Adaptive Robust Control for a Class of Nonlinear Uncertain System With Unknown Input Backlash

2009 ◽  
Author(s):  
Jian Guo ◽  
Bin Yao ◽  
Jun Jiang ◽  
Qingwei Chen
Keyword(s):  
Robust Control ◽  
Nonlinear Dynamic System ◽  
Uncertain System ◽  
Robust Adaptive Control ◽  
Adaptive Robust Control ◽  
Unknown Input ◽  
Uncertain Disturbances ◽  
On Line ◽  
Parameterized Model ◽  
Robust Adaptive

An adaptive robust control (ARC) algorithm is developed for a class of nonlinear dynamic system with unknown input backlash, parametric uncertainties and uncertain disturbances. Due to the non-smooth dynamic nonlinear nature of backlash, existing robust adaptive control methods mainly focus on using approximate inversion of backlash by on-line parameter adaptation. But experimental results show that a linear controller alone can perform better than a controller including the selected backlash inverter with a correctly estimated or overestimated backlash gap. Unlike many existing control schemes, the backlash inverse is not constructed in this paper. A new linearly parameterized model for backlash is presented. The backlash nonlinearity is linearly parameterized globally with bounded model error. The proposed adaptive robust control law ensure that all closed-loop signals are bounded and achieves the tracking within the desired precision. Simulations results illustrate the performance of the ARC.

Parameter estimation and upper bounding adaptation in adaptive-robust control approaches for trajectory control of robots

Robotica ◽  
2002 ◽  
Vol 20 (6) ◽  
pp. 653-660 ◽  
Author(s):  
Ibrahim Uzmay ◽  
Recep Burkan
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Robust Control ◽  
Uncertain System ◽  
Adaptive Controller ◽  
Robust Adaptive Control ◽  
Adaptive Robust Control ◽  
Lyapunov Theory ◽  
Control Law ◽  
Control Input

In this paper a new robust adaptive control law for n-link robot manipulators with parametic uncertainties is derived using the Lyapunov theory thus guaranteed the stability of an uncertain system. The novelty of the adaptive robust control algorithm is that manipulator parameters and adaptive upper bounding functions are estimated to control the system properly, and the adaptive robust control law is also updated as an exponential function of manipulator kinematics, inertia parameters and tracking errors. The proposed adaptive control input includes a parameter estimation law as an adaptive controller and an additional control input vector as a robust controller. The developed approach has the advantages of both adaptive and robust control laws, without their discolour tags.

Indirect Neural Network Adaptive Robust Control of Linear Motor Drive System

2002 ◽  
Author(s):  
J. Q. Gong ◽  
Bin Yao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Tracking Error ◽  
Linear Motor ◽  
Adaptive Robust Control ◽  
Operating Conditions ◽  
Tracking Performance ◽  
Motor Drive ◽  
Output Tracking ◽  
On Line

In this paper, an indirect neural network adaptive robust control (INNARC) scheme is developed for the precision motion control of linear motor drive systems. The proposed INNARC achieves not only good output tracking performance but also excellent identifications of unknown nonlinear forces in system for secondary purposes such as prognostics and machine health monitoring. Such dual objectives are accomplished through the complete separation of unknown nonlinearity estimation via neural networks and the design of baseline adaptive robust control (ARC) law for output tracking performance. Specifically, recurrent neural network (NN) structure with NN weights tuned on-line is employed to approximate various unknown nonlinear forces of the system having unknown forms to adapt to various operating conditions. The design is actual system dynamics based, which makes the resulting on-line weight tuning law much more robust and accurate than those in the tracking error dynamics based direct NNARC designs in implementation. With a controlled learning process achieved through projection type weights adaptation laws, certain robust control terms are constructed to attenuate the effect of possibly large transient modelling error for a theoretically guaranteed robust output tracking performance in general. Experimental results are obtained to verify the effectiveness of the proposed INNARC strategy. For example, for a typical point-to-point movement, with a measurement resolution level of ±1μm, the output tracking error during the entire execution period is within ±5μm and mainly stays within ±2μm showing excellent output tracking performance. At the same time, the outputs of NNs approximate the unknown forces very well allowing the estimates to be used for secondary purposes such as prognostics.

scholarly journals A Tutorial on Robust Control, Adaptive Control and Robust Adaptive Control—Application to Robotic Manipulators

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 49
Author(s):  
Bin Wei
Keyword(s):  
Adaptive Control ◽  
Robust Control ◽  
Robotic Manipulators ◽  
Joint Space ◽  
Robust Adaptive Control ◽  
Control Techniques ◽  
Operational Space ◽  
Advanced Control ◽  
Robust Adaptive ◽  
Control Application

A tutorial on robust control, adaptive control, robust adaptive control and adaptive control of robotic manipulators is presented in a systematic manner. Some limitations of the above methods are also illustrated. The relationships between the robust control, adaptive control and robust adaptive control are demonstrated. Basic information on the joint space control, operational space control and force control is also given. This tutorial summarizes the most advanced control techniques currently in use in a very simple manner, and applies to robotic manipulators, which can provide an informative guideline for students who have little knowledge of controls or who want to understand the adaptive control of robotics in a systematic way.

Switching Control With Learning for a Class of Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 448-450
Author(s):  
X. Zhang ◽  
S. S. Nair
Keyword(s):  
Adaptive Learning ◽  
Control Strategy ◽  
Controller Design ◽  
Switching Control ◽  
Robust Adaptive Control ◽  
Control Effort ◽  
On Line ◽  
Robust Adaptive ◽  
Adaptive Control Strategy ◽  
Stable Learning

Analytical details are developed for a robust adaptive control strategy that combines switching control and on-line adaptive learning, for a class of nonlinear systems. The condition for stable learning is derived, guidelines for design parameter selection are provided, and the tradeoff between performance and chattering control effort is examined. The results of the study are summarized in the form of a constructive procedure for controller design for the class of systems.

scholarly journals ROBUST ADAPTIVE CONTROL USING REINFORCEMENT LEARNING FOR NONLINEAR SYSTEM WITH INPUT CONSTRAINTS

2009 ◽  
Vol 12 (16) ◽  
pp. 5-18
Author(s):  
Luy Tan Nguyen ◽  
Thanh Thien Nguyen ◽  
Ha Thi Phuong Nguyen
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Robust Stabilization ◽  
Robust Adaptive Control ◽  
Novel Approach ◽  
On Line ◽  
Robust Adaptive ◽  
Associative Law ◽  
Set Up ◽  
And Control

This paper proposes a novel approach to design a controller in discrete time for the class of uncertain nonlinear systems in the presence of magnitude constrains of control signal which are treated as the saturation nonlinearity. A associative law between reinforcement learning algorithm based on adaptive NRBF neural networks and the theory of robust control Ho is set up in a novel control structure, in which the proposed controller allows learning and control on-line to compensate multiple uncertain nonlinearities as well as minimizing both the H. tracking performance index function and the unknown nonlinear dynamic approximation errors. The novel theorem of robust stabilization of the closed-loop system is declared and proved. Simulation results verify the theoretical analysis.

Sign in / Sign up

Export Citation Format

Share Document