Event-Triggered Adaptive Tracking With Guaranteed Transient Performance for Switched Nonlinear Systems Under Asynchronous Switching.

This paper studies a class of discrete-time impulsive switched delayed systems with delay-dependent impulses, aiming to solving the input-to-state stability (ISS) problem in the case of asynchronous switching signal between subsystem and controller and asynchrony of switching signals and impulsive signals. Among them, the delay effect is handled by a new Lyapunov-Krasovskii function divided into a delay-dependent part and a delay-independent part. In contrast to admissible edge-dependent average dwell time (AED-ADT) methods that address switching signals, we propose admissible edge-dependent average impulsive interval (AED-AII) to deal with delayed impulses, thereby establishing the relationship between these methods and the decay rate. Meanwhile, a stabilizing controller for discrete impulsive switched linear delayed systems is proposed and the minimum average dwell time and controller gain are obtained. Compared with the previous work, the parameters of Lyapunov functional relation depend on the directed edges; the operation of subsystem is divided into asynchronous time and synchronous time, which are less conservative; the combination of AED-ADT and AED-AII can be applied to a wider range of impulsive switched systems. Finally, two examples are given to show the effectiveness of the results.

Time delay has always been one of the main factors affecting the application performance of neural network (NN) systems, and dynamic performance research of NNs with time delays has been the focus of many scholars in recent years. This article enquires into the exponentially synchronous problem of switched delayed NNs with time delay in the leakage term. Adopting an unusual form from a common switched system, the switching modes of the switched delayed NNs system in this article are dependent on time delays. In the first place, the master, slave, and error NNs models are reconstructed into the switched form by introducing the switched delay idea. Then with the help of the admissible edge-dependent average dwell time (AED-ADT) method and delay-dependent switching adjustment indicators, a novel set of generalized delay-mode-dependent multiple Lyapunov-Krasovskii functionals (MLKFs) is built for analyzing the cases where a state-feedback controller exists and does not exist in the model, and where parts of LKFs may increase during the period when the corresponding subsystems are activated. For these cases, several effective exponential synchronization criteria and switching laws are presented accordingly. At last, the verification of the theoretical results is shown through a few examples.

The mixed H ∞ and l 2 − l ∞ control problem is tackled in the context of discrete-time switched systems that possess unstabilizable modes, employing an enhanced event-triggered control (ETC) approach. Unlike other papers that only consider stabilizable modes, the switched systems that possess unstabilizable modes are explored. Meanwhile, a new mixed H ∞ and l 2 − l ∞ control is employed that generalises the H ∞ control and l 2 − l ∞ control. Moreover, the admissible edge-dependent average dwell time (AED-ADT) switching signal is designed to offer greater generality and further alleviate the constraints. An enhanced period-sampled switched event-triggered mechanism (ETM) is put forward by utilising a relative threshold function combined with an asynchronous associated jumping function, which can minimise unnecessary waste of resources and effectively prevent the occurrence of Zeno behaviour. With introducing a compensation term composed of jumping function and a slack matrix to the controller design, the asynchronous behaviour is addressed without strict assumptions. Based on the proposed ETM and a flexible description specifying the unstabilizable modes, an improved and less restrictive sufficient criterion has been formulated to ensure the asymptotic stability, accompanied by a mixed H ∞ and l 2 − l ∞ performance index, for discrete-time switched systems that include unstabilizable modes. Finally, a boost converter example and the longitudinal switched system model of tiltrotor aircraft example are used to show the applicability and another numerical example is presented to validate the applicability and benefits of the derived conclusions.

Adaptive actuator failure compensation control of uncertain nonlinear systems with guaranteed transient performance

Stabilization of switched delay systems with polytopic uncertainties under asynchronous switching

SummaryIn this paper, we consider the transient performance of path‐following control of nonholonomic mobile robots with parametric uncertainties. By incorporating prescribed performance bound (PPB) technique, the transient performances on position and orientational tracking errors will be guaranteed with convergence rates no less than certain pre‐specified values and sufficiently small maximum overshoots. We also extend the proposed scheme to solve the formation control problem for a group of N unicycle‐type mobile robots without inter‐robot communications. It is ensured that all the robots can track their references with arbitrarily small errors and no collision will occur between any two robots. Simulation studies verify the established theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.

