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Fuzzy Self-Tuning PID Control of Hydrogen-Driven pneumatic Artificial muscle Actuator

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Journal of Bionic Engineering 2013年第3期10卷 329-340页

作者： Thanana Nuchkrua Thananchai Leephakpreeda School of Manufacturing Systems and Mechanical Engineering Sirindhorn International Institute of Technology Thammasat University Pathum Thani 12121 Thailand

In this paper, a fuzzy self-tuning Proportional-Integral-Derivative （PID） control of hydrogen-driven pneumatic Artificial muscle （PAM） actuator is presented. With a conventional PID control, non-linear thermodynamics of the hydrogen-driven PAM actuator still highly affects the mechanical actuations itself, causing deyiation of desired tasks. The fuzzy self-tuning PID con- troller is systematically developed so as to achieve dynamic performance targets of the hydrogen-driven PAM actuator. The fuzzy rules based on desired characteristics of closed-loop control are designed to finely tune the PID gains of the controller under different operating conditions. The empirical models and properties of the hydrogen-driven PAM actuator are used as a genuine representation of mechanical actuations. A mass-spring-damper system is applied to the hydrogen-driven PAM actuator as a typical mechanical load during actuations. The results of the implementation show that the viability of the proposed method in actuating the hydrogen-driven PAM under mechanical loads is close to desired oerformance.

关键词： pneumatic muscle metal hydride thermoelectric module PID control fuzzy tuning

Hybrid Dynamic Neural Network and PID Control of pneumatic Artificial muscle Using the PSO Algorithm

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International Journal of Automation and computing 2020年第3期17卷 428-438页

作者： Mahdi Chavoshian Mostafa Taghizadeh Mahmood Mazare School of Mechanical Engineering Sh.Beheshti UniversityTehran 16589-53571Iran

pneumatic artificial muscles(PAM)have been recently considered as a prominent challenge regarding pneumatic actuators specifically for rehabilitation and medical *** accomplishing accurate control of the PAM is comparatively complicated due to time-varying behavior,elasticity and ambiguous characteristics,a high performance and efficient control approach should be *** of the mentioned challenges,limited course length is another predicament with the PAM *** this regard,this paper proposes a new hybrid dynamic neural network(DNN)and proportional integral derivative(PID)controller for the position of the *** order to enhance the proficiency of the controller,the problem under study is designed in the form of an optimization *** the potential of particle swarm optimization,it has been applied to optimally tune the PID-DNN *** verify the performance of the proposed controller,it has been implemented on a real-time system and compared to a conventional sliding mode *** and experimental results show the effectiveness of the proposed controller in tracking the reference signals in the entire course of the PAM.

关键词： Dynamic neural network(DNN)control hybrid control pneumatic muscle particle swarm optimization sliding mode control

Design and Dynamic Model of a Frog-inspired Swimming Robot Powered by pneumatic muscles

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Chinese Journal of Mechanical Engineering 2017年第5期30卷 1123-1132页

作者： Ji-Zhuang Fan Wei Zhang Peng-Cheng Kong He-Gao Cai Gang-Feng Liu State Key Laboratory of Robotics and System Harbin Institute of Technology

pneumatic muscles with similar characteristics to biological muscles have been widely used in robots, and thus are promising drivers for frog inspired robots. How- ever, the application and nonlinearity of the pneumatic system limit the advance. On the basis of the swimming mechanism of the frog, a frog-inspired robot based on pneumatic muscles is developed. To realize the indepen- dent tasks by the robot, a pneumatic system with internal chambers, micro air pump, and valves is implemented. The micro pump is used to maintain the pressure difference between the source and exhaust chambers. The pneumatic muscles are controlled by high-speed switch valves which can reduce the robot cost, volume, and mass. A dynamic model of the pneumatic system is established for the sim- ulation to estimate the system, including the chamber, muscle, and pneumatic circuit models. The robot design is verified by the robot swimming experiments and the dynamic model is verified through the experiments and simulations of the pneumatic system. The simulation results are compared to analyze the functions of the source pressure, internal volume of the muscle, and circuit flow rate which is proved the main factor that limits the response of muscle pressure. The proposed research provides the application of the pneumatic muscles in the frog inspired robot and the pneumatic model to study muscle controller.

关键词： Frog-inspired robot pneumatic muscle High-speed switch valve pneumatic model

Pressure observer based adaptive robust trajectory tracking control of a parallel manipulator driven by pneumatic muscles

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第12期8卷 1928-1937页

作者： ZHU Xiao-cong TAO Guo-liang CAO Jian The State Key Laboratory of Fluid Power Transmission and Control Zhejiang University Hangzhou 310027 China

This paper presents a pressure observer based adaptive robust controller (POARC) for posture trajectory tracking of a parallel manipulator driven by three pneumatic muscles without pressure sensors. Due to model errors of the static forces and friction forces of pneumatic muscles, simplified average flow rate characteristics of valves, unknown disturbances of entire system, and unmeasured pressures, there exist rather severe parametric uncertainties, nonlinear uncertainties and dynamic uncertainties in modeling of the parallel manipulator. A nonlinear pressure observer is constructed to estimate unknown pressures on the basis of a single-input-single-output (SISO) decoupling model that is simplified from the actual multiple-input-multiple-output (MIMO) coupling model of the parallel manipulator. Then, an adaptive robust controller integrated with the pressure observer is developed to accomplish high precision posture trajectory tracking of the parallel manipulator. The experimental results indicate that the system with the proposed POARC not only achieves good control accuracy and smooth movement but also maintains robustness to disturbances.

