, massive amount of weights in the standard NNARX model can result in an increased NN training time and limit the application of the NNARX model in a real-time application. In this thesis, three types of NN architectures are considered to represent the time regression network: the multi-layered perceptron (MLP), the hybrid multi-layered perceptron (HMLP) and the modified Elman network. The latter two architectures are introduced to improve the training time and the convergence rate of the NN model. The model structures for the proposed architecture are selected using the proposed Lipschitz coefficient and k-cross validation methods to determine the best network configuration that guarantees good generalisation performance for model prediction. Most NN based modelling techniques attempt to model the time varying dynamics of a helicopter system using the off-line modelling approach which are incapable of representing the entire operating points of the flight envelope very well. Past research works attempt to update the NN model during flight using the mini-batch Levenberg-Marquardt (LM) training. However, due to the limited processing power available in the real-time processor, such approaches can only be employed to relatively small networks and they are limited to model uncoupled helicopter dynamics. In order to accommodate the time-varying properties of helicopter dynamics which change frequently during flight, a recursive Gauss-Newton (rGN) algorithm is developed to properly track the dynamics of the system under consideration. It is found that the predicted response from the off-line trained neural network model is suitable for modelling the UAS helicopter dynamics correctly. The model structure of the MLP network can be identified correctly using the proposed validation methods. Further comparison with model structure selection from previous studies shows that the identified model structure using the proposed validation methods offers improvements in terms of generalisation error. Moreover, the minimum number of neurons to be included in the model can be easily determined using the proposed cross validation method. The HMLP and modified Elman networks are proposed in this work to reduce the total number of weights used in the standard MLP network. Reduction in the total number of weights in the network structure contributes significantly to the reduction in the computation time needed to train the NN model. Based on the validation test results, the model structure of the HMLP and modified Elman networks are found to be much smaller than the standard MLP network. Although the total number of weights for both of the HMLP and modified Elman networks are lower than the MLP network, the prediction performance of both of the NN models are on par with the prediction quality of the MLP network. The identification results further indicate that the rGN algorithm is more adaptive to the changes in dynamic properties, although the generalisation error of repeated rGN is slightly higher than the off-line LM method. The rGN method is found capable of producing satisfactory prediction accuracy even though the model structure is not accurately defined. The recursive method presented here in this work is suitable to model the UAS helicopter in real time within the control sampling time and computational resource constraints. Moreover, the implementation of proposed network architectures such as the HMLP and modified Elman networks is found to improve the learning rate of NN prediction. These positive findings inspire the implementation of the real time recursive learning of NN models for the proposed MPC controller. The proposed system identification and hovering control of the unmanned helicopter system are validated in a 6 degree of freedom (DOF) safety test rig. The experimental results confirm the effectiveness and the robustness of the proposed controller under disturbances and parameter changes of the dynamic system.

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Sudakar, Madhavan. "Novel control techniques for a quadrotor based on the Sliding Mode Controller." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613746605628363.

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Yung, K. L. "Microprocessor based non-linear adaptive controller." Thesis, University of Plymouth, 1985. http://hdl.handle.net/10026.1/2492.

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The advent of microprocessors has created the possibility of developing low cost adaptive controllers for small process plants which in the past badly needed but could not afford such controllers. To examine the practicality of developing advanced low cost microprocessor based controller, this thesis describes the development of a non-linear adaptive controller for a nylon crimping plant which is a typical example of small process plants. In order to test the algorithm on site, an algorithm development/implement device basing on a novel multi-tasking concept was developed. This novel microprocessor based device can perform program development, on-line algorithm test and data logging at the same time, while, still maintaining its small size for easy transportation. When the control algorithm was fully developed and tested, a low cost dedicated controller using an Intel 8085 processor was designed to house the algorithm and as a direct replacement of the original analogue controller.

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Griesebner, Klaus. "Model-based Controller Development." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-34929.

