Robust control of robot manipulators with torque saturation using fuzzy logic

Robotica ◽  
2001 ◽  
Vol 19 (6) ◽  
pp. 631-639 ◽  
Author(s):  
Hyeung-Sik Choi
Keyword(s):  
Fuzzy Logic ◽  
Robust Control ◽  
Trajectory Tracking ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Uncertain System ◽  
Tracking Errors ◽  
Estimation Errors ◽  
System Parameters ◽  
Control Scheme

Robot manipulators, which are nonlinear structures and have uncertain system parameters, are complex dynamically when operated in an unknown environment. To compensate for estimate errors of the uncertain system parameters and to accomplish the desired trajectory tracking, nonlinear robust controllers are appropriate. However, when estimation errors or tracking errors are large, they require large input torques, which may not be satisfied due to torque limits of actuators such as driving motors. As a result, their stability cannot be guaranteed. In this paper, a new robust control scheme is presented to solve stability problems and to achieve fast trajectory tracking of uncertain robot manipulators in the presence of torque limits. By using fuzzy logic, new desired trajectories which can be reduced are generated based on the initial desired trajectory, and torques of the robust controller are regulated so as to not exceed torque limits. Numerical examples are shown to validate the proposed controller using an uncertain two degree-of-freedom underwater robot manipulator.

Trajectory tracking control of robot manipulators using a neural-network-based torque compensator

1998 ◽  
Vol 212 (5) ◽  
pp. 361-372 ◽  
Author(s):  
Q Li ◽  
S K Tso ◽  
W J Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
High Performance ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Lyapunov Stability Theory ◽  
System Stability ◽  
Trajectory Tracking Control ◽  
Modelling Uncertainties

In this paper, an adaptive neural-network-based torque compensator is developed for the trajectory-tracking control of robot manipulators. The overall control structure employs a classical non-linear decoupling controller for actuating torque computation based on an approximated robot dynamic model. To suppress the effects of uncertainties associated with the estimated model, a supplementary neural network algorithm is developed to generate compensation torques. The weight adaptation rule for this neuro-compensator is derived on the basis of the Lyapunov stability theory. Both global system stability and the error convergence can then be guaranteed. Simulation studies on a two-link robot manipulator demonstrate that high performance of the proposed control algorithm could be achieved under severe modelling uncertainties.

scholarly journals Neural Networks-based Adaptive State Feedback Control of Robot Manipulators

2007 ◽  
Vol 2 (4) ◽  
pp. 328 ◽  
Author(s):  
Ghania Debbache ◽  
Abdelhak Bennia ◽  
Noureddine Goléa
Keyword(s):  
Neural Networks ◽  
State Feedback ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
External Disturbance ◽  
Nonlinear Function ◽  
State Feedback Control ◽  
Robot Dynamics ◽  
Robust Controller ◽  
Control Scheme

This paper proposes an adaptive control suitable for motion control of robot manipulators with structured and unstructured uncertainties. In order to design an adaptive robust controller, with the ability to compensate these uncertainties, we use neural networks (NN) that have the capability to approximate any nonlinear function over a compact space. In the proposed control scheme, we need not derive the linear formulation of robot dynamic equation and tune the parameters. To reduce the NNs complexity, we consider the properties of robot dynamics and the decomposition of the uncertainties terms. The proposed controller is robust against uncertainties and external disturbance. The validity of the control scheme is demonstrated by computer simulations on a two-link robot manipulator.

Design of H∞ Position Tracking Controllers for Current-Fed Induction Motors Used in Articulated Mechanical Loads

2002 ◽  
Vol 124 (3) ◽  
pp. 485-491
Author(s):  
Stefano Chiaverini ◽  
Giuseppe Fusco
Keyword(s):  
Induction Motors ◽  
Robot Manipulator ◽  
Tracking Errors ◽  
Rotor Resistance ◽  
Control Scheme ◽  
Tracking Controllers ◽  
Riccati Matrix Equation ◽  
Set Up ◽  
Riccati Matrix

This paper describes a systematic procedure to design H∞ position and flux-norm tracking controllers for current-fed induction motors. The designed controllers achieve convergence to zero of both position and flux-norm tracking errors while ensuring robustness with respect to unknown load torque disturbances. The proposed procedure offers the possibility of a simple development of the controllers’ design; in particular, it does not require a numerical solution of the Riccati matrix equation. A case study has been set up which considers the application of the proposed control scheme to a two-link robot manipulator system actuated by two induction motors. Numerical simulation results confirm the validity of the proposed design methodology, even in the presence of rotor resistance uncertainty.

