Academic literature on the topic 'Enhancing feedback control Jerk system'
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Journal articles on the topic "Enhancing feedback control Jerk system"
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Karam, Adel Abed. "Controlling of jerk chaotic system via linear feedback control strategies." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 370–78. https://doi.org/10.11591/ijeecs.v20.i1.pp370-378.
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In this paper, the strategies of linear feedback control for jerk system is considered. These strategies consist of four strategies (ordinary feedback control, dislocated feedback control, speed feedback control, and enhancing feedback control). We propose to combine between these strategies and obtained a better result from this combine. Numerical solution achieved the same results.
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Adel Abed, Karam. "Controlling of jerk chaotic system via linear feedback control strategies." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 370. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp370-378.
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In this paper, the strategies of linear feedback control for jerk system is considered. These strategies consist of four strategies (ordinary feedback control, dislocated feedback control, speed feedback control, and enhancing feedback control). We propose to combine between these strategies and obtained a better result from this combine. Numerical solution achieved the same results.
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Adeleke, Adeniyi Kehinde, Thompson Odion Igunma, and Zamathula Sikhakhane Nwokediegwu. "Modeling Advanced Numerical Control Systems to Enhance Precision in Next-Generation Coordinate Measuring Machine." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 1 (2021): 638–49. https://doi.org/10.54660/.ijmrge.2021.2.1.638-649.
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The evolution of coordinate measuring machines (CMMs) has significantly improved precision in dimensional metrology, driven by the need for higher accuracy in manufacturing and quality assurance. Advanced numerical control (NC) systems play a pivotal role in optimizing CMM performance by enhancing motion control, reducing measurement uncertainty, and improving data acquisition speed. This research focuses on modeling advanced NC systems to enhance precision in next-generation CMMs by integrating artificial intelligence (AI)-driven control algorithms, real-time error compensation techniques, and adaptive feedback mechanisms. A hybrid modeling approach is proposed, combining physics-based dynamic modeling with AI-based predictive control to achieve sub-micron accuracy. The study explores the integration of real-time kinematic error compensation, leveraging machine learning algorithms to predict and correct deviations caused by thermal expansion, mechanical vibrations, and backlash effects. The model also incorporates sensor fusion techniques to improve the precision of spatial positioning, utilizing high-resolution encoders, laser interferometry, and inertial measurement units. Finite element analysis (FEA) is used to simulate the mechanical behavior of CMM structures under various loading conditions, ensuring optimal rigidity and stability. Additionally, a robust closed-loop control strategy employing proportional-integral-derivative (PID) controllers and fuzzy logic is implemented to enhance motion smoothness and reduce positional drift. The research further investigates the impact of advanced trajectory planning algorithms, such as jerk-limited motion profiles and model predictive control (MPC), in minimizing dynamic errors and improving measurement repeatability. Experimental validation is conducted on a prototype CMM equipped with an advanced NC system, demonstrating significant improvements in precision and repeatability compared to conventional systems. The results show that integrating AI-driven control, real-time error compensation, and adaptive feedback significantly reduces measurement errors and enhances system robustness. This study provides a comprehensive framework for developing next-generation CMMs with enhanced precision, paving the way for more accurate and reliable metrology solutions in aerospace, automotive, and semiconductor industries. Future work will focus on further optimizing AI-based control algorithms and exploring the potential of digital twin technology for real-time CMM performance monitoring and predictive maintenance.
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Xu, Changjin, Maoxin Liao, Peiluan Li, Lingyun Yao, Qiwen Qin, and Youlin Shang. "Chaos Control for a Fractional-Order Jerk System via Time Delay Feedback Controller and Mixed Controller." Fractal and Fractional 5, no. 4 (2021): 257. http://dx.doi.org/10.3390/fractalfract5040257.
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In this study, we propose a novel fractional-order Jerk system. Experiments show that, under some suitable parameters, the fractional-order Jerk system displays a chaotic phenomenon. In order to suppress the chaotic behavior of the fractional-order Jerk system, we design two control strategies. Firstly, we design an appropriate time delay feedback controller to suppress the chaos of the fractional-order Jerk system. The delay-independent stability and bifurcation conditions are established. Secondly, we design a suitable mixed controller, which includes a time delay feedback controller and a fractional-order PDσ controller, to eliminate the chaos of the fractional-order Jerk system. The sufficient condition ensuring the stability and the creation of Hopf bifurcation for the fractional-order controlled Jerk system is derived. Finally, computer simulations are executed to verify the feasibility of the designed controllers. The derived results of this study are absolutely new and possess potential application value in controlling chaos in physics. Moreover, the research approach also enriches the chaos control theory of fractional-order dynamical system.
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Vaidyanathan, Sundarapandian. "A novel 3-D jerk chaotic system with three quadratic nonlinearities and its adaptive control." Archives of Control Sciences 26, no. 1 (2016): 19–47. http://dx.doi.org/10.1515/acsc-2016-0002.