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Model-based design is a powerful design technique for embedded system development. The technique enables virtual prototyping to develop and debug controllers before touching real hardware. There are many tools available covering the distinct steps of the design cycle including modeling, simulation, and implementation. Unfortunately, none of them covers all three steps. This thesis proposes a formalism coupling the model and the implementation of a controller for equation-based simulation tools. The resulting formalism translates defined controller models to platform specific code using a defined set of syntax. A case study of a line-following robot has been developed to illustrate the feasibility of the approach. The prototype has been tested and evaluated using a sequence of test scenarios of increasing difficulty. The final experiments suggest that the behaviors of both modeled and generated controllers are similar. The thesis concludes that the approach of model-implementation coupling of controllers in the simplest form is feasible for equation-based tools. This allows it to conduct the whole model-based design cycle within a single environment.

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Patkar, Abhishek. "Adaptive neural controller based on convex parametrization." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128972.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, September, 2020<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 65-67).<br>The problem of control of a class of nonlinear plants has been addressed by using neural networks together with sliding mode control to lead to global boundedness. We revisit this problem in this thesis and suggest a specific class of neural networks that employ convex activation functions. By using the algorithms that have been proposed previously for adaptive control in the presence of convex/concave parameterization for adjusting the weights of the neural network, it is shown that global boundedness of all signals can be achieved together with a better tracking error than non-adaptive controllers. It is also shown through simulation studies of an aircraft landing problem that the proposed adaptive controller can lead to better learning of the underlying nonlinearity.<br>by Abhishek Patkar.<br>S.M.<br>S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering

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Marchant, Andrew Nicholas. "An adaptive knowledge based controller for refrigerated potato stores." Thesis, University of Exeter, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393347.

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Rapley, Veronica Elizabeth. "Model-based adaptive cluster sampling." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433939.

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Logan, Beth Teresa. "Adaptive model-based speech enhancement." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625004.

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Books on the topic "Model based adaptive controller"

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J, Merhav Shmuel, and Ames Research Center, eds. Performance characteristics of an adaptive controller based on least-mean-square filters. National Aeronautics and Space Administration, Ames Research Center, 1987.

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2

Zhen-Lei, Zhou, and Goddard Space Flight Center, eds. Position control of redundant manipulators using an adaptive error-based control scheme. Catholic University of America, Dept. of Electrical Engineering, 1990.

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Zhen-Lei, Zhou, and Goddard Space Flight Center, eds. Position control of redundant manipulators using an adaptive error-based control scheme. Catholic University of America, Dept. of Electrical Engineering, 1990.

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Vedage, Vishwanath. A personal computer based adaptive controller for heat exchangers. University of East London, 1997.

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5

Basten, Twan, Roelof Hamberg, Frans Reckers, and Jacques Verriet, eds. Model-Based Design of Adaptive Embedded Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4821-1.

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Zourntos, Takis. Nonlinear adaptive control based on the related model. National Library of Canada, 1996.

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Melvin, James E. AUV fault detection using model based observer residuals. Naval Postgraduate School, 1998.

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MacDonald, Gordon S. Model based design and verification of a rapid dive controller for an Autonomous Underwater Vehicle. Naval Postgraduate School, 1989.

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Guerquin, Jan. The development of an IC engine model-based fuel injection controller with fuel film compensation. National Library of Canada, 2003.

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United States. National Aeronautics and Space Administration., ed. Command operator tracker based direct model reference adaptive control of a Puma 560 manipulator. Center for Intelligent Robotic Systems for Space Exploration, Rensselaer Polytechnic Institute, 1992.

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Book chapters on the topic "Model based adaptive controller"

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Graham, Bruce, and Robert Newell. "An adaptive fuzzy model-based controller." In Fuzzy Logic and Fuzzy Control. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58279-7_19.

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Liu, Xia, Li Li, and Qiyan Yan. "Research on PID Controller Based on Adaptive Internal Model Control." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3648-5_155.

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Sun, Jiayue, Shun Xu, Yang Liu, and Huaguang Zhang. "Neural Networks-Based Immune Optimization Regulation Using Adaptive Dynamic Programming." In Adaptive Dynamic Programming. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5929-7_2.

