Adaptive output-feedback control of systems with output nonlinearities

Distributed model reference adaptive containment control of heterogeneous multi-agent systems with unknown uncertainties and directed topologies

Global adaptive neural network control of nonlinear time-varying systems with unknown control coefficients and model uncertainties

The force tracking error is dependent on both the position tracking error and the estimated parameter error so that, in constrained adaptive robot control, the convergence of the estimated parameter error becomes more important than in unconstrained adaptive robot control. However, in the direct adaptive controller, the parameter adaptation is driven only by the tracking error in the joint motion, while in the indirect adaptive controller, the parameter estimation is driven only by the prediction error in the joint torque. Based on this observation a new combined direct and indirect adaptive controller driven by both the tracking error and the prediction error is developed for constrained robots with inertia parameter uncertainty. Stability of the combined adaptive controllers is established in the Lyapunov sense. The combined control improves both position and force tracking performance compared with direct adaptive control. The computer simulations are presented to demonstrate the proposed schemes.

SummaryAt the present time, there is a great deal of interest in the adaptive control of systems with rapidly varying parameters. Such problems arise in different forms and in many disciplines including finance, sociology, biology, and engineering. In general, these problems are intractable mathematically and the time variations have to be classified in some form to obtain rigorous results. In this paper, we consider the adaptive identification and control of linear systems with periodically varying parameters (referred to as linear time‐varying periodic (LTP) systems). The class of systems with known periodic parameters has been investigated widely since the pioneering work of Floquet and lends itself to rigorous mathematical analysis. However, very little was known until the early years of the last decade concerning the adaptive control of such systems. The problem was introduced by Xu and explored in detail by Narendra and Zhiling in 2006 and 2009, respectively. In 2012, a new method was introduced in adaptive control by Han and Narendra and is referred to as second‐level adaptation. In this paper, the adaptive identification and control of LTP systems using second‐level adaptation is discussed.

In this paper, an adaptive fuzzy controller for single-input single-output (SISO) nonlinear systems with higher-order and unmatched uncertainties is provided. The design is based on the principle of sliding mode control, input-output linearizing controller, and the approximation capability of adaptive fuzzy systems. Finally, the suggested controller is applied to a flexible joint robot. Simulation results indicate its efficiency.

An adaptive integral plus states neural control of aerobic continuous stirred tank reactor

In this paper, the design of a neural network (NN) based adaptive model-based event-triggered control of an uncertain single input single output (SISO) nonlinear discrete time system in affine form is presented. The controller uses an adaptive estimator consisting of a single-layer NN not only to approximate the internal dynamics of an affine nonlinear discrete-time system but also to provide an estimate of the state vector during inter event interval. The NN weights of the adaptive NN estimator are tuned in a aperiodic manner at the event trigger instants unlike periodic updates in standard adaptive neural network (NN) control. A dead zone operator is used to reset the event trigger error to zero as long as the system states continue to remain in a bounded region due to NN reconstruction errors. Lyapunov method is used to derive the event trigger condition, prove uniform ultimate boundedness (UUB) of the NN weight estimation error and system states.

Adaptive traffic control systems continuously monitor traffic conditions on an arterial and adjust traffic signal timings to accommodate variation in traffic volume and minimize stops and delays. This paper describes currently practical adaptive control systems which have been used in the United States and worldwide, which include Adaptive Control System-Lite (ACS-Lite), Optimization Policies for Adaptive Control (OPAC), Real-Time Hierarchical Optimized Distributed and Effective System (RHODES), Sydney Coordinated Adaptive Traffic System (SCATS) and Split Cycle Offset Optimization (SCOOT). The comparisons of the benefits from past experiences, cost and hardware requirements will help the traffic engineers and decision makers a guideline to select the optimized adaptive control system based on research objectives.

Adaptive Variable Structure Control for Linear Systems with Time-varying Multi-delays and Mismatching Uncertainties

Implicit function based adaptive control of non-canonical form discrete-time nonlinear systems

This paper concerns the robust passive control problem for linear systems with norm-bounded time-varying uncertainties in all the system matrices. Our objective is to design a feedback controller such that the closed-loop system is both quadratically stable and strictly passive for all admissible uncertainties. It is shown that such a problem is equivalent to a scaled strictly positive real control problem for a certain linear time-invariant system in which no uncertainty occurs. Therefore, a complete solution can be obtained by using the existing results of standard positive real control.

An adaptive output-feedback control scheme is proposed for a class of nonlinear systems with unknown constant parameters, including the high-frequency gain, and unmodeled dynamics. With priori knowledge of the bounds of the parametric uncertainties, the smooth projection method and the discontinuous projection method are incorporated into the adaptive laws to prevent parameter drifts, respectively. The control schemes do not require a priori knowledge of high-frequency gain sign. It is proved that under the proposed control laws, all the closed loop signals are bounded and the output asymptotically converges to zero.

This paper analyzes a direct adaptive pole placement control for linear stable minimum-phase systems with input magnitude and rate constraints. The key feature is the derivation of a governing equation relating the prediction error to the "input discrepancy" between the adaptive control and corresponding non-adaptive one, independent of how parameter estimates were obtained. With the governing equation, one is able to establish the input matching property of the adaptive constrained control, i.e., asymptotic convergence to zero of the "input discrepancy".

Intelligent switching adaptive control for uncertain nonlinear dynamical systems

In this article, the predefined-time adaptive fuzzy control problem for strict-feedback nonlinear systems with asymmetric time-varying output constraint and unknown backlash-like input hysteresis is considered. An innovative predefined-time adaptive control method, in which utilises unified barrier function (UBF) to deal with the output constraint problem and adopts a differential equation to represent the unknown backlash-like hysteresis, is presented by combining a novel command filter with backstepping technology. Fuzzy logic systems (FLSs) are employed to estimate the unknown nonlinearities. Moreover, by designing appropriate parameters, the constructed control strategy ensures that all signals are predefined-time bound and the tracking error stays in a small neighbourhood adjacent zero. Ultimately, two simulation examples are given to prove the effectiveness of the presented control algorithm.