scholarly journals Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization

Machines ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Jiaqi Liu ◽  
Hongbin Fang ◽  
Jian Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Adaptive Control ◽  
Lower Extremity ◽  
Pid Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Medical Rehabilitation ◽  
Practical Case ◽  
Tracking Accuracy ◽  
Swarm Optimization

Robotic exoskeletons have great potential in the medical rehabilitation and augmentation of human performance in a variety of tasks. Proposing effective and adaptive control strategies is one of the most challenging issues for exoskeleton systems to work interactively with the user in dynamic environments and variable tasks. This research, therefore, aims to advance the state of the art of the exoskeleton adaptive control by integrating the excellent search capability of metaheuristic algorithms with the PID feedback mechanism. Specifically, this paper proposes an online adaptive PID controller for a multi-joint lower extremity exoskeleton system by making use of the particle swarm optimization (PSO) algorithm. Significant improvements, including a ‘leaving and re-searching mechanism’, are introduced into the PSO algorithm for better and faster update of the solution and to prevent premature convergence. In this research, a 9-DOF lower extremity exoskeleton with seven controllable joints is adopted as a test-bench, whose first-principle dynamic model is developed, which includes as many uncertain factors as possible for generality, including human–exoskeleton interactions, environmental forces, and joint unilateral constraint forces. Based upon this, to illustrate the effectiveness of the proposed controller, the human–exoskeleton coupled system is simulated in four characteristic scenarios, in which the following factors are considered: exoskeleton parameter perturbations, human effects, walking terrain switches, and walking speed variations. The results indicate that the proposed controller is superior to the standard PSO algorithm and the conventional PID controller in achieving rapid convergence, suppressing the undesired chattering of PID gains, adaptively adjusting PID coefficients when internal or external disturbances are encountered, and improving tracking accuracy in both position and velocity. We also demonstrate that the proposed controller could be used to switch the working mode of the exoskeleton for either performance or an energy-saving consideration. Overall, aiming at a multi-joint lower extremity exoskeleton system, this research proposes a PSO-based online adaptive PID controller that can be easily implemented in applications. Through rich and practical case studies, the excellent anti-interference capability and environment/task adaptivity of the controller are exemplified.

scholarly journals Comparison of Particle Swarm Optimization and Ziegler Nichols Techniques

2020 ◽  
pp. 184-188
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Optimization Technique ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Computational Method ◽  
Swarm Optimization ◽  
Tuning Method

Two tuning techniques namely: Particle Swarm Optimization (PSO) and Ziegler Nichols (ZN) technique are compared. PSO is an optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution about a given measure of quality. Ziegler Nichols tuning method is a heuristic method of tuning a PID controller. The ZN close loop tuning is performed by setting the I (integral) and D (derivative) gains to zero and increasing proportional gain to obtain sustained oscillations. The DC Motor is represented by second order transfer function is used as a plant, which is controlled using PID controller. The PID controller parameters are chosen by tuning the controller using PSO algorithm and ZN method. The response of the system to unit step input is plotted and performance measures are evaluated for comparing PSO algorithm and ZN technique. Here we have compared the two tuning methods based upon the settling time (Ts), peak overshoot (Mp) and the two performance indices namely Integral square error (ISE) and Integral Absolute error (IAE).

scholarly journals Solar System Array by Fuzzy Logic based on MPPT Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 1132-1139
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Solar System ◽  
Pid Controller ◽  
Performance Enhancement ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point

In recent times a huge attention has been given on development of proper planning In this paper we present a top dimension perspective on forefront status of Closed circle ID system the use of PID Controller from explicit creators. The proportional– integral– subsidiary (PID) controller is the most extreme comprehensively ordinary controller inside the business bundles, specifically in strategy enterprises in light of fabulous expense to profit proportion. In this paper we focus on MPPT based solar system performance enhancement by use of fuzzy logic controller’s designs optimized by particle swarm optimization (PSO). We have described about different latest A.I. techniques that has been hybrid with fuzzy logic for improving PV array based solar plants performance in recent time. The artificial intelligence technique applied in this work is the Particle Swarm Optimization (PSO) algorithm and is used to optimize the membership functions for maximum power point tracking rule set of the FLC. By using PSO algorithm, the optimized FLC is able to maximize energy to the system loads while also maintaining a higher stability and speed as compared to P& O based MPPT algorithm

Landsman Converter Based Particle Swarm Optimization Technique

2017 ◽  
Vol 8 (3) ◽  
pp. 619
Author(s):  
Ramesh P. ◽  
V. Mathivanan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Control Technique ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Comparative Results

This paper proposes a novel control technique for landsman converter using particle swarm optimization. The controller parameters are optimized by pso algorithm,the proposed algorithm is compared with pid controller and the comparative results are presented. Simulation results shows the dynamic performance of pso controller. landsman converter reduction in output voltage ripple in the order of mV along with reduced settling time as compared to the conventional pid controller . The simulated results are executed in MATLAB/SIMULINK.