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This paper announces an eight-term novel 3-D jerk chaotic system with three quadratic nonlinearities. The phase portraits of the novel jerk chaotic system are displayed and the qualitative properties of the jerk system are described. The novel jerk chaotic system has two equilibrium points, which are saddle-foci and unstable. The Lyapunov exponents of the novel jerk chaotic system are obtained as L1= 0.20572,L2= 0 and L3= −1.20824. Since the sum of the Lyapunov exponents of the jerk chaotic system is negative, we conclude that the chaotic system is dissipative. The Kaplan-Yorke dimension of the novel jerk chaotic system is derived as DKY= 2.17026. Next, an adaptive controller is designed via backstepping control method to globally stabilize the novel jerk chaotic system with unknown parameters. Moreover, an adaptive controller is also designed via backstepping control method to achieve global chaos synchronization of the identical jerk chaotic systems with unknown parameters. The backstepping control method is a recursive procedure that links the choice of a Lyapunov function with the design of a controller and guarantees global asymptotic stability of strict feedback systems. MATLAB simulations have been depicted to illustrate the phase portraits of the novel jerk chaotic system and also the adaptive backstepping control results.
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Li, Fang Fang, Jin Bao Ji, Xiao Jun Li, and Na Li. "Application of the Jerk Feedback on the Shaking Table." Applied Mechanics and Materials 580-583 (July 2014): 1521–27. http://dx.doi.org/10.4028/www.scientific.net/amm.580-583.1521.
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For the shaking table system, the 90o phase shift frequency of the servo valve system is close to the oil column resonance frequency, the bandwidth under the influence of servo valve features was so narrow that it cannot meet requirements. Multi-variable control is presented in this paper by introducing the jerk feedback on the basis of the three-variable control. The theoretical derivation and the system simulation show that: the jerk feedback can reduce the oil column resonance frequency, and decrease the influence of the system performance caused by the servo valve characteristics.
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Ali, Ali Abdulkareem, Fatma Ben Salem, and Jamal A. K. Mohammed. "Enhancing Elevator Ride Quality through Vector Control Techniques and S-Curve Profiles." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18785–91. https://doi.org/10.48084/etasr.9228.
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This study examines motor drive techniques, including Field-Oriented Control (FOC), sensorless FOC, and Direct Torque Control (DTC), to improve elevator ride quality by reducing jerk-sudden changes in acceleration that cause discomfort. A 200 cm tall prototype elevator system was developed, using S-curve velocity profiles alongside the considered control strategies. The system includes a TMS320F28379D DSP-controlled induction motor, sensors, and an encoder to assess performance. Results show that FOC with S-curve profiles reduces jerk by 72–73%, significantly improving comfort compared to the standard trapezoidal profile. Sensorless FOC reduces jerk by 68–71%, providing a cost-effective option, though it faces challenges during downward motion under load. DTC, reduces jerk by 65–68% and results in less smooth travel, especially during downward movement. In comparison, the trapezoidal velocity profile produced higher jerk levels and less ride comfort. This study emphasizes the critical role of control technique selection in enhancing elevator comfort and efficiency.
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Vaidyanathan, Sundarapandian. "A new 3-D jerk chaotic system with two cubic nonlinearities and its adaptive backstepping control." Archives of Control Sciences 27, no. 3 (2017): 409–39. http://dx.doi.org/10.1515/acsc-2017-0026.
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AbstractThis paper presents a new seven-term 3-D jerk chaotic system with two cubic nonlinearities. The phase portraits of the novel jerk chaotic system are displayed and the qualitative properties of the jerk system are described. The novel jerk chaotic system has a unique equilibrium at the origin, which is a saddle-focus and unstable. The Lyapunov exponents of the novel jerk chaotic system are obtained as L1= 0:2974, L2= 0 and L3= −3:8974. Since the sum of the Lyapunov exponents of the jerk chaotic system is negative, we conclude that the chaotic system is dissipative. The Kaplan-Yorke dimension of the new jerk chaotic system is found as DKY= 2:0763. Next, an adaptive backstepping controller is designed to globally stabilize the new jerk chaotic system with unknown parameters. Moreover, an adaptive backstepping controller is also designed to achieve global chaos synchronization of the identical jerk chaotic systems with unknown parameters. The backstepping control method is a recursive procedure that links the choice of a Lyapunov function with the design of a controller and guarantees global asymptotic stability of strict feedback systems. MATLAB simulations are shown to illustrate all the main results derived in this work.
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Tian, Huaigu, Zhen Wang, Peijun Zhang, Mingshu Chen, and Yang Wang. "Dynamic Analysis and Robust Control of a Chaotic System with Hidden Attractor." Complexity 2021 (January 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/8865522.
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In this paper, a 3D jerk chaotic system with hidden attractor was explored, and the dissipativity, equilibrium, and stability of this system were investigated. The attractor types, Lyapunov exponents, and Poincare section of the system under different parameters were analyzed. Additionally, a circuit was carried out, and a good similarity between the circuit experimental results and the theoretical analysis testifies the feasibility and practicality of the original system. Furthermore, a robust feedback controller was designed based on the finite-time stability theory, which guarantees the synchronization of 3D jerk master-slave system in finite time and asymptotically converges to the origin. Finally, we also give verification for the discussion in this paper by numerical simulation.
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Stoychitch, Mihaylo Y. "Design of a Digital Positioning System with Sinusoidal Change of the Jerk." Applied Mechanics and Materials 474 (January 2014): 255–60. http://dx.doi.org/10.4028/www.scientific.net/amm.474.255.