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AbstractThis chapter investigates optimal regulation scheme between tumor and immune cells based on ADP approach. The therapeutic goal is to inhibit the growth of tumor cells to allowable injury degree, and maximize the number of immune cells in the meantime. The reliable controller is derived through the ADP approach to make the number of cells achieve the specific ideal states. Firstly, the main objective is to weaken the negative effect caused by chemotherapy and immunotherapy, which means that minimal dose of chemotherapeutic and immunotherapeutic drugs can be operational in the treatment process. Secondly, according to nonlinear dynamical mathematical model of tumor cells, chemotherapy and immunotherapeutic drugs can act as powerful regulatory measures, which is a closed-loop control behavior. Finally, states of the system and critic weight errors are proved to be ultimately uniformly bounded with the appropriate optimization control strategy and the simulation results are shown to demonstrate effectiveness of the cybernetics methodology.

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Putratama, Muhammad Andy, Rémy Rigo-Mariani, Vincent Debusschere, and Yvon Bésanger. "Uncertainties Impact and Mitigation with an Adaptive Model-Based Voltage Controller." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55696-8_12.

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Seong, Junyeong, Sungjun Park, and Kunsoo Huh. "Robust Lane Keeping Control with Estimation of Cornering Stiffness and Model Uncertainty." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_39.

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AbstractThis paper introduces an adaptive lane-keeping control strategy that adapts to varying cornering stiffness while ensuring robustness against uncertainties. The system consists of three blocks: an Interacting Multiple Model (IMM) cornering stiffness estimator, a cornering stiffness uncertainty estimator, and a Robust Model Predictive Controller (RMPC). Improvements in estimation accuracy are achieved through a novel IMM probability derivation method, and the uncertainty estimator utilizes the IMM probability matrix to obtain reliable uncertainty boundaries. Real-time cornering stiffness estimations are integrated into the RMPC for adaptive model predictions. Uncertainty boundaries provide robustness against estimation error in the RMPC by constraint tightening and smoothing techniques. The performance of the estimator and controller is validated in simulations, where the overall control performance is compared to that of the Model Predictive Control (MPC) based on static cornering stiffness.

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Yu, Li, Xiaoxiong Liu, Weiguo Zhang, and Ming Ruichen. "Robust Flight Controller Design Based on Adaptive Nonlinear Dynamic Inverse with Reference Model." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8155-7_81.

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Mohapatra, Gayatri, and Manoj Kumar Debnath. "A Novel Fuzzy-Based Model Predictive Adaptive Controller for a PMSG Wind Turbine." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7076-3_7.

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Xu, Zhen, Mingchu Xu, and Qingwei Chen. "Adaptive Sliding Model Controller Design of Carlike Robot Speed and Steering Angle Based on Characteristic Model." In Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0474-7_80.

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Sathyamangalam Imran, Mohammed Irshadh Ismaaeel, Satyesh Shanker Awasthi, Michael Khayyat, Stefano Arrigoni, and Francesco Braghin. "A Rule-Defined Adaptive MPC Based Motion Planner for Autonomous Driving Applications." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_77.

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AbstractIn autonomous driving systems, motion planning to reach a given destination while avoiding obstacles becomes a task entirely managed by the on-board unit. In this work, we present a rule-defined motion planning algorithm for autonomous driving applications based on an adaptive Model Predictive Controller (MPC) framework. The motion planning task is first formulated as an Optimal Control Problem (OCP) subject to time-varying Control Barrier Function (CBF) constraints. It is then integrated within an MPC framework with adaptive weights settings, enabling the algorithm to dynamically adjust the MPC weights according to the rule-defined driving scenarios. The developed motion planner generates optimized trajectories for a high-fidelity Autonomous Vehicle (AV) model within IPG CarMaker software. Simulations performed showed that the developed motion planner adeptly facilitates successful overtaking, following, and stopping of the AV behind the Obstacle Vehicle (OV) based on rule-defined scenarios perceived by the AV.

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Gómez, Josué, Chidentree Treesatayapun, and América Morales. "Free Model Task Space Controller Based on Adaptive Gain for Robot Manipulator Using Jacobian Estimation." In Advances in Computational Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04497-8_22.

