Adaptive asymptotical tracking funnel control for strict-feedback nonlinear systems with input quantization

This article studies neural-network (NN) adaptive control for strict-feedback nonlinear systems with matched uncertainties and event-triggered communication. Radial basis function NNs (RBFNNs) are used in the backstepping design approach to compensate for nonlinear uncertain functions. The concept of valid compact sets for RBFNN adaptive controllers is proposed, where a local RBFNN approximator is defined and the closed-loop state can remain. To guarantee the existence of such valid compact sets, a new property on RBFNNs is presented, which shows that, in some properly designed RBFNNs, the norm of their ideal weight vectors can always become arbitrarily small. By utilizing this property, the selections on valid compact sets are investigated, resulting in rigorous proof on RBFNN adaptive controllers to solve a local tracking problem with a given smooth enough reference signal. Subsequently, to save limited communication resources, a Zeno-free event-triggering mechanism in controller-to-actuator channels is proposed. Under this event-triggered adaptive controller, the corresponding tradeoff among the tracking performance, computational burden, and communication consumption is analyzed. Furthermore, two extensions are made to the general local function approximator, which is in the form of a weight vector multiplying a group of basis functions, and to the communication in sensor-to-controller channels. Finally, several simulation results are provided to illustrate the efficiency and feasibility of the obtained results.

Command-filter-based adaptive tracking control for nonlinear systems with unknown input quantization and mismatching disturbances

Based on backstepping technique, an adaptive inverse control scheme is proposed for a class of uncertain nonlinear systems preceded by unknown hysteretic nonlinearities which are described by Krasnosel'skii-Pokrovkii (KP) model. The pseudo-inversion is introduced to compensate the hysteresis nonlinearities and the error caused by the hysteresis model and pseudo-inversion is estimated online by the adaptive controller. To avoid the possible chattering caused by the sign function, a smooth adaptive controller is designed. The proposed controller ensures the uniformly ultimate boundedness of the closed-loop system. Simulation results show the effectiveness of the proposed scheme.

Adaptive control of uncertain nonlinear systems using mixed backstepping and Lyapunov redesign techniques

In this paper, the problem of fixed-time adaptive tracking control for strict-feedback nonlinear systems is studied. By introducing a new tunnel constraint variable, a new adaptive and practical fixed-time controller is constructed based on fuzzy control, fixed-time Lyapunov stability theory and backstepping algorithm. Fuzzy logic system is introduced to approximate the unknown term of the system. Theoretical analysis shows that under the control strategy, and the tracking error converges to a small neighborhood of the origin within fixed-time intervals, where the convergence time is independent of the initial state of the system. At the same time, all the signals of the closed-loop system are bounded. Simulation results show that the proposed controller is effective.

In this note, the event-triggered adaptive control for a class of uncertain nonlinear systems is considered. Unlike the traditional adaptive event-triggered control, the controller and parameter estimator are event-triggered simultaneously. Asymptotical convergence of stabilization error is guaranteed. To solve this problem, we design a set of event-triggering conditions, which is updated for each triggering. At the same time, the input-to-state stable assumption is not needed. It is shown that the proposed control schemes guarantee that all the closed-loop signals are globally bounded and the stabilization error converges to the origin asymptotically. Simulation results illustrate the effectiveness of our scheme.

This work investigates the adaptive finite-time tracking problem for a class of strict-feedback nonlinear systems with parametric uncertainties and symmetric constant output constraint. A tan-type Barrier Lyapunov Function (BLF) is proposed to ensure the boundedness of the output tracking error, and a new tuning function is given to eliminate the effect of uncertainties. It is theoretically shown that the control scheme not only can assure the finite-time convergence of system output tracking error into a small set around zero but also can guarantee that the output constraint is not overstepped. The effectiveness of the proposed finite-time control scheme is further verified with a numerical example.

Stability of adaptive control for time-varying systems (TVS) is surveyed and then a powerful mathematical tool-martingale hyperconvergence theorem-is presented which can be successfully applied to analyze the convergence of various identification algorithms and adaptive control algorithms for TVS. It makes an important advance in the study of the convergence and stability problems of TVS. The analysis indicates that the output tracking error given by adaptive control based on the projection algorithm is consistently bounded. The authors state that this is the best convergence result in the area of adaptive control for time-varying systems up to now.