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In this paper we consider design of a digital electromechanical positioning system that ensures controlled change of the jerk. A system that is formed from a load and an actuating device is used as an object. For the proposed sinusoidal change of the jerk, the appropriate changes of the acceleration, velocity and displacement were found.. The control algorithm which ensures the motion of the object with sinusoidal change of the jerk so that the requirements which are related to the maximum values of the acceleration, velocity and displacement are satisfied, is also proposed. The digital feedforward and feedback controllers that are realized this algorithm are designed. Furthermore the simulation of that system is performed, which is confirmed the proposed theory.
Dissertations / Theses on the topic "Enhancing feedback control Jerk system"
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Slingsby, W. T. B. "An antenna isolation-enhancing system for On-Frequency Radio Repeaters." Thesis, University of Bristol, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245481.
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Meadows, Alexander David. "Plant error compensation and jerk control for adaptive cruise control systems." 2013. http://hdl.handle.net/1805/3426.
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Indiana University-Purdue University Indianapolis (IUPUI)<br>Some problems of complex systems are internal to the system whereas other problems exist peripherally; two such problems will be explored in this thesis. First, is the issue of excessive jerk from instantaneous velocity demand changes produced by an adaptive cruise control system. Calculations will be demonstrated and an example control solution will be proposed in Chapter 3. Second, is the issue of a non-perfect plant, called an uncertain or corrupted plant. In initial control analysis, the adaptive cruise control systems are assumed to have a perfect plant; that is to say, the plant always behaves as commanded. In reality, this is seldom the case. Plant corruption may come from a variation in performance through use or misuse, or from noise or imperfections in the sensor signal data. A model for plant corruption is introduced and methods for analysis and compensation are explored in Chapter 4. To facilitate analysis, Chapter 2 discusses the concept of system identification, an order reduction tool which is employed herein. Adaptive cruise control systems are also discussed with special emphasis on the situations most likely to employ jerk limitation.
Book chapters on the topic "Enhancing feedback control Jerk system"
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Walia, Simrat, and Jyotsna Sengupta. "IOT BASED EVAPOTRANSPIRATION IRRIGATION SYSTEM FOR ENHANCING WATER CONSERVATION." In Futuristic Trends in Computing Technologies and Data Sciences Volume 3 Book 4. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bict4p1ch2.
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In developing countries, water conservation has immense significance where economies rely on for livelihood and food security. The irrigation and other farming activities involve consuming a large amount of water. Smart farming uses IoT (Internet of Things) and WSN (Wireless Sensor Network) to handle fundamental farming activities like irrigation scheduling, weed control, pest control and disease managementinvolving sensor data acquisition, data storage, and data processing. The sensor inputs and their comparison against prescribed values for decision making utilized in these systems. This chapter proposed an automated irrigation system capable of monitoring field conditions and controlling the irrigation process. The proposed system aims at saving water, manpower saving, risk management and other resources. The system integrates various information from sensors, evapotranspiration and online weather forecasts. NodeMCU microcontroller, 5v DC submersible water pump, motor driver module (L293D), Capacitive soil moisture sensor, water flow sensor (FS300A), and solderless breadboard are the main hardware components of system. The sensor analyses soil moisture and delivers data to the microcontroller at predetermined intervals. When the soil moisture level drops below the threshold, a submersible water pump, which is linked to a microcontroller through a motor driver module, is activated to supply water. Based on the ETo of the region and infield sensor feedback, the system generates irrigation schedules.
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Akilan, A., and Sharon Chris Hepzebah P. "Rehabilitation Robotics in Adaptive Systems for Patient Recovery." In Intelligent Healthcare System. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552210-10.
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Rehabilitation robotics has emerged as a transformative approach in the management of various neurodegenerative disorders, offering novel solutions to enhance patient recovery. This chapter explores the integration of advanced robotic technologies in adaptive rehabilitation systems, focusing on their role in personalized care for conditions such as Parkinson’s disease and multiple sclerosis. By leveraging adaptive control strategies, real-time biosignal processing, and artificial intelligence, rehabilitation robots can provide individualized therapeutic interventions that address the unique needs of each patient. The chapter highlights the applications of robotic exoskeletons, robotic arms, and wearable devices in improving motor function, muscle strength, and overall mobility. Key challenges in the field, including safety, patient acceptance, and the need for customizable systems, are also discussed. The role of AI-based decision support systems in enhancing clinical practice and providing real-time feedback to clinicians was examined. Ultimately, this chapter underscores the potential of rehabilitation robotics to revolutionize patient care, offering a promising future for improving the quality of life for individuals with neurodegenerative disorders.
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Khoddami Maraghi, Zahra. "Vibration and Instability of Smart-Composite Sandwich Structure: Flutter and Divergences." In Vibration Engineering - Analysis, Control, and Utilization [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1009143.
