Robust Direct Adaptive Control Research Articles

Robust adaptive fuzzy control in the presence of external disturbance and approximation error

Robust direct and indirect adaptive fuzzy controllers are proposed for tracking control of a class of nonlinear SISO systems. The behavior of the adaptive law is investigated in the error state space when the perturbations are introduced in the adaptive fuzzy control system. A new adaptation scheme that can eliminate the effect of perturbations on the parameter adaptation is proposed. It is proved that all the signals in the closed-loop system are bounded in the presence of external disturbances and approximation error of the fuzzy logic system. The concept of persistent excitation is utilized to guarantee the convergence and the boundedness of adaptation parameters. It has also been observed that the convergence of parameters and tracking error could be smoother and faster with the proposed methods compared with the conventional adaptive fuzzy controllers. Two simulations have been conducted to demonstrate the effectiveness of the proposed controllers.

Integrated Direct/Indirect Adaptive Robust Precision Control of Linear Motor Drive Systems with Accurate Parameter Estimations

The focus of the paper is on the synthesis of nonlinear adaptive robust controllers for precision linear motor drive systems that achieve not only excellent output tracking performance but also accurate parameter estimations for secondary purposes such as machine health monitoring and prognostics. Such an objective is accomplished through the intelligent integration of the output tracking performance oriented direct adaptive robust control (DARC) design with the accurate parameter estimation based indirect adaptive robust control (IARC) design. Comparative experimental results demonstrate that the proposed DIARC controller is able to achieve better tracking performance and better parameter estimation than either the DARC or the IARC algorithms.

A new robust direct adaptive controller for discrete-time system with unmodelled uncertainties

Abstract In this paper, a new robust discrete-time direct adaptive control scheme with respect to bounded disturbance and unmodelled dynamics is presented. To cope with the residual error arising from the model-plant mismatch, the integrator is incorporated into controller. The parameters of reduced-order plant model and upper bounds on disturbance and unmodelled dynamics are estimated by utilizing a modified dead zone with adjustable size. The convergence and sufficient condition of robust stability established. This condition determines the admissible upper bound on unmodelled uncertainty, which can be tolerated. The simulation results demonstrating the effectiveness of the proposed adaptive controller are given.

Design of robust direct adaptive controllers for SISO systems: Time and frequency domain design conditions

This paper presents the design of a robust direct model reference adaptive control (DMRAC) algorithm with focus on the satisfaction of an almost strictly positive real (ASPR) condition in the presence of plant uncertainty represented as either variations in plant parameters or unmodelled dynamics in the form of additive or multiplicative norm bounded perturbations in the transfer function. Both the time and frequency domain design conditions for a feedforward compensator have been developed utilizing the robust stability analysis and optimization methods. Illustrative examples are presented to validate the algorithm's use and applicability to control systems with parametric uncertainty or unmodelled dynamics.

Robust direct adaptive control for a class of systems with delays

Robust direct adaptive control for a class of systems with delays

Control of an Inverted Pendulum Using Direct Model Reference Adaptive Control

This paper presents the design of a robust direct model reference adaptive control (DMRAC) algorithm with focus on the satisfaction of an almost strictly positive real (ASPR) condition over the maximal range of parameter variations of the plant, and its application to an inverted pendulum. To alleviate the ASPR condition, the pendulum is augmented with a feedforward compensator optimized for robustness. A DMRAC algorithm ensuring asymptotic model following is then integrated with the feedforward compensator design. These new experimental results validate the algorithm's use and applicability to the control of an inverted pendulum with a variety of parameter uncertainties. On presente la conception d'un algorithme robuste de reglage adaptif a 'reference au modele directe (DMRAC) qui dois satisfaire la condition de realization positive presque partout (PPP) et son application a un pendule renverse. Pour alleger la condition PPP, le pendule est ameliore' par l'ajout d'un compensateur "feed-forward" qui est optimise' pour la robustess. Puis, un algorithme DMRAC qui assure que la modele soit suivi asymptotiquement est integre' dans la realisation du compensateur. Les nouvaux resultats experimentaux verifient l'usage et l'application de l'algorithme pour regler le pendule renverse' avec des incretitudes sur les valeurs des parametres.

Design of Robust Feedforward Compensators for Direct Model Reference Adaptive Systems

Design of a robust direct model reference adaptive control algorithm focusing on the satisfaction of an almost strictly positive real condition (ASPR) for both single-input single-output and multi-input multi-output plants is presented. Systematic design procedures utilizing optimization methods and frequency domain design conditions for a feedforward compensator are developed so that the ASPR condition is satisfied over the maximal range of plant parameter variations.

Robust direct adaptive control with least prior knowledge

A new, robust, discrete time, singularity-free, direct adaptive-control scheme is proposed with respect to a class of modeling uncertainties in this paper. Two key features of this scheme are that a relative dead zone is used, but no knowledge of the parameters of the upper bounding function on the class of modeling uncertainties is required, and no knowledge of the lower bound on the leading coefficient of the parameter vector is required to ensure the control law singularity free. Global stability and convergence results of the scheme are provided.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Robust direct adaptive controllers with a new normalization technique

This note presents a robust direct adaptive control scheme with a new normalization technique and its stability analysis in the presence of unmodeled dynamics and/or bounded disturbances. Following input-output stability theory, L/sub 2/ and L/sub /spl infin// stability of the system is studied using the sector stability and passivity theorems and conditions for global stability are derived. It is also shown that robustness of the adaptive control system can be improved when relative dead zone and the novel normalization technique are incorporated into the scheme. >

Comments on "Robust pole placement direct adaptive control" by F. Giri et al

An improved proof is presented for the main result of the paper of Giri et al. (ibid., vol.34, no.3, p.356-9, Mar. 1989). >

A robust adaptive performance enhancement controller using set membership identification

A robust direct adaptive control scheme is proposed to enhance the performance of a fixed nominal controller. The key idea lies in the introduction of a set membership constraint in the estimation process. The set membership constraint results in an adaptive scheme that has a built-in discriminator to discard incoming data that carry little or no information content on the actual plant. The proposed scheme, which uses only informative data to refine the parameter estimates, is more robust than conventional methods that continuously update the parameter estimates. >

A robust direct adaptive controller

This paper proposes a new direct adaptive control algorithm which is robust with respect to additive and multiplicative plant unmodeled dynamics. The algorithm is designed based on the reduced-order plant, which is assumed to be minimum phase and of known order and relative degree, but is analyzed with respect to the overall plant which, due to the unmodeled dynamics, may be nonminimum phase and of unknown order and relative degree. It is shown that if the unmodeled dynamics are sufficiently small in the low-frequency range, then the algorithm guarantees boundedness of all signals in the adaptive loop and "small" residual tracking errors for any bounded initial conditions. In the absence of unmodeled dynamics, the residual tracking error is shown to be zero.