scholarly journals Position Tracking of a Pneumatic-Muscle-Driven Rehabilitation Robot by a Single Neuron Tuned PID Controller

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhong ◽  
Yue Zhu ◽  
Chun Zhao ◽  
Zhenfeng Han ◽  
Xin Zhang
Keyword(s):  
Pid Controller ◽  
Single Neuron ◽  
Position Tracking ◽  
Experimental Setup ◽  
Rehabilitation Robot ◽  
Medical Robot ◽  
Pneumatic Muscle ◽  
Pid Algorithm ◽  
Muscle Actuators

Pneumatic muscle actuators (PMAs) own compliant characteristics and are suitable for use in rehabilitation equipment. This paper introduces a rehabilitation robot driven by PMAs devised in the Rehabilitation and Medical Robot Laboratory. Considering high nonlinearities inside PMAs, a single neuron tuned PID controller is carefully designed. Experimental setup is built up and trials are performed. Results demonstrate the proposed advanced PID algorithm can achieve better capacity in position tracking than the conventional PID controller.

scholarly journals AN REHABILITATION ROBOT DRIVEN BY PNEUMATIC ARTIFICIAL MUSCLES

2020 ◽  
Vol 20 (09) ◽  
pp. 2040008 ◽  
Author(s):  
JUN ZHONG ◽  
DONGKAI HE ◽  
CHUN ZHAO ◽  
YUE ZHU ◽  
QIANZHUANG ZHANG
Keyword(s):  
Artificial Muscles ◽  
Driving Mechanism ◽  
Rehabilitation Robot ◽  
Pneumatic Muscle ◽  
Rehabilitation Robots ◽  
Ankle Rehabilitation ◽  
Human Ankle ◽  
Movement Ability ◽  
Motion Characteristics ◽  
Muscle Actuators

Rehabilitation robots are playing an important role in restoring movement ability of hemiplegic patients. However, most of these robots adopt motors as actuators. Considering human body is a flexible organism, rigid motors lack compliance when getting in touch with patients. This paper designs an ankle rehabilitation robot by employing pneumatic muscle actuators which are soft and have similar compliance with biological muscles. Analysis of motion characteristics of human ankle is performed, and relationship between angle and torque of human ankle acquired from experiment is studied. Driving mechanism using pneumatic muscle actuators is addressed carefully and ankle-rehabilitation robot is designed. Then, dynamics of the robot is established and structure optimization of the driving mechanism is performed. Consequently, prototype is manufactured and assembled.

BLDC Motor Control System Based on Quadratic Single Neuron Adaptive PID Algorithm

2014 ◽  
Vol 6 (1) ◽  
pp. 12-25
Author(s):  
Xiaoyuan Wang ◽  
Tao Fu ◽  
Xiaoguang Wang
Keyword(s):  
Pid Controller ◽  
Single Neuron ◽  
High Efficiency ◽  
Control Method ◽  
Current Control ◽  
Operating Conditions ◽  
Regulation System ◽  
Bldc Motor ◽  
Motor Speed ◽  
Pid Algorithm

Brushless DC (BLDC) motors are widely used for many industrial applications because of their high efficiency, high torque and low volume. In view of the problem that the current control method of speed regulation system of BLDC motor has poor control effect caused by fixed parameters of PID controller, an adaptive PID algorithm with quadratic single neuron (QSN) was designed. Quadratic performance index was introduced in adjustment of weight coefficients; expected optimization effect was gotten by calculating control law. QSN adaptive PID controller can change its parameters online when operating conditions are changed, it can also change its control characteristic automatically. Matlab simulations and experiment results showed that the proposed approach has less overshoot, faster response, stronger ability of anti-disturbance, the results also showed more effectiveness and efficiency than the conventional PID model in motor speed control.

