Adaptive control of a manipulator with a flexible link

An adaptive controller for a manipulator with one rigid link and one flexible link is presented. The performance and robustness of the controller are demonstrated by numerical simulation results. In the simulations, the manipulator moves in a gravitational field and a finite element model represents the flexible link

Robust Adaptive Self-Organizing Wavelet Fuzzy CMAC Tracking Control for De-icing Robot Manipulator

In this paper, a robust adaptive self-organizing control system based on a novel wavelet fuzzy cerebellar model articulation controller (WFCMAC) is developed for an n-link robot manipulator to achieve the high-precision position tracking. This proposed controller consists of two parts: one is the WFCMAC approach which is implemented to cope with nonlinearities, due to the novel WFCMAC not only incorporates the wavelet decomposition property with fuzzy CMAC fast learning ability but also it will be self-organized; that is, the layers of WFCMAC will grow or prune systematically. Therefore, dimension of WFCMAC can be simplified. The second is the order which is the adaptive robust controller which is designed to achieve robust tracking performance of the system. The adaptive tuning laws of WFCMAC parameters and error estimation of adaptive robust controller are derived through the Lyapunov function so that the stability of the system can be guaranteed. Finally, the simulation and experimental results of novel three-link deicing robot manipulator are applied to verify the effectiveness of the proposed control methodology

Intelligent control of nonlinear systems with actuator saturation using neural networks

Common actuator nonlinearities such as saturation, deadzone, backlash, and hysteresis are unavoidable in practical industrial control systems, such as computer numerical control (CNC) machines, xy-positioning tables, robot manipulators, overhead crane mechanisms, and more. When the actuator nonlinearities exist in control systems, they may exhibit relatively large steady-state tracking error or even oscillations, cause the closed-loop system instability, and degrade the overall system performance. Proportional-derivative (PD) controller has observed limit cycles if the actuator nonlinearity is not compensated well. The problems are particularly exacerbated when the required accuracy is high, as in micropositioning devices. Due to the non-analytic nature of the actuator nonlinear dynamics and the fact that the exact actuator nonlinear functions, namely operation uncertainty, are unknown, the saturation compensation research is a challenging and important topic with both theoretical and practical significance. Adaptive control can accommodate the system modeling, parametric, and environmental structural uncertainties. With the universal approximating property and learning capability of neural network (NN), it is appealing to develop adaptive NN-based saturation compensation scheme without explicit knowledge of actuator saturation nonlinearity. In this dissertation, intelligent anti-windup saturation compensation schemes in several scenarios of nonlinear systems are investigated. The nonlinear systems studied within this dissertation include the general nonlinear system in Brunovsky canonical form, a second order multi-input multi-output (MIMO) nonlinear system such as a robot manipulator, and an underactuated system-flexible robot system. The abovementioned methods assume the full states information is measurable and completely known. During the NN-based control law development, the imposed actuator saturation is assumed to be unknown and treated as the system input disturbance. The schemes that lead to stability, command following and disturbance rejection is rigorously proved, and verified using the nonlinear system models. On-line NN weights tuning law, the overall closed-loop performance, and the boundedness of the NN weights are rigorously derived and guaranteed based on Lyapunov approach. The NN saturation compensator is inserted into a feedforward path. The simulation conducted indicates that the proposed schemes can effectively compensate for the saturation nonlinearity in the presence of system uncertainty

Model-Based Robot Control and Multiprocessor Implementation

Model-based control of robot manipulators has been gaining momentum in recent years. Unfortunately there are very few experimental validations to accompany simulation results and as such majority of conclusions drawn lack the credibility associated with the real control implementation

Two solutions to the adaptive visual servoing problem

Published versio

Advances in PID Control

Since the foundation and up to the current state-of-the-art in control engineering, the problems of PID control steadily attract great attention of numerous researchers and remain inexhaustible source of new ideas for process of control system design and industrial applications. PID control effectiveness is usually caused by the nature of dynamical processes, conditioned that the majority of the industrial dynamical processes are well described by simple dynamic model of the first or second order. The efficacy of PID controllers vastly falls in case of complicated dynamics, nonlinearities, and varying parameters of the plant. This gives a pulse to further researches in the field of PID control. Consequently, the problems of advanced PID control system design methodologies, rules of adaptive PID control, self-tuning procedures, and particularly robustness and transient performance for nonlinear systems, still remain as the areas of the lively interests for many scientists and researchers at the present time. The recent research results presented in this book provide new ideas for improved performance of PID control applications

Nonlinear Control Of Robot Manipulator Using Sliding Mode And Computed Torque Control Technique

The idea of a robot is created based on human and biological nature. The purpose of creating robots is to replace human work that is tiresome, repetitive, or dangerous task in industries or military application. The most extensively used is the manipulation robot system which can be found in manufacturing industries. This type of robot is an arm-link mechanism system, and the control objective is to manipulate material without direct contact by commanding the end-effector motion to achieve the desired operation. For this purpose, the study on the relation of joint-space and Cartesian-space, together with the control system, is essential. The objectives of this research are to study the linear Proportional, Integrator and Derivative (PID) control, nonlinear Computed Torque Control (CTC) and Sliding Mode Control (SMC), and the control algorithm was built using MATLAB Simulink block. The control algorithm of the PID, PIDCTC and SMCTC were implemented into the robot manipulator, where the controller parameters were determined within a prescribed range. The performance of the control system was tested for step response, position tracking and modelled estimation. An additional test for chattering reduction has been carried out for SMCTC with reaching control law. Based on the results, the performance of each of the controllers was compared. The PID control system shows a relatively good performance within the requirements of this study. Also, the PIDCTC produced better results than the PID controller, especially for position tracking. However, when the system were subjected to external disturbance, both controllers were unable to reject the disturbance. The SMCTC is robust towards external disturbance, and has shown the best performance. The implementation of reaching control law not only reduced the settling time, but was also able to eliminate the chattering phenomenon. The effectiveness of SMCTC has been shown by its implementation into a servomotor robot manipulator. Based on the results, the experimental performance has shown that both joints are able to reach the position as desired