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Conference papers on the topic "Model based adaptive controller"

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Picardi, Giacomo, Lorenzo Pollini, Stefano Geluardi, Mario Olivari, Heinrich Buelthoff, and Mario Innocenti. "L1-based Model Following Control of an Identified Helicopter Model in Hover." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11466.

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The aim of this study is to augment the uncertain dynamics of the helicopter in order to resemble the dynamics of a new kind of vehicle, the so called Personal Aerial Vehicle. To achieve this goal a two step procedure is proposed. First, the helicopter model dynamics is augmented with a PID-based dynamic controller. Such controller implements a model following on the nominal helicopter model without uncertainties. Then, anL1 adaptive controller is designed to restore the nominal responses of the augmented helicopter when variations in the identified parameters are considered. The performance of the adaptive controller is evaluated via Montecarlo simulations. The results show that the application of the adaptive controller to the augmented helicopter dynamics can significantly reduce the effects of uncertainty due to the identification of the helicopter model. For implementation reasons the adaptive controller was applied to a subset of the outputs of the system. However, the under actuation typical of helicopters makes the tracking of the nominal responses good also on the not directly adapted outputs.

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Xu, Chengtao, Zhaowu Ping, Yuqian He, Yunzhi Huang, and Jun-Guo Lu. "An internal model based adaptive controller design for robot manipulator." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662224.

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Chu, Kenny Sau Kang, KuewWai Chew, Yoong Choon Chang, Stella Morris, and Kein Huat Chua. "Adaptive Direct Current Motor Proportional Integral Derivative Controller based on Deep Learning Model." In 2024 3rd Asian Conference on Frontiers of Power and Energy (ACFPE). IEEE, 2024. https://doi.org/10.1109/acfpe63443.2024.10801042.

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Zeng, Fuchuan, Xuejian Zhang, Hang Li, and Xiaobing Hu. "Neural Network-Based Adaptive Optimal Terminal Sliding Model Controller for Robot Manipulator Trajectory Tracking." In 2024 8th International Conference on Automation, Control and Robots (ICACR). IEEE, 2024. https://doi.org/10.1109/icacr62205.2024.11053746.

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Ding-lei, Wang. "Model Reference Based Neural Network Adaptive Controller." In 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop (KAM 2008 Workshop). IEEE, 2008. http://dx.doi.org/10.1109/kamw.2008.4810600.

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Amini, Mohammad Reza, Mahdi Shahbakhti, Selina Pan, and J. Karl Hedrick. "Handling Model and Implementation Uncertainties via an Adaptive Discrete Sliding Mode Controller Design." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9732.

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Analog-to-digital conversion (ADC) and uncertainties in modeling the plant dynamics are the main sources of imprecisions in the design cycle of model-based controllers. These implementation and model uncertainties should be addressed in the early stages of the controller design, otherwise they could lead to failure in the controller performance and consequently increase the time and cost required for completing the controller verification and validation (V&V) with more iterative loops. In this paper, a new control approach is developed based on a nonlinear discrete sliding mode controller (DSMC) formulation to mitigate the ADC imprecisions and model uncertainties. To this end, a DSMC design is developed against implementation imprecisions by incorporating the knowledge of ADC uncertainties on control inputs via an online uncertainty prediction and propagation mechanism. Next, a generic online adaptive law will be derived to compensate for the impact of an unknown parameter in the controller equations that is assumed to represent the model uncertainty. The final proposed controller is an integrated adaptive DSMC with robustness to implementation and model uncertainties that includes (i) an online ADC uncertainty mechanism, and (ii) an online adaptation law. The proposed adaptive control approach is evaluated on a nonlinear automotive engine control problem in real-time using a processor-in-the-loop (PIL) setup with an actual electronic control unit (ECU). The results reveal that the proposed adaptive control technique removes the uncertainty in the model fast, and significantly improves the robustness of the controllers to ADC imprecisions. This provides up to 60% improvement in the performance of the controller under implementation and model uncertainties compared to a baseline DSMC, in which there are no incorporated ADC imprecisions.

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Pham, Hoang Anh, and Dirk Söffker. "Modified Model-Free Adaptive Control Method Applied to Vibration Control of an Elastic Crane." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97654.