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This study presents a comprehensive analysis of the vibration and instability characteristics of smart sandwich structures, focusing on beams, circular plates, and square plates with magnetorheological fluid (MRF) cores and magnetostrictive (Ms) skins. Timoshenko beam theory is applied to model the beam structures, accounting for both shear deformation and rotational inertia, while classical plate theory is used for the plate structures to capture the rheology behavior. The energy method is applied to derive the system’s potential and kinetic energy, which are essential for evaluating the stability and vibration characteristics, and the differential quadrature method (DQM) is employed to solve the governing equations numerically. The research investigates the impact of magnetic fields on the natural frequency, loss factor, and system stability using several MRF models. Geometric parameters, including core and skin thickness, significantly affected the natural frequency and damping behavior, identifying flutter and divergences phenomena. The study also introduces a velocity feedback control parameter (Kvfc), which demonstrated an opposing effect on magnetic fields by enhancing damping and reducing the natural frequency. The research provides valuable insights for optimizing vibration control and stability in MRF-based sandwich structures.
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Geisler, C. Daniel. "Middle Ear." In From Sound To Synapse. Oxford University PressNew York, NY, 1998. http://dx.doi.org/10.1093/oso/9780195100259.003.0004.
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Abstract Transmission of sound through the mammalian middle ear is provided by a chain of the tiniest bones in the body. The physical properties of these bones and their supporting structures determine how efficiently this transmission is. In the absence of feedback from the central nervous system, middle ear mechanisms are essentially linear, allowing the highly developed techniques for analyzing passive mechanical systems to be employed. The fruits of those analyses give us a firm understanding of the general attributes of mid dle ear transmissions. In the alert mammal, however, the middle ear is not a passive structure but one that is under continuous central control. We will review that control, highlighting its important protective features and its role in enhancing the usefulness of intense acoustic signals.
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Liu, Hao, Yinghong Pan, Junqi Zhan, Yuan Xie, Yu Lin, and Yingqi Feng. "Application of Foot-Controlled Technology in Assisting Desk Activities for People with Upper Limb Disabilities." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2025. https://doi.org/10.3233/faia250311.
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This study focuses on developing a foot-controlled robotic arm system to assist individuals with upper limb disabilities in performing desk activities, thus enhancing their autonomy and quality of life. By integrating advanced foot gesture recognition technology with robotic arm control, we designed and implemented a direct, user-friendly assistance system. This system enables users to control the robotic arm precisely through foot movements, allowing for various desk tasks such as writing and picking up items. Evaluation results show that the system can be effectively controlled by the target user group, significantly improving their ability to complete daily tasks. User feedback confirmed its usability and positive impact on quality of life. This research demonstrates the potential of foot-controlled technology as a new solution for assisting individuals with upper limb disabilities in desk activities, showcasing the role of assistive technology in promoting autonomy. Additionally, the study provides valuable insights for the future development and refinement of related technologies.
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Gao, Chongli, Mingyue Xia, Zhehao Zhang, Yongpeng Han, and Yaxin Gu. "Improving the Brain-Computer Interface Learning Process with Gamification in Motor Imagery: A Review." In Gamification - Analysis, Design and Development [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105715.
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Brain-computer-interface-based motor imagery (MI-BCI), a control method for transferring the imagination of motor behavior to computer-based commands, could positively impact neural functions. With the safety guaranteed by non-invasive BCI devices, this method has the potential to enhance rehabilitation and physical outcomes. Therefore, this MI-BCI control strategy has been highly researched. However, applying a non-invasive MI-BCI to real life is still not ideal. One of the main reasons is the monotonous training procedure. Although researchers have reviewed optimized signal processing methods, no suggestion is found in training feedback design. The authors believe that enhancing the engagement interface via gamification presents a potential method that could increase the MI-BCI outcome. After analyzing 2524 articles (from 2001 to 2020), 28 pieces of research are finally used to evaluate the feasibility of using gamified MI-BCI system for training. This paper claims that gamification is feasible for MI-BCI training with an average accuracy of 74.35% among 111 individuals and positive reports from 26 out of 28 studies. Furthermore, this literature review suggests more emphasis should be on immersive and humanoid design for a gaming system, which could support relieving distraction, stimulate correct MI and improve learning outcomes. Interruptive training issues such as disturbing graphical interface design and potential solutions have also been presented for further research.
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Scano, Alessandro, Marco Caimmi, Andrea Chiavenna, Matteo Malosio, and Lorenzo Molinari Tosatti. "A Kinect-Based Biomechanical Assessment of Neurological Patients' Motor Performances for Domestic Rehabilitation." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9740-9.ch013.