Design of a Simple Experimental Setup for P-I-D Control Testing

2020 ◽  
Author(s):  
Zeki Okan Ilhan ◽  
William Loveland ◽  
Justiz Baker
Keyword(s):  
Pid Controller ◽  
Position Control ◽  
Experimental Setup ◽  
State University ◽  
Level Control ◽  
Pid Algorithm ◽  
Ping Pong ◽  
3D Printed ◽  
Accurate Position ◽  
System Transfer Function

Abstract This work aims to demonstrate the use of a simple experimental setup for accurate position control, which will be used to supplement the senior level “Control Systems” class taught in McCoy School of Engineering at Midwestern State University. The experimental setup is an unstable, doubleintegrator system, which consists of a ping-pong ball rolling on a pivoted beam. The control task is to stabilize the ball at the center of the beam by systematically changing the angle of rotation of the beam through the servomotor. The experimental setup is built out of 3D-printed parts, and simple electronics are used for controls. A control-oriented dynamic model is first obtained based on the standard Lagrangian approach and the model is linearized to simplify the control design. Proportional Integral Derivative (PID) controller is then designed based on the system transfer function, and the performance of the PID controller is tested in closed-loop numerical simulations in MATLAB-SIMULINK environment. Finally, the proposed PID algorithm is implemented in the actual setup using the ARDUINO microcontroller platform. Performance of the PID controller is discussed based on the initial results and possible improvement areas are addressed.

Position Tracking System of Filling Machine Based on Compound Control Strategy

2013 ◽  
Vol 380-384 ◽  
pp. 321-324
Author(s):  
Zhi Qiang Wei ◽  
Dan Jin
Keyword(s):  
Control Strategy ◽  
Pid Controller ◽  
Single Neuron ◽  
Tracking System ◽  
Dynamic State ◽  
Position Tracking ◽  
Compound Control ◽  
Good Ability ◽  
On Line ◽  
Single Neuron Pid

In view of the complexity and periodic motion of automatic filling machine, a novel compound control strategy based on single neuron PID model reference adaptive control and repetitive control is proposed. Diagonal recurrent neural network (DRNN) is used as on-line identifier of system for the single neuron PID controller to adjust its weights and PID parameters by self-learning and self-adapting. The dynamic state performance can be improved by adaptive PID controller based on DRNN on-line Identification and the steady state performance is improved by modified repetitive controller. Simulation results show that the control system has good ability of restraining disturbances and high position tracking precision and good robustness.

Sliding Mode Control of an Exoskeleton Gait Rehabilitation Robot Driven by Pneumatic Muscle Actuators

2015 ◽  
Author(s):  
Jinghui Cao ◽  
Sheng Quan Xie ◽  
Andrew McDaid ◽  
Raj Das
Keyword(s):  
Knee Joint ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Human Gait ◽  
Gait Rehabilitation ◽  
Sliding Mode Controller ◽  
Rehabilitation Robot ◽  
Single Input Single Output ◽  
Pneumatic Muscle ◽  
Muscle Actuators

This paper firstly summarizes a newly developed knee joint mechanism of a gait rehabilitation robot, as well as a modified dynamics model for pneumatic muscle actuators (PMAs). The major sections focus on the development of single-input-single-output sliding mode trajectory tracking controller for the knee mechanism. The sliding mode controller takes the models of the whole system, which include the pneumatic flow dynamics of the analogue valves and PMAs, dynamic model of the PMAs and dynamics of the mechanism, into account. It controls the voltage applied to the valves to track desired angular trajectories of the knee joint. The preliminary experiments on the sliding mode controller have been conducted and the results have indicated that the knee mechanism’s successful tracking of sinusoidal waves with frequencies and magnitudes closed to actual human gait. Currently, the researchers are working on the development of multi-inputs-multi-outputs control of the mechanism for both trajectory tracking and compliance adjustments.

scholarly journals Extended-State-Observer-Based Super Twisting Control for Pneumatic Muscle Actuators

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 35
Author(s):  
Yu Cao ◽  
Zhongzheng Fu ◽  
Mengshi Zhang ◽  
Jian Huang
Keyword(s):  
Control Method ◽  
Experimental Studies ◽  
State Observer ◽  
Extended State Observer ◽  
Extended State ◽  
Pneumatic Muscle ◽  
System States ◽  
The Stability ◽  
Twisting Control ◽  
Muscle Actuators

This paper presents a tracking control method for pneumatic muscle actuators (PMAs). Considering that the PMA platform only feedbacks position, and the velocity and disturbances cannot be observed directly, we use the extended-state-observer (ESO) for simultaneously estimating the system states and disturbances by using measurable variables. Integrated with the ESO, a super twisting controller (STC) is design based on estimated states to realize the high-precision tracking. According to the Lyapunov theorem, the stability of the closed-loop system is ensured. Simulation and experimental studies are conducted, and the results show the convergence of the ESO and the effectiveness of the proposed method.

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