This paper presents a discrete-time decentralized control scheme for identification and trajectory tracking of a two degrees of freedom (DOF) robot manipulator. A recurrent high order neural network (RHONN) structure is used to identify the plant model and based on this model, a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques. The neural network learning is performed online by Kalman filtering. A controller is designed for each joint, using only local angular position and velocity measurements. These simple local joint controllers allow trajectory tracking with reduced computations. The proposed scheme is implemented in real-time to control a two DOF robot manipulator.

In this paper we present a fuzzy self-tuning algorithm to select the Proportional, Integral and Derivative gains of a PID controller according to the actual state of a robotic manipulator. The stability via Lyapunov theory for the closed loop control system is also analyzed and shown that is locally asymptotically stable for a class of gain matrices depending on the manipulator states. This feature increases the potential of the PID control scheme to handle practical constraints in actual robots such as presence of actuators with limited torque capabilities. Experimental results on a two degrees of freedom robot arm shown the usefulness of the proposed approach.

This paper presents the development of a control algorithm, which combines the linear regulator theory and Takagi–Sugeno (T–S) fuzzy modeling. Each local controller consists in a feedback control term plus a trajectory tracking term. The global controller is determined by means of T–S methodology. Then, the proposed algorithm is applied in real time on a robot manipulator. © 2009 Wiley Periodicals, Inc.

This paper addresses the set-point control of robot manipulators with friction where avoiding saturation of the actuators is a major issue. The original contribution is a novel direct fuzzy control system dealing with both practical constraints in mechanical manipulators: saturation and friction. The control system is made by taking advantage of input-output properties of the so-called sectorial fuzzy controllers. When friction is considered, we prove, via Lyapunov theory, that the steady state position errors owing to static friction are inside of a global attractor, which can be arbitrarily reduced. In case of absence of friction, the closed-loop system becomes globally asymptotic stable. In both cases, the important theoretical and practical feature of maintaining the control actions always within prescribed limits according to the actuator torque capabilities is guaranteed. Experimental evaluation of the proposed direct fuzzy control system on a nonlinear direct-drive robot arm is presented to validate its effectiveness.

This paper shows that fuzzy control systems satisfying sectorial properties are effective for motion tracking control of robot manipulators. We propose a controller whose structure is composed by a sectorial fuzzy controller plus a full nonlinear robot dynamics compensation, in such a way that this structure leads to a very simple closed-loop system represented by an autonomous nonlinear differential equation. We demonstrate via Lyapunov theory, that the closed-loop system is globally asymptotically stable. Experimental results show the feasibility of the proposed controller.

This paper deals with applications of fuzzy logic systems to motion control of robot manipulators. In the proposed application, the fuzzy logic system plays the role of a tuner of the robot controller gains. This idea is evoked for the gains of the so-called PD+ control scheme and we demonstrate, by taking into account the full nonlinear and multivariable nature of the robot dynamics, that the overall closed loop system is globally asymptotically stable. Experimental results on a two degrees of freedom direct-drive arm show the usefulness of the proposed control approach.

Computed-torque control is a well-known motion control strategy for manipulators which ensures global asymptotic stability for fixed symmetric positive definite (proportional and derivative) gain matrices. In this paper, we show that global asymptotic stability also holds for a class of gain matrices depending on the manipulator state. This feature increases the potential of the computed-torque control scheme to handle practical constraint in actual robots such as presence of friction in the joints and actuators with limited torque capabilities. We illustrate this potential by means of a fuzzy self-tuning algorithm to select the proportional and derivative gains according to the actual tracking position error. Experiments on a two degrees of freedom robot arm show the usefulness of the proposed approach.

Computed-torque control is a well known motion control strategy for manipulators which ensures global asymptotic stability for fixed symmetric positive definite (proportional and derivative) gain matrices. We show that the global asymptotic stability attribute also holds for a class of gain matrices depending on the manipulators state. This feature increases the potential of the computed-torque scheme to handle practical constraints in actual robots such as presence of friction in the joints and actuators with limited torque capabilities. We illustrate this potential by means of a fuzzy self-tuning algorithm to select the proportional and derivative gains according to the actual tracking position error. Experiments on a two degrees of freedom robot arm shown the usefulness of the proposed approach.