Model-Free Adaptive Control Based on Particle Swarm Optimization Algorithm for Water Bath Drawing Process of Carbon Fiber

2013 ◽  
Vol 303-306 ◽  
pp. 1180-1184
Author(s):  
Jian Ren ◽  
Yong Sheng Ding ◽  
Kuang Rong Hao
Keyword(s):  
Particle Swarm Optimization ◽  
Adaptive Control ◽  
Carbon Fiber ◽  
Mimo System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Water Bath ◽  
Swarm Optimization ◽  
Fiber Production ◽  
Model Free

The water bath drawing slot in carbon fiber production is a MIMO system with coupling and time delay. It is difficult for a traditional control scheme to realize stable control on the water bath drawing slot. In this paper, a model-free adaptive control based on particle swarm optimization (PSO) algorithm was designed with few parameters, convenient control, and without decoupling. The PSO algorithm was used to adjust MFAC controller parameters online at each sampling time. The proposed method was applied to the liquid-level-concentration control in the water bath drawing slot of carbon fiber production. The control method has rapid response time, good decoupling and performance by comparing with model-free adaptive control and traditional PID control.

scholarly journals Performance of PID Controller Tuning based on Particle Swarm Optimization and Firefly Algorithm

2019 ◽  
Vol 8 (3S2) ◽  
pp. 225-230 ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Firefly Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Transient Responses ◽  
Swarm Optimization ◽  
Pid Controller Tuning ◽  
Tank System

This paper shows the study of tuning the Proportional-Integral-Derivatives (PID) in the application of coupled tank system. The controller was tuned by using an optimization technique which is a Firefly Algorithm (FA) and a Particle Swarm Optimization (PSO) Algorithm. Both FA and PSO performance were evaluated by using performance index of Integral Time Square Error (ITSE). The systems response of FA and PSO were gathered and compared in term of transient responses, ITSE and standard deviation by considering the system condition of with and without a disturbance. The simulation is conducted by using MATLAB software. The result shows that the FA giving a better system performance compared to PSO in term of overall transient responses.

scholarly journals PID Controller Design for Cruise Control System using Particle Swarm Optimization

2017 ◽  
Vol 43 (2) ◽  
pp. 30-35
Author(s):  
Ahmed Abdulnabi
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Controller Design ◽  
Robustness Analysis ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Peak Time ◽  
Cruise Control ◽  
Swarm Optimization ◽  
Best Response

This paper presents a design of a Proportional-Integral-Derivative (PID) controller for automobile cruise controlsystem. The parameters of the PID controller, which are the proportional ( ), derivative ( ) , and integrator ( ), have beenselected using Particle Swarm Optimization (PSO) algorithm. In this study, the overall system performance has beencompared with other predesigned controllers (conventional PID, Fuzzy logic PID, state space, and Genetic algorithm basedPID controller). The simulation result illustrates that PSO based PID controller gives the best response in terms of settlingtime, rise time, peak time, and maximum overshot. The robustness analysis shows that the system is robust despite thedeviations in some of the system parameters.

OPTIMAL TUNING OF A PID CONTROLLER FOR EMDAP-CVT USING PARTICLE SWARM OPTIMIZATION

2015 ◽  
Vol 75 (11) ◽  
Author(s):  
M. Azwarie Mat Dzahir ◽  
Mohamed Hussein ◽  
Bambang Supriyo ◽  
Kamarul Baharin Tawi ◽  
Mohd Shafiek Yaakob ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Controlled System ◽  
Optimal Tuning ◽  
Proportional Integral ◽  
Variable Transmission

This paper looked into optimal tuning of a Proportional-Integral-Derivative (PID) controller used in Electro-mechanical Dual Acting Pulley Continuously Variable Transmission (EMDAP-CVT) system for controlling the output obtained, and hence, to minimize the integral of absolute errors (IAE). The main objective was to obtain a stable, robust, and controlled system by tuning the PID controller by using Particle Swarm Optimization (PSO) algorithm. The incurred value was compared with the traditional tuning techniques like Ziegler-Nichols and it had been proven better. Hence, the results established that tuning the PID controller using PSO technique offered less overshoot, a less sluggish system, and reduced IAE.

Application of Particle Swarm Optimization in Design of PID Controller for AVR System

2013 ◽  
Vol 2 (3) ◽  
pp. 1-17 ◽  
Author(s):  
H. F. Abu-Seada ◽  
W. M. Mansor ◽  
F. M. Bendary ◽  
A. A. Emery ◽  
M. A. Moustafa Hassan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Stability Margin ◽  
Pso Algorithm ◽  
Step Response ◽  
Voltage Response ◽  
Swarm Optimization ◽  
Avr System ◽  
Terminal Voltage

This paper presents a method to get the optimal tuning of Proportional Integral Derivative (PID) controller parameters for an AVR system of a synchronous generator using Particle Swarm Optimization (PSO) algorithm. The AVR is not initially robust to variations of the power system parameters. Therefore, it was necessary to use PID controller to increase the stability margin and to improve performance of the system. Fast tuning of optimum (PID) controller parameter yield high quality solution. New criteria for time domain performance evaluation was defined. Simulation for comparison between the proposed method and Ziegler-Nichols method is done. The proposed method was indeed more efficient also. The terminal voltage step response for AVR model will be discussed in different cases and the effect of adding rate feed back stabilizer to the model on the terminal voltage response. Then the rate feedback will be compared with the proposed PID controller based on use of (PSO) method to find its coefficients. Different simulation results are presented and discussed.

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