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Abstract Model-free adaptive control (MFAC) is a data-driven control approach receiving increased attention in the last years. Different model-free-based control strategies are proposed to design adaptive controllers when mathematical models of the controlled systems should not be used or are not available. Using only measurements (I/O data) from the system, a feedback controller is generated without the need of any structural information about the controlled plant. In this contribution an improved MFAC is discussed for control of unknown multivariable flexible systems. The main improvement in control input calculation is based on the consideration of output tracking errors and its variations. A new updated control input algorithm is developed. The novel idea is firstly applied for controlling vibrations of a MIMO ship-mounted crane. The control efficiency is verified via numerical simulations. The simulation results demonstrate that vibrations of the elastic boom and the payload of the crane can be reduced significantly and better control performance is obtained when using the proposed controller compared to standard model-free adaptive and PI controllers.

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Abdelhameed, Magdy Mohamed, Unat Pinson, and Sabri Cetinkunt. "Adaptive Learning Algorithm for Cerebellar Model Articulation Controller: Neural Network Based Hybrid-Type Controller—Part II." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2201.

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Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks adequate learning algorithm especially when it is used in a hybrid-type controller. Part I of this work was devoted to introduce a new CMAC adaptive learning algorithm. Part II will be directed to experimental application of new learning algorithm of a CMAC based hybrid-type real time controller. The proposed controller is applied for the trajectory tracking of a piezoelectric actuated tool post. It has been proven that the piezoelectric actuated tool post has hysteretic behavior. Extensive experiments have been carried out on the experimental setup to evaluate the proposed adaptive learning algorithm of CMAC. Only few experiments and their results are being presented. In these experiments, the performance of the piezoelectric actuated tool post has been examined and evaluated using different types of control algorithms and applying external load disturbance. The control performance of the proposed controller is compared with those of conventional controllers (PI controller and the conventional CMAC based controller). The experimental results showed that performance of the hybrid-type controller using the proposed learning algorithm is stable and more effective than that of the conventional controllers. Testing and comparing the learning ability of the proposed learning algorithm with that of the conventional CMAC learning algorithm indicated the effectiveness of the learning ability of the proposed algorithm. Finally, the response using the proposed hybrid-type controller is slightly better than using the conventional PI controller under the effect of external load disturbance.

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Abdelrauf, Ahmed A., M. Abdel-Geliel, and E. Zakzouk. "Adaptive PID controller based on model predictive control." In 2016 European Control Conference (ECC). IEEE, 2016. http://dx.doi.org/10.1109/ecc.2016.7810378.

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Gadsden, S. Andrew. "An Adaptive PID Controller Based on Bayesian Theory." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5340.

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One of the most popular trajectory-tracking controllers used in industry is the PID controller. The PID controller utilizes three types of gains and the tracking error in order to provide a control gain to a system. The PID gains may be tuned manually or using a number of different techniques. Under most operating conditions, only one set of PID gains are used. However, techniques exist to compensate for dynamic systems such as gain scheduling or basic time-varying functions. In this paper, an adaptive PID controller is presented based on Bayesian theory. The interacting multiple model (IMM) method, which utilizes Bayes’ theorem and likelihood functions, is implemented on the PID controller to present an adaptive control strategy. The strategy is applied to a simulated electromechanical system, and the results of the proposed controller are compared with the standard PID method. Future work is also considered.

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Reports on the topic "Model based adaptive controller"

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Mayo, Jackson, Karla Morris Wright, Jon Aytac, et al. Demonstration of Model-Based Design for Digital Controller Using Formal Methods. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2430067.

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Garris, Michael D. Component-based handprint segmentation using adaptive writing style model. National Institute of Standards and Technology, 1996. http://dx.doi.org/10.6028/nist.ir.5843.

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Basher, A. M. H. Development of a Robust Model-Based Water Level Controller for U-Tube Steam Generator. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/814425.

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Cui, Xiaohui, and Thomas E. Potok. Particle Swarm Social Adaptive Model for Multi-Agent Based Insurgency Warfare Simulation. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/984372.

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Rangaswamy, Muralidhar. Parametric and Model Based Adaptive Detection Algorithms for Non-Gaussian Interference Backgrounds. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada369457.

