International Journal of Robust and Nonlinear Control最新文献

This article proposes a novel approach to design a robust estimator that is able to keep its consistency in system state estimation when system process model mismatch occurs. To successfully develop such an estimator, not only the estimation strategy proposed but also the designer's knowledge and experience about the system behavior are crucial and determining. To assess the performance of the resultant estimator, its performance is compared with that of three well-known estimators, that is, the unscented Kalman filter, the cubature Kalman filter, and the extended Kalman filter on the IEEE 5-generator 14-bus system. The results indicate that the proposed method has led to an estimator outperforming its rivals under the presence of model errors.

In this paper, we develop an integral reinforcement learning (IRL)-based trajectory tracking control (TTC) scheme via firefly optimized neural networks for partially unknown multiplayer nonlinear systems. Under the developed TTC scheme, IRL is proved to be equivalent to the classical policy iteration, which guarantees the convergence of the IRL algorithm. By implementing the IRL method, the requirement of the drift dynamics is obviated. The TTC policy for each player is obtained by solving the coupled Hamilton–Jacobi equation with a critic neural network, whose weight vector is tuned by the firefly algorithm. The tracking error of the closed-loop system is guaranteed to be stable via the Lyapunov's direct method. Simulation results illustrate the effectiveness and superiority of the present IRL-based TTC scheme, and show that the success rate of system operation is increased by introducing the firefly algorithm.

A neural network adaptive integral terminal sliding mode control method with input saturation is proposed for the horizontal vibration problem of high-speed elevator car systems caused by uncertainties such as guideway excitation and shaft piston wind change. First, considering the input saturation problem existing in the elevator control actuator, a class of smooth functions is introduced to approximate the nonlinearity of switching saturation, and an eight-degree-of-freedom asymmetric anti-saturation nonlinear system model of the high-speed elevator car is established; second, in order to solve the singularity problem existing in the terminal sliding-mode control, a nonlinear term is introduced into the sliding-mode design, and a neural network is utilized for the fitting of the complex unknown function, and the design of the an adaptive integral terminal sliding mode controller (AITSMC), which enables the state variables of the system to achieve finite time convergence and proves the stability of the system by using Lyapunov theory; finally, under the action of two typical guide excitations, the proposed controller is compared with the passive control and adaptive control (AC), and the results show that, after adopting the proposed control method, the vibration acceleration eigenvalue is reduced by more than 60%, which effectively suppresses the horizontal vibration of the car system and verifies the effectiveness of the proposed controller.

Based on the Roesser model, the asynchronous sliding-mode control (SMC) problem for two-dimensional (2-D) Markov jump systems (MJSs) with time-varying delays is studied in this article. Since the controller mode cannot always access the system mode, the asynchronous phenomenon between the system mode and the controller mode will be described by the hidden Markov model (HMM). First, under the framework of HMM, the asynchronous 2D-SMC law is designed based on 2-D sliding surface function to ensure closed-loop 2-D discrete MJSs asymptotic mean square stability and the reachability of sliding regions around specific sliding surfaces. Second, an algorithm for deducing the law of 2-D asynchronous SMC is presented. Finally, a numerical example is given to verify the effective of the results.

In this article, we consider the equations of motion for an articulated $��n�$-trailer vehicle with different masses and inertias in the presence of force and torque commands. We design a second-order optimal filter to estimate the pose of the first vehicle and of the trailers exploiting the Lie group structure and the nonholonomic and hooking constraints. We consider the filtering problem from three different perspectives: in the first masses and inertias are time-varying and perfectively known, in the second they are known only in the first part of the maneuver, but they are not updated over time. In the last case masses and inertias are considered within the state vector and therefore estimated by the filter. Depending on the needs in real-life applications (e.g., whether the masses and inertias remain fixed or change during the maneuver and are unknown), the best-performing filter can be used. The sensing system consists of a GPS-like configuration (global positioning system) obtained by using an antenna attached to the leading car.

This paper focuses on the study of the prescribed-time bipartite time-varying formation tracking (BTVFT) problem for multiple heterogeneous Euler-Lagrange systems (HELSs) with external disturbances under directed signed graphs. Considering the fact that the states of leader cannot be accurately obtained, a set of observers which can estimate the states of the leader within a prescribed time, is firstly established according to the time-varying function. Subsequently, a prescribed-time distributed control protocol is proposed by employing a terminal sliding surface, aiming to achieve the BTVFT under digraphs within a prescribed time. The theoretical proof utilizes suitable Lyapunov functions to demonstrate that the control law can guide the state trajectories of each follower to the corresponding terminal sliding surface within a predefined time, thus ensuring the solution to the BTVFT problem of HELSs. Finally, simulation results are provided to validate the effectiveness of the derived findings.

This article is dedicated to event-triggered anti-disturbance control of networked control systems via a guaranteed cost triggering scheme. A distribution interconnected observer structure is built to estimate these unknown matched disturbances. A novel event triggered condition is constructed in terms of real-time cost and averaged functions composed of observer states and the last successfully transmitted one. A composite control scheme is provided by an event-triggered observer-based state feedback controller and compensators for the matched disturbances. A sufficient condition in term of linear matrix inequality is supplied to ensure the asymptotic stability of the resultant closed-loop system. A simulation is carried out to demonstrate the effectiveness of the proposed triggering control strategy.

This article aims to achieve scalable exact output and regulated output synchronization for discrete-time multi-agent systems in presence of disturbances and measurement noise with known frequencies. Both homogeneous and heterogeneous multi-agent systems are considered, with parts of agents' states accessible in the latter case. The key contribution of this article is to establish scalable exact synchronization results under disturbances and measurement noise, which is achieved by using distributed protocols that only use information about agent models and no information about the communication network or the number of agents. The validity of the protocol is verified by numerical simulations with arbitrarily chosen number of agents.

This paper investigates the practical prescribed time tracking for a class of uncertain nonlinear systems based on neural networks and event-triggered control. Introducing a time-varying constraint function transforms the original practical prescribed time-tracking control issue into a tracking error constraint problem. An event-triggered adaptive control has been proposed, which can effectively reduce the communication burden between the controller and the actuator. Using neural networks to approximate unknown nonlinear functions avoids the differentiation of virtual controllers, thereby reducing the computational burden. In addition, users can independently choose preset time and tracking accuracy without changing the control structure, which remains independent of the initial conditions and any design parameters. Finally, the effectiveness of this method is verified through simulation examples.