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Stroke is one of the main causes of disability in Western countries. Damaged brain areas are not able to provide the fine-tuned muscular control typical of human upper-limbs, resulting in many symptoms that affect consistently patients' daily-life activities. Neurological rehabilitation is a multifactorial process that aims at partially restoring the functional properties of the impaired limbs, taking advantage of neuroplasticity, i.e. the capability of re-aggregating neural networks in order to repair and substitute the damaged neural circuits. Recently, many virtual reality-based, robotic and exoskeleton approaches have been developed to exploit neuroplasticity and help conventional therapies in clinic. The effectiveness of such methods is only partly demonstrated. Patients' performances and clinical courses are assessed via a variety of complex and expensive sensors and time-consuming techniques: motion capture systems, EMG, EEG, MRI, interaction forces with the devices, clinical scales. Evidences show that benefits are proportional to treatment duration and intensity. Clinics can provide intensive assistance just for a limited amount of time. Thus, in order to preserve the benefits and increase them in time, the rehabilitative process should be continued at home. Simplicity, easiness of use, affordability, reliability and capability of storing logs of the rehabilitative sessions are the most important requirements in developing devices to allow and facilitate domestic rehabilitation. Tracking systems are the primary sources of information to assess patients' motor performances. While expensive and sophisticated techniques can investigate neuroplasticity, neural activation (fMRI) and muscle stimulation patterns (EMG), the kinematic assessment is fundamental to provide basic but essential quantitative evaluations as range of motion, motor control quality and measurements of motion abilities. Microsoft Kinect and Kinect One are programmable and affordable tracking sensors enabling the measurement of the positions of human articular centers. They are widely used in rehabilitation, mainly for interacting with virtual environments and videogames, or training motor primitives and single joints. In this paper, the authors propose a novel use of the Kinect and Kinect One sensors in a medical protocol specifically developed to assess the motor control quality of neurologically impaired people. It is based on the evaluation of clinically meaningful synthetic performance indexes, derived from previously developed experiences in upper-limb robotic treatments. The protocol provides evaluations taking into account kinematics (articular clinical angles, velocities, accelerations), dynamics (shoulder torque and shoulder effort index), motor and postural control quantities (normalized jerk of the wrist, coefficient of periodicity, center of mass displacement). The Kinect-based platform performance evaluation was off-line compared with the measurements obtained with a marker-based motion tracking system during the execution of reaching tasks against gravity. Preliminary results based on the Kinect sensor suggest its efficacy in clustering healthy subjects and patients according to their motor performances, despite the less sensibility in respect to the marker-based system used for comparison. A software library to evaluate motor performances has been developed by the authors, implemented in different programming languages and is available for on-line use during training/evaluation sessions (Figure 1). The Kinect sensor coupled with the developed computational library is proposed as an assessment technology during domestic rehabilitation therapies with on-line feedback, enabled by an application featuring tracking, graphical representation and data logging. An experimental campaign is under development on post-stroke patients with the Kinect-One sensor. Preliminary results on patients with different residual functioning and level of impairment indicate the capability of the whole system in discriminating motor performances.
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Scano, Alessandro, Marco Caimmi, Andrea Chiavenna, Matteo Malosio, and Lorenzo Molinari Tosatti. "A Kinect-Based Biomechanical Assessment of Neurological Patients' Motor Performances for Domestic Rehabilitation." In Robotic Systems. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch042.
Full textAbstract:
Stroke is one of the main causes of disability in Western countries. Damaged brain areas are not able to provide the fine-tuned muscular control typical of human upper-limbs, resulting in many symptoms that affect consistently patients' daily-life activities. Neurological rehabilitation is a multifactorial process that aims at partially restoring the functional properties of the impaired limbs, taking advantage of neuroplasticity, i.e. the capability of re-aggregating neural networks in order to repair and substitute the damaged neural circuits. Recently, many virtual reality-based, robotic and exoskeleton approaches have been developed to exploit neuroplasticity and help conventional therapies in clinic. The effectiveness of such methods is only partly demonstrated. Patients' performances and clinical courses are assessed via a variety of complex and expensive sensors and time-consuming techniques: motion capture systems, EMG, EEG, MRI, interaction forces with the devices, clinical scales. Evidences show that benefits are proportional to treatment duration and intensity. Clinics can provide intensive assistance just for a limited amount of time. Thus, in order to preserve the benefits and increase them in time, the rehabilitative process should be continued at home. Simplicity, easiness of use, affordability, reliability and capability of storing logs of the rehabilitative sessions are the most important requirements in developing devices to allow and facilitate domestic rehabilitation. Tracking systems are the primary sources of information to assess patients' motor performances. While expensive and sophisticated techniques can investigate neuroplasticity, neural activation (fMRI) and muscle stimulation patterns (EMG), the kinematic assessment is fundamental to provide basic but essential quantitative evaluations as range of motion, motor control quality and measurements of motion abilities. Microsoft Kinect and Kinect One are programmable and affordable tracking sensors enabling the measurement of the positions of human articular centers. They are widely used in rehabilitation, mainly for interacting with virtual environments and videogames, or training motor primitives and single joints. In this paper, the authors propose a novel use of the Kinect and Kinect One sensors in a medical protocol specifically developed to assess the motor control quality of neurologically impaired people. It is based on the evaluation of clinically meaningful synthetic performance indexes, derived from previously developed experiences in upper-limb robotic treatments. The protocol provides evaluations taking into account kinematics (articular clinical angles, velocities, accelerations), dynamics (shoulder torque and shoulder effort index), motor and postural control quantities (normalized jerk of the wrist, coefficient of periodicity, center of mass displacement). The Kinect-based platform performance evaluation was off-line compared with the measurements obtained with a marker-based motion tracking system during the execution of reaching tasks against gravity. Preliminary results based on the Kinect sensor suggest its efficacy in clustering healthy subjects and patients according to their motor performances, despite the less sensibility in respect to the marker-based system used for comparison. A software library to evaluate motor performances has been developed by the authors, implemented in different programming languages and is available for on-line use during training/evaluation sessions (Figure 1). The Kinect sensor coupled with the developed computational library is proposed as an assessment technology during domestic rehabilitation therapies with on-line feedback, enabled by an application featuring tracking, graphical representation and data logging. An experimental campaign is under development on post-stroke patients with the Kinect-One sensor. Preliminary results on patients with different residual functioning and level of impairment indicate the capability of the whole system in discriminating motor performances.