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Eguchi, Hiroaki, Takanori Fukao, and Koichi Osuka. Design Method of Reference Model for Active Steering Based on Nonlinear Adaptive D* Control. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0423.

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Hand, M. M. Variable-Speed Wind Turbine Controller Systematic Design Methodology: A Comparison of Non-Linear and Linear Model-Based Designs. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/12172.

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Osadchyi, Viacheslav V., Hanna Y. Chemerys, Kateryna P. Osadcha, Vladyslav S. Kruhlyk, Serhii L. Koniukhov, and Arnold E. Kiv. Conceptual model of learning based on the combined capabilities of augmented and virtual reality technologies with adaptive learning systems. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4417.

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The article is devoted to actual problem of using modern ICT tools to increase the level of efficiency of the educational process. The current state and relevance of the use of augmented reality (AR) and virtual reality (VR) technologies as an appropriate means of improving the educational process are considered. In particular, attention is paid to the potential of the combined capabilities of AR and VR technologies with adaptive learning systems. Insufficient elaboration of cross-use opportunities for achieving of efficiency of the educational process in state-of-the-art research has been identified. Based on analysis of latest publications and experience of using of augmented and virtual reality technologies, as well as the concept of adaptive learning, conceptual model of learning based on the combined capabilities of AR and VR technologies with adaptive learning systems has been designed. The use of VR and AR technologies as a special information environment is justified, which is applied in accordance with the identified dominant type of students' thinking. The prospects of using the proposed model in training process at educational institutions for the implementation and support of new teaching and learning strategies, as well as improving learning outcomes are determined by the example of such courses as “Algorithms and data structures”, “Computer graphics and three-dimensional modeling”, “Circuit Engineering”, “Computer Architecture”.

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capabilities of deep learning architectures. Additionally, it explores biologically plausible learning mechanisms such as Hebbian learning, spike-timing-dependent plasticity (STDP), and neuromodulation, drawing insights from neuroscience and cognitive computing. The role of spiking neural networks (SNNs) and neuromorphic computing in low-power AI and real-time decision-making is also analyzed, with applications in robotics, brain-computer interfaces, edge AI, and cognitive computing. Case studies highlight the industrial adoption of biologically inspired AI, focusing on adaptive neural controllers, neuromorphic vision, and memory-based architectures. This research underscores the importance of integrating theoretical learning principles with biologically motivated AI models to develop more interpretable, generalizable, and scalable intelligent systems. Keywords Neural computation, learning theory, expressivity, deep learning, recurrent neural networks, spiking neural networks, biologically inspired AI, infinite-width networks, kernel methods, attractor networks, synaptic plasticity, STDP, neuromodulation, cognitive computing, dynamical systems, function approximation, generalization, AI stability, neuromorphic computing, robotics, brain-computer interfaces, edge AI, biologically plausible learning.

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Sikora, Yaroslava B., Olena Yu Usata, Oleksandr O. Mosiiuk, Dmytrii S. Verbivskyi, and Ekaterina O. Shmeltser. Approaches to the choice of tools for adaptive learning based on highlighted selection criteria. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4447.

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The article substantiates the relevance of adaptive learning of students in the modern information society, reveals the essence of such concepts as “adaptability” and “adaptive learning system”. It is determined that a necessary condition for adaptive education is the criterion of an adaptive learning environment that provides opportunities for advanced education, development of key competencies, formation of a flexible personality that is able to respond to different changes, effectively solve different problems and achieve results. The authors focus on the technical aspect of adaptive learning. Different classifications of adaptability are analyzed. The approach to the choice of adaptive learning tools based on the characteristics of the product quality model stated by the standard ISO / IEC 25010 is described. The following criteria for the selecting adaptive learning tools are functional compliance, compatibility, practicality, and support. By means of expert assessment method there were identified and selected the most important tools of adaptive learning, namely: Acrobatiq, Fishtree, Knewton (now Wiliy), Lumen, Realize it, Smart Sparrow (now Pearson). Comparative tables for each of the selected tools of adaptive learning according to the indicators of certain criteria are given.

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