This paper is concerned with the problem of adaptive neural network (NN) tracking control for a class of uncertain switched nonlinear systems with time delays. Compared with the existing results, a new common Lyapunov function with switched adaptive parameters is constructed. The unknown functions with time-delay state are compensated by using appropriate Lyapunov–Krasovskii functionals in the design. To guarantee transient performance of switched systems with time delays, the prescribed performance bound (PPB) method is utilized. Based on convex combination technique, a novel adaptive NN tracking control method under state-dependent switching law is proposed. It is shown that under the proposed control and switching laws, all the signals of the closed-loop system are bounded and the tracking error is preserved within PPB. Finally, the simulation example is given to demonstrate the effectiveness of the proposed control scheme.

This paper investigates the iterative learning control (ILC) problem of a flexible manipulator in the presence of external disturbances and output constraints. The dynamic behavior of the flexible manipulator is represented by partial differential equations (PDEs). We propose an ILC law to track the desired trajectory and suppress the vibration of the elastic deflection. The control scheme is based on a prescribed performance bound (PPB) which characterizes the maximum restrictions and convergence rate of the tracking error and deflection error. It is shown that the errors satisfy the prescribed performance bond all the time at any iterations. The established theoretical results are illustrated using numerical simulations for control performance verification.

This study aims to address the inherent uncertainty in plug loads and load extraction, distributed generation, and the inevitable circulating current in a parallel structure. Therefore, in this paper, an adaptive backstepping control scheme with an online parameter estimator (OPE) for a plug-and-play parallel converter system in a four-port power switcher is proposed. The adaptive backstepping control method was designed in the dq0 coordinate system to suppress the circulating current in the zero-component; the circulating current can be suppressed by using an embedded algorithm and omitting the extra controller. An adaptive update law was designed to weaken the influence of the arbitrary plug and extraction operations in the DC and AC buses to realize the plug-and-play function. The transient tracking performance is governed by the limitation of maximum total errors in the voltage and current. As a result, the settling times of the voltage, current, and power decreased. Additionally, to further improve the system robustness, an online inductance and resistance estimator was established using an optimal algorithm that solves the weighted least squares problem. In the estimator, there are no additional voltage and current sensors needed, and the mean squared error (MSE) of the estimation can be minimized. Simulation studies on a two-converter parallel system with a plug-and-play function were conducted using MATLAB/SIMULINK (R2018b, MathWorks, Natick, MA, USA) to verify the effectiveness of the proposed adaptive backstepping control strategy. The results show that this strategy improves system performance over that of a system with unbalanced parameters among a parallel structure with AC and DC system disturbances caused by arbitrary plug and extraction operations.

A new backstepping control scheme for piezoactuator-driven stage using prescribed performance is proposed. The piezoactuator-driven stage's hysteresis behavior is described by Bouc-Wen model. A prescribed performance bound is used to tackle the tracking error's convergence rate and maximum overshoot. H-infinity robust control is used to improve the robust controllability of the piezoactuator-driven stage with hysteresis behavior and other disturbance. A backstepping controller is designed for the system. The controller can guarantee that all signals are bounded in the closed-loop system and the prescribed tracking error transient and steady state are satisfied. The closed-loop system's stability is proved by Lyapunov functions. Finally, computer simulation results are presented to illustrate the effectiveness of the proposed control scheme.

In this work, the flexible performance-based control scheme is presented for the networked uncertain mechanical system under the quasi-periodic denial-of-service (DoS) attack and the appointed event-triggering (ET) mechanism. This scheme features the capability of releasing and recovering the prescribed performance in light of the activation and sleeping of the DoS attack. These functionalities attribute to the introduction of a self-regulation auxiliary system that is developed based on the theories of the positive system and the finite-time stability. Furthermore, the neural network (NN) is utilized to approximate the system uncertainty, and the noncertainty-equivalent structure is adopted to construct the update law of the NN. Then, the influence of the quasi-periodic DoS attack and the appointed ET mechanism on uncertainty approximation are totally isolated. Based on the Lyapunov function method and the invariant set theory, the boundedness of the closed-loop system is theoretically proved. Finally, both the numerical and experimental illustrations are presented to demonstrate the effectiveness and the benefits of the developed control scheme.