Conference papers on the topic "Enhancing feedback control Jerk system"
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Nguyen, Huy, Omid Bagherieh, and Roberto Horowitz. "Settling Control of the Triple-Stage Hard Disk Drives Using Robust Output Feedback Model Predictive Control." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9831.
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Track settling control for a hard disk drive with three actuators has been considered. The objective is to settle the read/write head on a specific track by following the minimum jerk trajectory. Robust output feedback model predictive control methodology has been utilized for the control design which can satisfy actuator constraints in the presence of noises and disturbances in the system. The controller is designed based on a low order model of the system and has been applied to a higher order plant in order to consider the model mismatch at high frequencies. Since the settling control generally requires a relatively low frequency control input, simulation result shows that the head can be settled on the desired track with 10 percent of track pitch accuracy while satisfying actuator constraints.
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Abiri, Reza, Joseph McBride, Xiaopeng Zhao, and Yang Jiang. "A Real-Time Brainwave Based Neuro-Feedback System for Cognitive Enhancement." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9855.
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Brain Computer Interface (BCI) provides a pathway to connect the brain to external devices. Neuro-rehabilitation provides advanced means to assist people with movement disorders such as post-stroke patients and those with lost limbs. While much progress has been made in neuro-rehabilitation as assistive devices, few studies had examined mental rehabilitation assisted by BCI such as memory training using neuroenhancement. It should be noted that many patients with physical disabilities also suffer cognitive difficulties. On the other hand, cognitive decline can also be the result of normal aging without brain injury nor diseases. Here, we propose a novel real-time brainwave BCI platform for enhancing human cognitive by designing and employing a personalized neuro-feedback robot. Short-term memory and attention are among the most important cognitive abilities which manifest in many mental diseases. A social robot is integrated into the BCI system to provide feedback based on individual’s brainwaves and memory performance. As a simple scenario of memory task, real-time EEG signals will be monitored during a visual object memory task. Our novel neuro-feedback system has great potential as a neuro-enhancing device for cognitive rehabilitation.
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Nomoto, Takuya, Daisuke Hunakoshi, Toru Watanabe, and Kazuto Seto. "Modeling and Vibration Control of a Flexible Rotor by Using Magnetic Bearings." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48057.
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This paper presents a new modeling method and a control system design procedure for a flexible rotor with many elastic modes using active magnetic bearings. The purpose of our research is to let the rotor rotate passing over the 1st and the 2nd critical speeds caused by flexible modes. To achieve this, it is necessary to control motion and vibration of the flexible rotor simultaneously. The new modeling method named as Extended Reduced Order Physical Model is presented to express its motion and vibration uniformly. By using transfer function of flexible rotor-Active Magnetic Bearings system, we designed a Local Jerk Feedback Control system and conducted stability discrimination with root locus. In order to evaluate this modeling and control method, levitation experimentation is conducted.
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Attia, Tamer, Kevin Kochersberger, John Bird, and Steve C. Southward. "System Identification and Optimal Control of Half-Car Active Suspension System Using a Single Noisy IMU With Position Uncertainty." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5097.
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An active suspension based on Linear Quadratic Gaussian (LQG) optimal controller is an effective system for enhancing the ride comfort and handling characteristics of a vehicle. LQG requires a good plant model for success, and this may be difficult to extract using a single inertial measurement device in the presence of noise. This paper presents a method for estimating the vehicle states by measuring both the vehicle bounce and pitch accelerations using an Inertial Measurement Unit (IMU) with position uncertainty relative to the sprung mass center of gravity. Frequency domain methods are used for System Identification (SysId). The state estimation is based on channel-by-channel model estimation using uncorrelated random excitation which is applied to the front wheels, rear wheels, front actuator, and rear actuator. An anti-aliasing filter eliminates false response harmonics and a Kalman filter is used to estimate the current states of the actual plant and the LQR block for the full-states-feedback controller. The controllers and observer are implemented in simulation using a four degree-of-freedom half car linear model.
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Huynh, Jenny, Han Xuyen Duong, Gaojian Huang, Egbe-Etu Etu, David Quintero, and Lin Jiang. "Modular Tactile End Effector Design for Enhancing Haptic Feedback in Teleoperated Robotic Systems." In ASME 2023 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/imece2023-113969.
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Abstract This paper describes a novel end effector design that combines off-the-shelf sensors and an endoscopic camera to improve haptic perception. By providing realistic force feedback through the tactile end effector, the aim is to enhance the operator’s situational awareness and sense of touch during teleoperations on a laparascopic surgical simulator. Modular tactile end effectors include a load cell, a force resistive sensor (FSR), a linear actuator, an endoscope, a surgical forcep, and custom fixers. The modular base allows users to switch easily between different tools. Through experiments on a teleoperated robotic system, the feasibility of using the modular tactile end effector to differentiate the stiffness of materials when grasping objects was demonstrated. The results highlight the potential of the designed end effector to improve teleoperation and robot control in various applications.
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Liu, Shiguang. "Fuel Cell Inlet Temperature Feedback Control Based on the Internal Model Principle." In SAE 2024 Vehicle Powertrain Diversification Technology Forum. SAE International, 2025. https://doi.org/10.4271/2025-01-7073.