关键词： pneumatic muscle Parallel manipulator Pressure observer Adaptive robust control Trajectory tracking

PARAMETRIC IDENTIFICATION OF PARALLEL MANIPULATOR WITH REDUNDANCY DRIVEN BY pneumatic muscleS

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Chinese Journal of Mechanical Engineering 2008年第1期21卷 66-71页

作者： ZHU Xiaocong TAO Guoliang CAO Jian State Key Laboratory of Fluid Power Transmission and Control Zhejiang University Hangzhou 310027 China

A new parameter estimation algorithm is proposed for parametric identification of a parallel manipulator driven by pneumatic muscles with redundancy. Due to the special physical properties of the parallel manipulator studied, the regression model for parametric identification is characterized by multieollinearity, which will result in unreliable and inaccurate parameter estimations with large eovarianee if the conventional parameter estimation algorithm based on single error minimizing criterion is used. To improve the quality of parameter estimation and achieve high precise posture trajectory tracking control of the parallel manipulator, a new parameter estimation algorithm based on composite error minimizing criterion is developed in need of theoretical contractive forces of pneumatic muscles. The experimental results indicate that the proposed algorithm integrated with adaptive robust control could provide reliable parametric identification and greatly enhance the control accuracy in the trajectory tracking control of the parallel manipulator, and that the variation of known theoretical contractive forces of pneumatic muscles has slight influence on the control performance.

关键词： pneumatic muscle Parallel manipulator Redundancy Parameter estimation

Cross-coupling integral adaptive robust posture control of a pneumatic parallel platform

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Journal of Central South University 2016年第8期23卷 2036-2047页

作者：左赫陶国良 State Key Laboratory of Fluid Power Transmission and Control Zhejiang University

A pneumatic parallel platform driven by an air cylinder and three circumambient pneumatic muscles was considered. Firstly, a mathematical model of the pneumatic servo system was developed for the MIMO nonlinear model-based controller designed. The pneumatic muscles were controlled by three proportional position valves, and the air cylinder was controlled by a proportional pressure valve. As the forward kinematics of this structure had no analytical solution, the control strategy should be designed in joint space. A cross-coupling integral adaptive robust controller(CCIARC) which combined cross-coupling control strategy and traditional adaptive robust control(ARC) theory was developed by back-stepping method to accomplish trajectory tracking control of the parallel platform. The cross-coupling part of the controller stabilized the length error in joint space as well as the synchronization error, and the adaptive robust control part attenuated the adverse effects of modelling error and disturbance. The force character of the pneumatic muscles was difficult to model precisely, so the on-line recursive least square estimation(RLSE) method was employed to modify the model compensation. The projector mapping method was used to condition the RLSE algorithm to bound the parameters estimated. An integral feedback part was added to the traditional robust function to reduce the negative influence of the slow time-varying characteristic of pneumatic muscles and enhance the ability of trajectory tracking. The stability of the controller designed was proved through Laypunov's theory. Various contrast controllers were designed to testify the newly designed components of the CCIARC. Extensive experiments were conducted to illustrate the performance of the controller.

关键词： servo-pneumatic system pneumatic muscle parallel platform cross coupling adaptive robust control parameter estimation

RLSESN-based PID adaptive control for a novel wearable rehabilitation robotic hand driven by PM-TS actuators

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International Journal of Intelligent Computing and Cybernetics 2012年第1期5卷 91-110页

作者： Jun Wu Jian Huang Yongji Wang Kexin Xing Key Laboratory of Image Processing and Intelligent Control Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhanChina

Purpose-The purpose of this paper is to develop a novel wearable rehabilitation robotic hand driven by pneumatic muscle-Torsion Spring(PM-TS)for finger *** has complex nonlinear dynamics,which makes PM modelling *** realize high-accurate tracking for the robotic hand,an Echo State Network(ESN)-based PID adaptive controller is proposed,even though the plant model is ***/methodology/approach-To drive a single joint of rehabilitation robotic hand,the paper proposes a new PM-TS actuator comprising a pneumatic muscle(PM)and a Torsion Spring(TS).Based on the novel actuator,a wearable robotic hand is *** employing the model-free approximation capability of ESN,the RLSESN based PID adaptive controller is presented for improving the trajectory tracking performance of the rehabilitation robotic *** ESN together with Recursive Least Square(RLS)is called a RLSESN,where the ESN output weight matrix is updated by the online RLS learning ***–Practical experiments demonstrate the validity of the PM-TS actuator and indicate that the performance of the RLSESN based PID adaptive controller is better than that of the conventional PID *** addition,they also verify the effectiveness of the proposed rehabilitation robotic ***/value–A new PM-TS actuator configuration that uses a PM and a torsion spring for bi-directional movement of joint is *** utilizing the new PM-TS actuator,a novel wearable rehabilitation robotic hand for finger therapy is *** on the unknown plant model,the RLSESN_PID controller is proposed to attain satisfactory performance.

关键词： Robotics Control systems Controllers Actuators Adaptive system theory Wearable rehabilitation robotic hand Echo state network pneumatic muscle Trajectory tracking