SummaryThis paper deals with the design and application of nonlinear model‐based control schemes for stable and nonlinear benchmark industrial processes. The primary control objective is to facilitate set‐point (constant/time‐varying) tracking in the presence of external disturbances, process noise, measurement noise, parametric uncertainty, and model mismatch. We first propose a “noninferential‐type” model‐based control scheme which involves a finite‐dimensional, nonlinear, and deterministic process model to generate the model states. Secondly, an “inferential‐type” model‐based control scheme has been introduced particularly to take into account the stochastic uncertainties such as process noise and measurement noise. The second scheme exploits the dual extended Kalman filter for estimating the immeasurable states and the process parameters through which disturbance is injected. Unlike fixed‐parameter controllers, the proposed schemes update the controller gains at each step depending on the real‐time process gains. In order to demonstrate the usefulness of the proposed closed‐loop tracking control schemes, two exhaustive case studies have been carried out on the CSTR and Van de Vusse reactor processes, which are considered to be benchmark industrial processes due to highly nonlinear and unpredictable behaviour and due to nonminimum phase property. Finally, the performance of the proposed schemes are compared with an EKF‐based adaptive PI control framework and the simulation results reveal that the transient performance of the proposed schemes are better than that of the aforementioned PI technique especially in perturbed condition (ie, in presence of model mismatch and measurement noise).

This article investigates the adaptive neural network (NN) event-triggered containment control problem for a class of nonlinear multiagent systems (MASs). Since the considered nonlinear MASs contain unknown nonlinear dynamics, immeasurable states, and quantized input signals, the NNs are adopted to model unknown agents, and an NN state observer is established by using the intermittent output signal. Subsequently, a novel event-triggered mechanism consisting of both the sensor-to-controller and controller-to-actuator channels are established. By decomposing quantized input signals into the sum of two bounded nonlinear functions and based on the adaptive backstepping control and first-order filter design theories, an adaptive NN event-triggered output-feedback containment control scheme is formulated. It is proved that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) and the followers are within a convex hull formed by the leaders. Finally, a simulation example is given to validate the effectiveness of the presented NN containment control scheme.

A trajectory tracking control scheme for a class of underactuated vehicles is presented here. The underactuated vehicle is modeled as a rigid body to which control thrust is applied along a single body-fixed axis and control torque can be applied along all three body-fixed coordinate axes. The combined rotational and translational motion of the rigid body is underactuated with the degree of underactuation two. Such an actuation scheme is reflective of a wide range of vehicles such as vertical take-off and landing (VTOL) aircraft, fixed-wing aircraft, and quadcopter uncrewed aerial vehicles (UAVs). For this class of underactuated vehicles, the proposed controller gives exponential stabilization of the position tracking error. Additionally, the component of the angular velocity parallel to the thrust direction, is regulated to be zero under the proposed control scheme. The other two components of the angular velocity track desired profiles that lead to exponential stabilization of the desired position trajectory; this angular velocity tracking control scheme is finite time stable. The combination of these schemes leads to guaranteed exponential stability of the overall feedback control scheme. A set of numerical simulation results that illustrate the performance of the overall feedback tracking control scheme is provided.

In this paper, a nonlinear adaptive backstepping based speed controller is designed for the field weakening area of a separately excited DC motor. The nonlinear dynamic model of the motor is firstly derived, in which the parameter uncertainties such as the inertia and load torque are considered. Then by using a linear reference model to design the transient performance and through suitable nonlinear change of coordinates, a nonlinear adaptive backstepping control algorithm is derived step by step and the stability analysis is also carried out. The mechanical speed and the back EMF tracking objectives are satisfied. Simulation results clearly show that the proposed controller is of good performance and robust to the parameter uncertainties.