Quantized output‐feedback event‐triggered distributed control of switched fractional multi‐agent systems subject to input nonlinearities and consensus error constraints

This article focuses on an adaptive dynamic surface tracking control issue of nonlinear multiagent systems (MASs) with unmodeled dynamics and input quantization under predefined accuracy. Radial basis function neural networks (RBFNNs) are employed to estimate unknown nonlinear items. A dynamic signal is established to handle the trouble introduced by the unmodeled dynamics. Moreover, the predefined precision control is realized with the aid of two key functions. Unlike the existing works on nonlinear MASs with unmodeled dynamics, to avoid the issue of "explosion of complexity," the dynamic surface control (DSC) method is applied with the nonlinear filter. By using the designed controller, the consensus errors can gather to a precision assigned a priori. Finally, the simulation results are given to demonstrate the effectiveness of the proposed strategy.

This article investigates the problem of path following for the underactuated unmanned surface vehicles (USVs) subject to state constraints. A useful control algorithm is proposed by combining the backstepping technique, adaptive dynamic programming (ADP), and the event-triggered mechanism. The presented approach consists of three modules: guidance law, dynamic controller, and event triggering. First, to deal with the "singularity" problem, the guidance-based path-following (GBPF) principle is introduced in the guidance law loop. In contrast to the traditional barrier Lyapunov function (BLF) method, this article converts the USV's constraint model to a class of nonlinear systems without state constraints by introducing a nonlinear mapping. The control signal generated by the dynamic controller module consists of a backstepping-based feedforward control signal and an ADP-based approximate optimal feedback control signal. Therefore, the presented scheme can guarantee the approximate optimal performance. To approximate the cost function and its partial derivative, a critic neural network (NN) is constructed. By considering the event-triggered condition, the dynamic controller is further improved. Compared with traditional time-triggered control methods, the proposed approach can greatly reduce communication and computational burdens. This article proves that the closed-loop system is stable, and the simulation results and experimental validation are given to illustrate the effectiveness of the proposed approach.

Adaptive control for non-affine nonlinear systems with input saturation and output dead zone

In this article, an adaptive fault-tolerant control (FTC) method and a fractional-order dynamic surface control (DSC) algorithm are jointly proposed to deal with the stabilization problem for a class of multiple-input-multiple-output (MIMO) switched fractional-order nonlinear systems with actuator faults and arbitrary switching. In each MIMO subsystem and each switched subsystem, the neural networks (NNs) are utilized to identify the complicated unknown nonlinearities. A fractional filter DSC technology is adopted to conquer the issue of "explosion of complexity," which may occur when some functions are repeatedly derived. The common Lyapunov function method is used to restrain arbitrary switching problems in the system, and the actuator compensation technique is introduced to tackle the failure faults and bias faults in the actuators. By combining the backstepping DSC design technique and fractional-order stability theory, a novel NN adaptive switching FTC algorithm is proposed. Under the operation of the proposed algorithm, the stability and control performance of the fractional-order systems can be guaranteed. Finally, a simulation example of a permanent magnet synchronous motor (PMSM) system reveals the feasibility and effectiveness of the developed scheme.

In this article, the adaptive fixed‐time prescribed performance (FTPP) regulation is investigated for a class of time‐varying state constrained switched stochastic systems with input delay. The time‐varying barrier Lyapunov function and a compensation system are presented, respectively, to deal with the design problems caused by the existence of both time‐varying state constraints and input delay. Some radial basis function neural networks are used to approximate unknown functions, and the common Lyapunov function method is displayed to handle the switched signals. Besides, by designing a fixed‐time prescribed performance function, the desired adaptive neural controller is constructed. Compared with the existing works for state constrained control problem, the FTPP regulation control scheme is first proposed for time‐varying state constrained stochastic switched systems under input delay, and the adaptive dynamic surface control scheme with the nonlinear filter is designed to solve the problem of “explosion of complexity.” Based on the stochastic stability theory, the FTPP of system output is achieved, other system state variables are restricted in the predefined regions, and all signals of this closed‐loop system remain bounded in probability. Finally, the availability of the proposed control scheme is illustrated via two simulation examples.

The adaptive event-triggered fixed-time control for switched nonlinear systems with unmeasured states is addressed. Firstly, the state observer is constructed for switched nonlinear systems to estimate the unmeasured states, and the output-feedback approach is presented. Next, fuzzy logic systems (FLSs) are utilised to approximate the unknown functions, the novel event-triggered fixed-time control strategy is addressed based on common Lyapunov function (CLF) method and backstepping technology, which can greatly reduce the communication load. The asynchronous error compensation function is introduced into the designed controller and triggering mechanism, which effectively solves the asynchronous switching problem caused by the interaction of triggering and switching. Eventually, all closed-loop signals are assured to be bounded, the system output tracks the given reference trajectory at a fixed time, and the convergence time does not depend on the initial states of system. Two illustrative examples are shown to verify the effectiveness of the developed scheme.