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<div class="section abstract"><div class="htmlview paragraph">The thermal management system of fuel cells poses considerable challenges, particularly due to large time delays and nonlinear behaviors that complicate effective temperature control of the stack. In response to these challenges, this study introduces a novel fuel cell inlet temperature feedback control method based on the internal model principle, designed to enhance control accuracy. Simulations were conducted using MATLAB/Simulink® to evaluate the performance of both Proportional-Integral (PI) and internal model controllers through various tests, including step response and random condition assessments. The results demonstrated that the proposed internal model controller significantly outperformed traditional PID control in both static and dynamic scenarios. Specifically, during step response testing, the maximum temperature overshoot was minimized to just 1.5°C, with a steady-state error of less than 0.5°C. In dynamic performance testing, the inlet temperature exhibited a rapid response, achieving a maximum downward overshoot of 1.8°C while maintaining a steady-state error below 0.5°C. These findings validate the effectiveness of the internal model controller and highlight its potential for more precise parameter tuning. Ultimately, this advancement contributes to improved thermal management in fuel cell systems, ensuring optimal temperature control that is crucial for enhancing both the efficiency and longevity of fuel cells. The study underscores the promising application of this control method in future fuel cell technologies.</div></div>
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Jing, Junchao, Yiqiang Liu, Dongfei li, Botao Zuo, and Weishan Huang. "Research on the Gearshift Control in Dual Motor Hybrid System." In WCX SAE World Congress Experience. SAE International, 2025. https://doi.org/10.4271/2025-01-8512.
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<div class="section abstract"><div class="htmlview paragraph">This paper delineates a shift control approach for a dual motor structure incorporating a drum-type shift lever in a parallel mode, which can be approximately categorized into five stages. In the first stage, the torque of the dual motor and internal combustion engine is interchanged, and the engine side torque is reverted to zero within the capacity range, with the P3 motor compensating for the torque loss on the engine side. In the second stage, the vehicle control unit transmits a request for series connection to the powertrain control module and dispatches a request for the internal combustion engine gear position to be in neutral to the powertrain control module. The powertrain control module enters the sequence for the transition from parallel to series and undertakes the action of unloading the C0 clutch torque. Once the C0 clutch torque is completely disengaged, the actual mode is fed back as parallel, and the actual engine gear position is fed back as neutral. In the third stage, upon the powertrain control module feedback of the actual series connection, the vehicle control unit internally conducts a controller area network delay judgment and subsequently requests a return to parallel, which is dispatched to the powertrain control module. Simultaneously, the new engine gear position is sent to the powertrain control module. The engine gear position at this juncture is the target gear position for parallel connection and is continuously updated. The actual engine gear position fed back by the powertrain control module is neutral, and the gear actuator commences to disengage the gear and engage the new one. In the fourth stage, the powertrain control module continues to provide feedback that the actual internal combustion engine gear position is in the N position, and the powertrain control module governs the gear actuator to engage the new gear until the engagement is accomplished. In the fifth stage, after the powertrain control module finalizes the gear engagement and the gear actuator, it increments the C0 clutch torque and aligns the C0 clutch. The outcomes of the on-vehicle verification substantiate that it assumes a vital role in enhancing driving performance.</div></div>
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Zhang, Yi, Hongyu Zheng, Chuyo Kaku, Changfu Zong, and Yuzhou Zhang. "Research on Control Algorithm for Active Rear Wheel Steering System of Passenger Cars." In WCX SAE World Congress Experience. SAE International, 2025. https://doi.org/10.4271/2025-01-8755.
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<div class="section abstract"><div class="htmlview paragraph">With the continuous development of automobile technology, vehicle handling performance and safety have become increasingly critical research areas. The active rear-wheel (ARW) steering system, a technology that significantly enhances vehicle dynamics and driving stability, has garnered widespread attention. By coordinating front-wheel steering with rear-wheel angle adjustments, ARW improves handling flexibility and stability, particularly during high-speed driving and under extreme conditions. Therefore, designing an efficient ARW control algorithm and optimizing its performance are vital to enhancing a vehicle's overall handling capability.</div><div class="htmlview paragraph">This study delves into the control algorithm design and performance optimization of ARW. First, a comprehensive vehicle dynamics model is constructed to provide a solid theoretical basis for developing control algorithms. Next, optimal control theory is applied to regulate the rear-wheel steering angle, and an LQR control strategy with variable weight coefficients is proposed to address the linear and nonlinear characteristics of tire lateral slip. Finally, comparative simulation verification is conducted using CarSim software, referencing a conventional front wheel steering (2WS) vehicle, a proportional steering control strategy for front and rear wheels, and a proportional feedforward plus yaw rate feedback control strategy. The results demonstrate that under conditions such as angle step inputs, double-lane change maneuvers, and limit double-lane changes on low-friction roads, the vehicle equipped with the LQR control strategy with variable coefficients achieves excellent control performance. The algorithm enhances stability and active safety, meeting all control objectives with objective and quantitative evaluation criteria.</div></div>
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Hewing, Lukas, Dennis Gramlich, Chris Verhoek, et al. "Enhancing the Guidance, Navigation and Control of Autonomous Parafoils using Machine Learning Methods." In ESA 12th International Conference on Guidance Navigation and Control and 9th International Conference on Astrodynamics Tools and Techniques. ESA, 2023. http://dx.doi.org/10.5270/esa-gnc-icatt-2023-135.
