Composite Adaptive Law Research Articles

Adaptive Dynamic Surface Based Nonsingular Fast Terminal Sliding Mode Control for Semistrict Feedback System

This paper focuses on an adaptive dynamic surface based nonsingular fast terminal sliding mode control (ADS-NFTSMC) for a class of nth-order uncertain nonlinear systems in semistrict feedback form. A simple and effective controller has been obtained by introducing dynamic surface control (DSC) technique on the basis of second-order filters that the “explosion of terms” problem caused by backstepping method can be avoided. The nonsingular fast terminal sliding mode control is adopted in the last step of the controller design, and the error convergence rate is improved. An composite adaptive law is used to gain fast and accurate parameter estimation. Finally, simulation results are presented to illustrate the effectiveness of the proposed method.

Composite Adaptation for Neural Network-Based Controllers

With the motivation of using more information to update the parameter estimates to achieve improved tracking performance, composite adaptation that uses both the system tracking errors and a prediction error containing parametric information to drive the update laws, has become widespread in adaptive control literature. However, despite its obvious benefits, composite adaptation has not been widely implemented in neural network-based control, primarily due to the neural network (NN) reconstruction error that destroys a typical prediction error formulation required for the composite adaptation. This technical note presents a novel approach to design a composite adaptation law for NNs by devising an innovative swapping procedure that uses the recently developed robust integral of the sign of the error (RISE) feedback method. Semi-global asymptotic tracking is proven for a Euler-Lagrange system. Experimental results are provided to illustrate the concept.

Precise position control of tubular linear motors with neural networks and composite learning

This paper examines an adaptive control scheme for tubular linear motors with micro-metric positioning tolerances. Uncertainties such as friction and other electro-magnetic phenomena are approximated with a radial basis function neural network, which is trained online using a learning law based on Lyapunov design. Differently from related literature, the approximator is trained using a composite adaptation law combining the tracking error and the model prediction error. Stability analysis and bounds for both errors are established, and an extensive experimental investigation is performed to assess the practical advantages of the proposed scheme.

Composite adaptive control for Euler–Lagrange systems with additive disturbances

In a typical adaptive update law, the rate of adaptation is generally a function of the state feedback error. Ideally, the adaptive update law would also include some feedback of the parameter estimation error. The desire to include some measurable form of the parameter estimation error in the adaptation law resulted in the development of composite adaptive update laws that are functions of a prediction error and the state feedback. In all previous composite adaptive controllers, the formulation of the prediction error is predicated on the critical assumption that the system uncertainty is linear in the uncertain parameters (LP uncertainty). The presence of additive disturbances that are not LP would destroy the prediction error formulation and stability analysis arguments in previous results. In this paper, a new prediction error formulation is constructed through the use of a recently developed Robust Integral of the Sign of the Error (RISE) technique. The contribution of this design and associated stability analysis is that the prediction error can be developed even with disturbances that do not satisfy the LP assumption (e.g., additive bounded disturbances). A composite adaptive controller is developed for a general MIMO Euler–Lagrange system with mixed structured (i.e., LP) and unstructured uncertainties. A Lyapunov-based stability analysis is used to derive sufficient gain conditions under which the proposed controller yields semi-global asymptotic tracking. Experimental results are presented to illustrate the approach.

ADAPTIVE FUZZY CONTROL FOR SPEED-REFERENCE TRACKING IN NONLINEAR SERVO DRIVES

Adaptive fuzzy control (AFC) has been an active research area over the last decade and several stable adaptive fuzzy controllers have been proposed in the literature. Such controllers are generally based on feedback linearization and their parameters are updated by tracking error-based adaptive laws, designed by Lyapunov synthesis. However, most of AFC schemes have only been evaluated on relatively simple simulations examples. In this paper, we study and compare different indirect adaptive schemes (linear and fuzzy, with standard and with composite adaptive laws), by means of an experimental benchmark consisting of two coupled servo drives. Parametric and structural changes are introduced to the controlled plant, in order to emphasize the advantages and limitations of the considered adaptive controllers. Experimental results demonstrate that AFC achieves significantly better tracking performance than the linear adaptive controller and that the composite adaptive laws provide a further improvement over the standard adaptive laws.

Robustez de um controlador adaptativo proposto à perturbações limitadas

This article presents a robust and adaptive control design for robot manipulators in joint space coordinates. A new composite adaptive control law, which contains a robust additional term and which uses the prediction error and the tracking errors to drive parameter estimation, is based on passivity and on Lyapunov direct method. It is shown that global stability and convergence can be achieved for the robust and adaptive control algorithm. Numerical simulations are provided to demonstrate the performance and the robustness of the proposed algorithm

COMPOSITE ADAPTIVE FUZZY CONTROL

Adaptive fuzzy control has been a topic of active research over the last decade. However, most efforts have been directed toward one goal: achieving asymptotic stability and tracking. Little attention has been paid to the accuracy of the identified fuzzy models and to their transparency and interpretability whereas these should be the key aspects motivating the use of fuzzy models in adaptive control. The main contribution of this paper is to present an adaptive fuzzy controller with composite adaptive laws based on both tracking and prediction error. Compared to other adaptive fuzzy controllers, the proposed controller achieves smoother parameter adaptation, better accuracy and improved performance. It overcomes some of the drawbacks of similar schemes described in the literature on adaptive fuzzy control. The limitations of the proposed approach are also discussed.

A new adaptive control algorithm for robot manipulators in task space

Adaptive control of robotic manipulators in task space coordinates is considered in this paper. A new composite adaptive control law, which uses the prediction error and tracking error to drive parameter estimation, is developed based on sliding mode and a general Lyapunov-like concept. It is shown that global stability and convergence can be achieved for the adaptive control algorithm. The algorithm has the advantage that inverse of Jacobian matrix and the bounded inverse of the estimated inertia matrix are not required. The algorithm is further modified so as to achieve robustness to bounded disturbances. A simulation example is provided to demonstrate the performance of the proposed algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Robust adaptive control for robot manipulators

A composite adaptive control law for robot manipulators in task space, which uses both the tracking error and the prediction error to drive parameter estimation, is developed in this paper. It is shown that global stability and convergence can be achieved for the adaptive control algorithm in the ideal case, and furthermore that the algorithm can be easily modified by using parameter projection to achieve robustness with respect to a class of unmodelled dynamics. In addition, the algorithm has the advantage that no requirement is needed for the inverse of the jacobian matrix or for the bounded inverse of the estimated inertia matrix. A simulation example is provided for performance demonstration.