New results in decentralized adaptive nonlinear control with output-feedback

A novel decentralised indirect adaptive output feedback fuzzy controller with a compensation controller and an H ∞ tracking controller is presented for a class of uncertain large-scale nonlinear systems in this article. The compensator adaptively compensates for interconnections between subsystems as well as mismatched errors, while the H ∞ controller suppresses the effect of external disturbances. Based upon the combination of fuzzy inference systems, a state observer, H ∞ tracking technique and the strictly positive real condition, the proposed overall observer-based decentralised algorithm guarantees not only asymptotical tracking of reference trajectories but also an arbitrary small attenuation level of the unmodelled error dynamics including the disturbances on the tracking control. Simulation results substantiate the effectiveness of the proposed scheme.

In this article, a novel high gain observer (HGO)-based decentralised indirect adaptive fuzzy controller is developed for a class of uncertain affine large-scale nonlinear systems. By the combination of fuzzy logic systems and an HGO, the state variables are not required to be measurable. The proposed feedback and adaptation mechanisms guarantee that each subsystem is able to adaptively compensate for interconnections and disturbances with unknown bounds. It is ascertained using a singular perturbation method that all the signals of the closed-loop large-scale system stand uniformly ultimately bounded and the tracking errors converge to tunable neighbourhoods of the origin. Simulation results of correlated double inverted pendulums substantiate the effectiveness of the proposed controller.

formula omitted] tracking-based decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale nonlinear systems

This paper presents a decentralized adaptive controller for a class of large-scale nonlinear systems with unknown subsystems and interactions. A direct adaptive controller is devised based on Lyapunov stability analysis so that the stability of the closed loop system is guaranteed by introducing a suitably driven adaptive rule. The adaptive controller proposed in this paper can guarantee the stability of the closed-loop system without knowing the sign of the controller coefficient. To show the effectiveness of the proposed decentralized adaptive controller, a nonlinear system is chosen as a case study. Simulation results are very promising.

In this paper, we consider decentralized adaptive output-feedback control problem for a class of large-scale nonlinear systems, in which the nonlinear actions within subsystems and nonlinear interactions between subsystems contain unknown time delays, and need not satisfy the matching condition. A memoryless decentralized adaptive output-feedback controller is proposed, and global stability results of the closed-loop system are proved.

In this paper, we propose an adaptive fuzzy output feedback control method for trajectory tracking control problem for robotic systems. Using Lyapunov method, we first develop a stable adaptive fuzzy state feedback control algorithm by assuming that the systems output and its derivatives are available for feedback control design. The algorithm combines fuzzy systems with robust adaptive controller. The fuzzy system approximates the certainty equivalent (CE)-based optimal controller while robustifying adaptive control term is used to cope with uncertainties that appeared from the effect of external disturbance, fuzzy approximation errors and other modeling errors. Then, an output feedback form of the position-velocity (state feedback) controller is proposed where unknown velocity signal is replaced by the output of model-free linear estimator. We show via asymptotic analysis that the tracking performance of the output feedback design can recover the performance achieved under the state feedback control design. Finally, the proposed method is implemented and evaluated on a 2-DOF robotic system to demonstrate the theoretical development for the real-time applications.

This paper focuses on the problem of adaptive output feedback tracking control for a class of non-strict-feedback nonlinear systems with unknown control coefficients. By employing a linear state transformation, the original system is transformed to a new system for which control design becomes feasible. Then, after the introduction of a simple input-driven observer, an adaptive output feedback controller which contains only one parameter is developed for such systems by using backstepping technique and variable separation method. The designed controller ensures that all the signals in the closed-loop systems are Semi-globally Uniformly Ultimately Bounded (SGUUB).

This paper presents a fuzzy basis function approach for adaptive decentralized control of a class of large-scale nonlinear systems with MIMO subsystems. Hybrid adaptive-robust tracking control schemes which are based on a combination of the H? tracking theory, and fuzzy control design are developed such that all the states and signals are bounded and the H? tracking control performance is guaranteed. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The resultant decentralized control with multi-controller architecture guarantee stability and convergence of the output errors to zero asymptotically, by local output-feedback. Simulation results on the control of a model of a nonlinear electrical machine are presented to illustrate the effectiveness of the proposed controller.