This paper proposes a global tracking control method for underactuated ships with input and velocity constraints using the dynamic surface control (DSC) method, where the control structure is formed in a modular way that cascaded kinematic and dynamic linearizations can be achieved similarly as in the backstepping method. First, the first step linearization of the kinematics determines the pseudo (or auxiliary) surge velocity and yaw angle, which are used as the commands for the second-step linearization. Then, in the second-step linearization of dynamics, the actual torque inputs are designed to make the actual surge velocity and yaw angle follow these pseudo commands to achieve the position and yaw angle tracking. By employing the dynamic surface control method in the design of each kinematic and dynamic linearization law, we can obtain a control structure that is much simpler than the previous backstepping-based controllers such that it is beneficial from the practical application viewpoint. In addition, it is possible to track general reference trajectories, i.e., the reference yaw velocity need not be persistently exciting and there is no restriction on the initial yaw tracking error. In particular, global tracking control is achieved even in the presence of input and velocity constraints, unlike the DSC method which introduces the several filters in the backstepping design procedure to avoid the model differentiation and make it easier to be implemented and usually has semiglobal tracking performance. Finally, the stability analysis and numerical simulations are performed to confirm the effectiveness of the proposed scheme.

Fixed-time adaptive neural tracking control for high-order nonlinear switched systems with input saturation and dead-zone

Fully distributed dynamic event-triggered reliable COR of nonlinear MASs with communication link faults and actuator faults for directed graphs

An improved adaptive neural dynamic surface control for pure-feedback systems with full state constraints and disturbance

Adaptive output feedback dynamic surface control of nonlinear systems with unmodeled dynamics and unknown high-frequency gain sign

In this paper, robust adaptive control problem is discussed for a class of output feedback nonlinear switched systems with unmodeled dynamics and time-varying constraints. Based on the common Lyapunov function (CLF), an adaptive output feedback dynamic surface control (DSC) scheme is developed. Radial basis function neural networks are utilized to approximate unknown continuous functions, and K-filters and a dynamic signal are designed to estimate the unmeasured states and deal with the dynamic uncertainty. An asymmetric barrier Lyapunov function (ABLF) is introduced to handle output constraint. It is proved that the designed controller can guarantee the semi- global uniform ultimate boundedness (SGUUB) for all the signals in the closed-loop system under arbitrary switchings. The simulation results are provided to demonstrate the effectiveness of the proposed method.

To fully adapt to the distributed access of renewable energy, microgrid technology has been developed rapidly. Aiming at the coordination and efficient regulation of distributed resources in microgrid, this paper proposes a distributed autonomous economic control strategy for microgrid considering event triggering mechanism. First, a distributed autonomous economic control architecture is built to provide a distributed operation architecture for optimal regulation of the microgrid. Secondly, a distributed secondary control strategy based on the consensus control theory is established to realize the economic allocation of active power as well as safe and stable operation of the microgrid. On this basis, an event trigger protocol based on the consensus error of the control variables is constructed, which is conductive to reduce redundant communication. The stability of the event trigger protocol is deduced by means of Lyapunov function analysis. The simulation analysis based on the equivalent microgrid verifies that the proposed control strategy can reduce redundant communication and acquire fair distribution of reactive power and active power among DGs, realizing distributed, economical and safe operation of microgrid.

This paper presents a controller design method for dealing with the induced ℒ 2 -norm problem for switched linear parameter-varying (LPV) systems. Considering the arbitrary switchings caused by the parameters varying, a common parameter-dependent Lyapunov function is employed to derive sufficient linear matrix inequality (LMI) conditions for the switched LPV systems. A family of LPV controllers are designed according to the LMI conditions, and each of them is suitable for the corresponding parameter region. The proposed switching LPV control method is applied to an F-16 aircraft longitudinal model and simulation results demonstrate the effectiveness of the approach.

This article investigates the adaptive fuzzy distributed optimal event-triggered (ET) control problem for high-order nonlinear multiagent (NMA) system under weight-unbalanced directed graph. An optimal signal generator with ET mechanism is first established to estimate optimization point and optimal network channel utilization between agents and neighbors. Second, since the ET mechanism is introduced in optimal signal generator, the first-order derivative of optimal virtual signals is not continuous at the trigger instant, a high-order filter is established to estimate high-order derivatives of optimal virtual signals. Based on the optimal signal generator with ET mechanism and high-order filter, an adaptive fuzzy distributed optimal ET control scheme is proposed by backstepping control design technology. It is shown that controlled NMA system is asymptotically stable and the global cost function is minimized. Finally, we apply the proposed distributed optimal ET control approach to marine surface vehicle systems, the simulation and comparison results verify its effectiveness.