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Artificial Intelligence techniques have developed into a transformative force across many industries. Their industrial adaption in aerospace Guidance, Navigation and Control (GNC) systems, however, has been rather limited to date. The “Artificial Intelligence for Guidance, Navigation and Control” (AI4GNC) project led by SENER Aeroespacial and funded by the European Space Agency (ESA) investigates the potential of several machine learning methods to enhance the performance and robustness of an aerospace GNC design. As a specific use case, we consider the descent and landing phase of an autonomous parafoil-guided return vehicle, inspired by the ESA Space Rider whose GNC software is developed by SENER. On this benchmark scenario, we demonstrate how a combination of machine learning methods can be used to significantly improve the performance of a baseline GNC design and gain insight into the system behaviour and its sensitivities. We investigate several complementing technologies at different hierarchical levels in the GNC and its design process and demonstrate the gained advantages on a full-complexity functional simulator, representative of industrial practice. At the intersection of guidance and controls, the project employs data-driven system identification to capture closed-loop system behaviour to serve as a basis for higher-level planning and guidance algorithms. Such models are typically cumbersome to derive from first principles since flight software, including lower-level controllers, actuator saturations and similar effects are part of the loop. In particular, neural networks which have been trained with an efficient deep-learning-based system identification method are used to augment an idealized baseline model which assumes perfect lower-level control. This is shown to effectively reduce residual errors while extending the region of validity compared to alternative linear variants and thereby provide an accurate system description to higher-level planning algorithms. Within the guidance layer, a robust trajectory planning technique is developed based on onboard optimization which can take numerous sources of uncertainty into account, such as wind conditions or uncertain system dynamics. The planning method relies on a novel extension of differential dynamic programming using results from robust control to formulate a sequence of semidefinite programs to find feedback & feedforward policies that efficiently steer the system despite the adversarial action of uncertainties. Extensive evaluations on the functional simulator show a clear hierarchy of achieved performances: (nominal) optimization-based guidance outperforms the baseline solution, while the novel robust variant shows the strongest performance. We furthermore present a developed GNC auto-tuner tool that utilizes Bayesian optimization (BO) to efficiently tune high-level GNC parameters for complex natural language constraints or objectives formulated in terms of temporal logic expressions. The use of Bayesian optimization enables a data-efficient stochastic black-box optimization of several key GNC parameters using a small number of (simulation) experiments. We further demonstrate that it is straightforward to employ the techniques in an antagonistic fashion leading to an effective worst-case-analysis tool. Our results show how such temporal logic-constrained BO can be efficiently used to improve system performance, explore parameter interdependencies and provide valuable insights to support the tuning of complex GNC systems. Finally, all developments are presented in a unified perspective highlighting synergies and sketching a general framework in which AI and data-driven techniques can contribute to the GNC discipline. This particularly highlights the increasingly central position of simulations, not only as a verification and validation tool but rather as an integral part of the GNC design process itself. With this, we envision a viable path forward towards the integration of AI techniques towards industrial practice, and towards realizing its considerable potential for the field.
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Wei, Chen, Yuanhang Chen, Oscar Gabaldon, et al. "Enhancing Offshore Drilling Safety: Identification and Integration of Leading Indicators for Loss of Offshore Well Control." In Offshore Technology Conference. OTC, 2025. https://doi.org/10.4043/35818-ms.
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Abstract In an effort to enhance safety and efficiency in offshore drilling operations, this research addresses critical gaps in the current understanding and application of leading indicators for Loss of Well Control (LOWC) events. Existing indicators often fall short in predicting LOWC events accurately and in a timely manner, due to lack of integration of real-time data which is crucial for proactive risk management. Our study aims to bridge these gaps by identifying robust leading indicators and incorporating them into an advanced real-time detection and response system. The research began with a comprehensive analysis of existing LOWC events to identify the key leading indicators. Employing advanced data analytics techniques, including Data Assimilation (DA), and Change-Point-Bayesian Networks (CP-BN), we have developed an integrated real-time kick detection and state estimation framework and evaluated the effectiveness and reliability of the identified indicators. This evaluation was supported by extensive testing, including real-time and offline simulations, as well as full-scale experiments where operational parameters were adjusted to replicate various drilling scenarios. Several key leading indicators were identified based on this study, such as abnormal pressure fluctuations, unexpected changes in drilling fluid properties, and precursors detected through distributed fiber-optic sensing data. We conducted a series of surveys and engaged with industry professionals to gather practical insights and feedback to further validate these findings and enhance the applicability of our results. The integration of these indicators into the kick-detection and state-estimation framework significantly enhanced early detection capabilities. Experimental testing and assessment of the integrated framework in a full-scale system have demonstrated their operational viability, markedly improved preventive safety measures, and contributed to the reduction of overall system risks. This research represents an advancement in operational safety for offshore drilling operations, facilitating the development of proactive approaches that enable drilling crews to respond more effectively to emerging threats and reduce the likelihood of LOWC incidents. It serves as a foundational study for continued safety enhancements in the oil and gas industry, encouraging the ongoing refinement of safety practices.