A parameter dependent Riccati equation approach is taken to analyze the stability properties of an output feedback adaptive control law design. The adaptive controller is intended to augment a nominal, fixed gain, observer based output feedback control law. Although the formulation is in the setting of model following adaptive control, the realization of the adaptive controll er does not require implementing the reference model. In this regard, the cost of implementing the adaptive controller above that of a fixed gain control law is far less than that of other methods. The error signals are shown to be uniformly ultimately bounded and an expression for the ultimate bound is provided. The control design process and theoretical results are illustrated using a model for wing-rock dynamics. Research in adaptive output feedback control of uncertain nonlinear systems is motivated by the many emerging applications that employ novel actuation devices for active control of flexible structures and fluid flows. These applications include actuators such as piezo-electric films and s ynthetic jets, which are typically nonlinearly coupled to t he plant dynamics they are intended to control. Models for these applications vary from accurate low frequency models to models that crudely approximate the true dynamics even at low frequencies. Examples of applications include active damping of flexible structures, control of aeroservoelasti c aircraft, and active control of flows. Adaptive control can be used to satisfy performance requirements in the presence of large scale parameter uncertainty, and improved safety in the event of actuator failure.

This paper presents a decentralized adaptive control design for a class of large-scale nonlinear systems with unknown subsystems. When the subsystems are modeled by affine equations, a direct adaptive controller is devised based on the Lyapunov theory, so that the stability of the closed-loop system is guaranteed by introducing a suitably driven adaptive rule. A neuro-based structure is proposed when the subsystems are nonaffine, and the stability analysis is also performed based on the Lyapunov theory. Moreover, the unknown interactions among the subsystems are considered as having a nonlinear function against the simple form considered for the affine case. The proposed controllers are employed in an inverted two-pendulum system, and their promising performances are illustrated.

It is well recognized that an adaptive output feedback control based on almost strictly positive real‐ness (ASPR) or output feedback strictly passivity (OFSP) property of the system has strong robustness with respect to disturbances and uncertainty. However, most practical systems do not have such properties. In order to realize an augmented ASPR system for designing adaptive output feedback controller, a kind of adaptive parallel feedforward compensator (PFC) design schemes have been proposed for linear systems. In this paper, we propose an adaptive PFC design scheme by Kernel method for nonlinear systems and provide output feedback passivity based adaptive output feedback control for nonlinear systems. The effectiveness of the proposed method will be confirmed through numerical simulations. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

This paper deals with a design problem of an adaptive output feedback tracking control for systems with a parallel feedforward compensator (PFC) introduced in order to realize the output feedback exponential passive (OFEP) augmented control system. It is well known that one can easily design an adaptive output feedback control system for nonlinear systems satisfying the OFEP conditions. We adopt introduceing a PFC to make a non-OFEP nonlinear system OFEP and propose an adaptive control system design scheme which improves the control performance of the practical control system without the PFC. The effectiveness of the proposed method will be confirmed through experiments of a magnetic levitation system.

Self-tuned local feedback gain based decentralized fault tolerant control for a class of large-scale nonlinear systems

Both dynamic state feedback as well as output feedback tracking control designs are presented in this paper for constrained robot systems under parametric uncertainties and external disturbances. The previous studies on tracking control design, not considering the velocity measurements, address only the unconstrained robot design. In contrast, a dynamic output feedback controller based on a linear and reduced-order observer that uses only position measurements is proposed here for the first time to treat the trajectory tracking control problem of constrained robot systems. Both adaptive state feedback control schemes and adaptive output feedback control schemes with a guaranteed H∞ performance are constructed. It is shown that all the variables of the closed-loop system are bounded and a pre-assigned H∞ tracking performance is achieved, in the sense that the influence of external disturbance on the tracking motion error can be attenuated to any specified level. Moreover, it is also shown that the motion and force trajectories asymptotically converge to the desired ones as the dynamic model of robot systems is well-known and the external disturbance is neglected. Finally, simulation examples are presented to illustrate the tracking performance of a two-link robotic manipulator with a circular path constraint by the proposed control algorithms. © 1998 John Wiley & Sons, Ltd.

This paper proposes a novel adaptive synchronization approach for a class of nonlinear large-scale systems via T-S fuzzy model. It is known that the practical large-scale systems are very complicated with nonlinear coupling strength between every two subsystems, thus the classical linear control methods are invalid for them. To overcome this deficiency, we employ the T-S fuzzy model in this paper. Firstly, the local linearization approach is utilized to preprocess the nonlinear large-scale systems, and then, according to the universal approximation property of TS fuzzy model, a new system model is established. Secondly, the fuzzy adaptive synchronization controllers are designed by using Parallel Distributed Compensation technique and the corresponding stability analysis is presented through Lyapunov technique. In addition, a new criterion, under which the proposed control scheme can be transformed into pinning control and save unnecessary costs, is also given. Finally, a numerical simulation example is presented to demonstrate the effectiveness of our proposed approach.