Fares Boudjema

Velocity observer-based iterative learning control for robot manipulators

This article addresses the problem of designing an iterative learning control for trajectory tracking of rigid robot manipulators subject to external disturbances, and performing repetitive tasks, without using the velocity measurement. For solving this problem, a velocity observer having an iterative form is proposed to reconstruct the velocity signal in the control laws. Under assumptions that the disturbances are repetitive and the velocities are bounded, it has been shown that the whole control system (robot plus controller plus observer) is asymptotically stable and the observation error is globally asymptotically stable, over the whole finite time-interval when the iteration number tends to infinity. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed observer–controller schemes.

A stable self-tuning proportional-integral-derivative controller for a class of multi-input multi-output nonlinear systems

This paper proposes a self-tuned proportional-integral-derivative (PID) controller for a class of uncertain continuous-time multi-input multi-output nonlinear dynamic systems. Within this scheme, the PID controller is employed to approximate an unknown ideal controller that can achieve control objectives. The three PID control gains are adjustable parameters and they are updated online with a stable adaptation mechanism designed to minimize the error between the unknown ideal controller and the used PID controller. The proposed approach can be regarded as a simple and effective model-free control because the mathematical model of the system is assumed unknown. The stability analysis of the closed-loop system is performed using a Lyapunov approach. It is proved that all signals in the closed-loop system are uniformly ultimately bounded and that the tracking error can be made to converge to zero in the absence of approximation errors. The effectiveness of the proposed adaptive PID control is demonstrated in simulation.

Linear adaptive control of a class of SISO nonaffine nonlinear systems

This paper addresses the problem of linear adaptive control for a class of uncertain continuous-time single-input single-output (SISO) nonaffine nonlinear dynamic systems. Using the implicit function theory, the existence of an ideal controller which can achieve control objectives is firstly demonstrated. However, this ideal controller cannot be known and computed even if the system model is well known. The aim of our work is to construct this unknown ideal controller using a simple linear controller with the free parameters updated online by a stable adaptation mechanism designed to minimise the error between the unknown ideal controller and the used linear controller. Since the mathematical model of the system is assumed unknown in this work, the proposed control scheme can be regarded as a simple model free controller for the studied class of nonaffine systems. We prove that the closed-loop system is stable and all the signals are bounded. An application of the proposed linear adaptive controller for a nonaffine system is illustrated through the simulation results to demonstrate the effectiveness of the proposed control scheme.

A stable linear adaptive controller applied to a pneumatic actuator system

This paper presents a stable linear adaptive control scheme for the position control of a pneumatic actuator system. The proposed controller does not require the experimental identification of the mass flow rate, which is really a novelty in the field of such applications. The controller synthesis is based on an uncertain non-affine nonlinear model of the pneumatic system. Firstly, the existence of an ideal controller which can achieve position control objectives is demonstrated using the implicit function theory. Nevertheless, even if the actuator model is well-known, this ideal controller cannot be known and computed. The aim of the proposed approach is to construct this unknown ideal controller using a simple linear controller with a stable adaptation mechanism. The stability of the closed-loop system is studied by using a Lyapunov approach. Finally, simulation results are provided to show the capabilities of the presented control method.

An ℒ1 fuzzy adaptive controller for a class of SISO nonaffine nonlinear systems: application to the control of an electropneumatic actuator:

This paper proposes an ℒ1 fuzzy adaptive controller for a class of uncertain continuous-time single-input single-output nonaffine nonlinear systems. The structure of this controller is derived based on ℒ1 adaptive control design methodology and integrates a fuzzy system. The latter is used to approximate as best as possible a function of an unknown ideal implicit controller, which provides good results and improves the performance significantly. The ℒ1 fuzzy adaptive controller consists of a predictor, a control law and its adaptive laws. The major advantage of the proposed control scheme is its ability to guarantee uniformly bounded transient and tracking performance for the controlled system. These performance bounds can be rendered arbitrarily small by the systematic choice of design parameters. The effectiveness and feasibility of the proposed ℒ1 fuzzy adaptive controller are examined experimentally in the position control of a pneumatic actuator system.