This paper deals with an adaptive robust tracking control using a multilayer neural network (NN) for a class of nonlinear dynamic systems with unknown time varying state delays. Typical adaptive NN backstepping controllers for uncertain nonlinear systems with time-delay give rise to computation complexity caused by the the repeated derivatives of virtual controllers and nonlinear functions. Moreover, the combined techniques usually result in only uniformly ultimately bounded (UUB) stability caused by the inherent NN approximation error. This paper presents a control scheme that uses an integral sliding mode control as a feedback term and an adaptive neural controller as a feedforward term based on the desired compensation adaptive law (DCAL) technique. First, we develop a new DCAL formulation which avoids the explosion of complexity caused by the general NN backstepping scheme to compensate for nonlinear system uncertainties, bounded system disturbances, and unknown state time delays. Then, using a Lyapunov-Krasovskii (LK) functional, it is shown that the proposed controller renders the class of uncertain nonlinear time-delay systems asymptotically stable.

SummaryThis paper investigates the robust resilient control problem for a class of parametric strict feedback nonlinear systems with prescribed output and virtual tracking errors performance. The resilience is governed by a continuously nonlinear control gains function, which endows the virtual and actual controllers with self‐adjusting abilities with respect to transformed error surfaces. The proposed control scheme is adaptation, estimation, and approximation‐free in the presence of unknown parameters and nonlinearities, and only a number of control gains, which is equal to the relative degree of the considered plants, need to be selected in applications. It is proved by rigorous analysis that the output tracking errors are confined in predefined prescribed performance functions under some nonrestrictive initial conditions, and the bounds of states are obtained characterized as control gains and systemic function‐associated constants. Finally, comparative illustrative examples are given to demonstrate the effectiveness of the proposed control scheme.

This paper studies the adaptive output feedback control problem for single-input, single-output piecewise linear systems with switched parameters. By employing the knowledge of the time instant indicator functions of system parameter switching, a new controller structure parametrization is proposed for the development of a stable adaptive control scheme which is capable of reducing the modeling error in the estimation error signal used for parameter adaptive laws. This key feature is achieved by the new control scheme's ability to avoid a major parameter swapping term in the error model, with the help of indicator functions whose knowledge is available in many applications. It is shown that under the usual slow system parameter switching condition, closed-loop stability (signal boundedness) and small output tracking error in the mean square sense are achieved. Simulation results on linearized NASA GTM models are presented, demonstrating the effectiveness and performance improvement of the proposed adaptive control scheme.

Cascade control for a class of uncertain nonlinear systems: a backstepping approach

This paper focuses on an adaptive robust dynamic surface control (ARDSC) with composite adaptation laws (CAL) for a class of uncertain nonlinear systems in semi‐strict feedback form. A simple and effective controller has been obtained by introducing dynamic surface control (DSC) technique and designing novel adaptation laws. First, the ‘explosion of terms’ problem caused by backstepping method in the traditional adaptive robust control (ARC) is avoided. Meanwhile, through a new proof philosophy the asymptotical output tracking that the ARC possesses is theoretically preserved. Second, when persistent excitation (PE) condition satisfies, true parameter estimates could be acquired via designing CALs which integrate the information of estimation errors. Finally, simulation results are presented to illustrate the effectiveness of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.

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.

This paper studies the problem of global output-feedback tracking control for a class of uncertain nonlinear systems. The system studied in this paper has unknown time-varying control coefficients and unknown reference signal. Notably, the nonlinearities of the system are bounded by the lower triangular linear unmeasured states multiplying the unknown constant, the polynomial-of-output and the polynomial-of-input growth rates, which indicates the presence of serious uncertainties. Motivated by the closely related works, by combining the ideas of universal control and deadzone with backstepping technique, this paper presents a new adaptive tracking control scheme based on two dynamic high-gains in the new forms. The proposed control scheme ensures that the tracking error converges to a specified arbitrarily small interval range after a finite time while the state of the resulting closed-loop system is globally bounded. Finally, the effectiveness of the control scheme is verified by an illustrative example.