Experimental Verification of Adaptive Dominant Type Hybrid Adaptive and Learning Controller for Trajectory Tracking of Robot Manipulators

2013 ◽  
Vol 25 (4) ◽  
pp. 737-747 ◽  
Author(s):  
Munadi ◽  
◽  
Tomohide Naniwa ◽  
Keyword(s):  
Adaptive Control ◽  
Motion Control ◽  
Trajectory Tracking ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Control Input ◽  
Dominant Type ◽  
Dynamical Parameters ◽  
Small Robot

This paper presents an experimental study to verify an adaptive dominant type hybrid adaptive and learning controller for acquiring an accurate trajectory tracking of periodic desired trajectory of robot manipulators. The proposed controller is developed based on combining the model-based adaptive control (MBAC), repetitive learning control (RLC) and proportionalderivative (PD) control in which the MBAC input becomes dominant than other inputs. Dominance of adaptive control input gives the advantage that the proposed controller could adjust the feed-forward motion control input immediately after changing the desired motion or load of the manipulator. In motion control law, the proposed controller uses only one vector to estimate the unknown dynamical parameters. It makes the proposed controller as a simpler hybrid adaptive and learning controller which does not need much computational power and also is easily be implemented for real applications of robot manipulators. The proposed controller is verified through experiments on a four-link small robot manipulator as representation of a scale robot manipulator to ensure this controller can be applied in the real applications of robot manipulators. The experimental results show the effectiveness of the proposed controller by indicating the position tracking error approaches to zero.

scholarly journals Saturating Stiffness Control of Robot Manipulators with Bounded Inputs

2017 ◽  
Vol 27 (1) ◽  
pp. 79-90 ◽  
Author(s):  
María del Carmen Rodríguez-Liñán ◽  
Marco Mendoza ◽  
Isela Bonilla ◽  
César A. Chávez-Olivares
Keyword(s):  
Closed Loop ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Contact Forces ◽  
Control Law ◽  
Closed Loop System ◽  
Control Scheme ◽  
Lyapunov Stability Analysis ◽  
Stiffness Control ◽  
Three Degree Of Freedom

AbstractA saturating stiffness control scheme for robot manipulators with bounded torque inputs is proposed. The control law is assumed to be a PD-type controller, and the corresponding Lyapunov stability analysis of the closed-loop equilibrium point is presented. The interaction between the robot manipulator and the environment is modeled as spring-like contact forces.The proper behavior of the closed-loop system is validated using a three degree-of-freedom robotic arm.

An Adaptive Evolutionary Switching Control for Robot Manipulators

2005 ◽  
Author(s):  
P. R. Ouyang ◽  
W. J. Zhang ◽  
Madan M. Gupta
Keyword(s):  
Trajectory Tracking ◽  
Control Method ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Switching Control ◽  
Learning Ability ◽  
Pd Control ◽  
Control Approach ◽  
Gain Switching ◽  
Control Scheme

In this paper, a new adaptive switching control approach, called adaptive evolutionary switching PD control (AES-PD), is proposed for iterative operations of robot manipulators. The proposed AES-PD control method is a combination of the feedback of PD control with gain switching and feedforward using the input torque profile obtained from the previous iteration. The asymptotic convergence of the AES-PD control method is theoretically proved using Lyapunov’s method. The philosophy of the switching control strategy is interpreted in the context of the iteration domain to increase the speed of the convergence for trajectory tracking of robot manipulators. The AES-PD control has a simple control structure that makes it easily implemented. The validity of the proposed control scheme is demonstrated for the trajectory tracking of robot manipulators through simulation studies. Simulation results show that the AES-PD control can improve the tracking performance with an increase of the iteration number. The EAS-PD control method has the adaptive and learning ability; therefore, it should be very attractive to applications of industrial robot control.

scholarly journals Fuzzy Adaptation Algorithms’ Control for Robot Manipulators with Uncertainty Modelling Errors

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yongqing Fan ◽  
Keyi Xing ◽  
Xiangkui Jiang
Keyword(s):  
Fuzzy Logic ◽  
Online Learning ◽  
Reference Model ◽  
Robot Manipulators ◽  
Fuzzy Logic System ◽  
Fuzzy Rules ◽  
Fuzzy Logic Systems ◽  
Control Scheme ◽  
Logic Systems ◽  
Logic System

A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.

Sign in / Sign up

Export Citation Format

Share Document