Asian Journal of Control最新文献

The convergence and stability of uncertain nonlinear systems is a challenging problem in the nonlinear control area. Besides, in many practical cases, all states are not measurable and are affected by measurement noise. Based on this motivation, the first objective of this paper is to design a novel output feedback robust model predictive control approach for nonlinear systems with state- and input-dependent uncertainties and measurement noise. This approach combines state estimation and robust model predictive control (MPC) into one min–max optimization and by solving the optimization, these two tasks are performed simultaneously. The studied nonlinear system comprises a linear part, a nonlinear part, and a function that denotes the state- and input-dependent uncertainties. Therefore, the other objective is to reduce the computational complexity; thus, the system's nonlinear term and the aforementioned uncertainties are converted into additional disturbances. Subsequently, the optimization problem becomes a quadratic form, which leads to global convergence with the appropriate selection of objective function weights. Besides, this paper explores the convergence of the closed-loop system states and the sufficient synthesis conditions to guarantee input-to-state stability. The implementation on a numerical example and a CSTR process demonstrate the applicability and reliability of the proposed approach.

This paper introduces mix-zero-sum differential (MZSD) game theory to address multi-player tracking systems, offering a better understanding of the coexistence of cooperation and competition among players. Within this framework, we present an optimal safety tracking control (OSTC) method, which incorporates a control barrier function (CBF) into the value function to ensure that the tracking error remains within a specified range, thus guaranteeing safety while achieving optimization. Simultaneously, to eliminate the need for system dynamics, we propose a novel approach leveraging off-policy integral reinforcement learning (IRL) technology to obtain the Nash equilibrium solution of the MZSD games. We establish a unique critics–actors neural network (NN) structure that updates concurrently. Furthermore, we analyze stability and convergence using the Lyapunov method. We conduct two simulations to demonstrate the effectiveness of the proposed algorithm.

This paper investigates the problem of robust coordination control based on interval observers for multi-agent systems in the presence of input saturation and disturbances. Firstly, in the scenario where the system state is not directly measurable, two types of interval observers are, respectively, constructed by resorting to the upper and lower bounds on external perturbances as well as the output information. Secondly, a parametric Lyapunov equation-based low-gain feedback control method is proposed to guarantee semiglobal bounded consensus. In contrast to the conventional approach based on parametric algebraic Riccati equations, the proposed method offers the advantage of allowing the parameters to be determined in advance. Finally, a simulation example and a practical electrical circuit model are conducted to verify the theoretical results.

State-based games contain an additional state space by comparing with normal games. Correspondingly, the equilibrium of state-based games is called recurrent state equilibrium (RSE). For state-based games, the stochastic convergence to RSE is investigated in this paper. Firstly, the stochastic convergence of state-based games is defined. Then, a kind of state-based best-response update rule is designed for state-based games. Under this update rule, the state-based games can be converted into the Markovian switching logical networks through the semi-tensor product. Next, based on the results of Markovian switching logical networks and positive systems, the stochastic convergence of state-based games is investigated and a verifiable criterion is derived. Finally, an example is presented to illustrate the validity of the obtained criterion.

The proportional-integral-derivative (PID) was developed and recognized for its reliability. A PID controller is not only simple but also relatively cheap. However, the controller causes system performance degeneration over time due to the presence of windup in a motor speed control system. The windup phenomenon is caused by the saturated control state. Various anti-windup methods were introduced to decrease a system's long settling time and extreme overshooting. Most anti-windup techniques require integral switching between saturated and unsaturated states, whereby both versions of steady-state integral proportional-integral controller do not need integral switching mechanism. They possess a certain degree of decoupling between $��{�k�}_{�p�}�$ and $��{�k�}_{�i�}�$ tuning parameters and tested to allow a more comprehensive range of tuning in the absence of derivative control. This research investigated the impact of derivative control component on the tuning gain decoupling through hardware simulation. By integrating the derivative component, $��{�k�}_{�d�}�$, the control system demonstrated an improved system stability and reduced overshoot. Result shows that the decoupling feature allows SIPIC01+D and SIPIC02+D controllers to produce performance with zero overshoot and short settling time. However, SIPIC01+D has better dynamical performance with fastest rise and settling time with no overshoot as compared to other anti-windup controllers.

Mechatronic systems with nonlinear dynamics are met in motion transmission applications for vehicles and robots. In this article, the control problem for the nonlinear dynamics of mechatronic motion transmission systems is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these systems is separated into a series of subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system, and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. In this chain of $��i�=�1�,�2�,�\dots �,�N�$ subsystems, the state variables of the subsequent ($��i�+�1�$)-th subsystem become virtual control inputs for the preceding $��i�$-th subsystem and so on. In turn, exogenous control inputs are applied to the last subsystem and are computed by tracing backwards the virtual control inputs of the preceding $��N�-�1�$ subsystems. The whole control method is implemented in successive loops, and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in the following two case studies: (a) control of a permanent magnet linear synchronous motor (PMLSM)-actuated vehicle's clutch and (ii) control of a multi-Degrees of Freedom (multi-DOF) flexible joint robot.

This paper studies the stability of stochastic nonlinear time-delay systems with arbitrary switching and its application. A novel Krasovskii-type stability theorem is introduced for stochastic nonlinear systems featuring arbitrary switching and time-delay. Unlike previous results, this stability result removes the restriction that the infinitesimal generator of Lyapunov–Krasovskii functional (i.e., $��L�V�$) must be negative definite and allows $��L�V�$ to be indefinite. As an application, the tracking control is studied for a class of stochastic switched systems with inverse dynamics and time-delay. The assumption condition imposed on the inverse dynamic subsystem is weaker compared with most of existing findings. Finally, we illustrate the feasibility and effectiveness of proposed control strategy by two simulation examples.

This paper investigates the stabilization problem for cyber-physical systems subject to positive constraint and denial-of-service (DoS) attacks using the event-triggered scheme. Unlike traditional modeling of cyber physical systems with DoS attack, this paper establishes a positive switched system model composed of two positive subsystems that according to the DoS attack is active or inactive. Considering the effect of positive constraint and DoS attack, a 1-norm-based event-triggered mechanism is proposed in the sleep interval of DoS attacks. By incorporating switching theory with event-triggered mechanism, the sufficient condition of positive property and exponential stabilization for the considered system is established. Thereafter, the gain matrix of the controller is calculated by matrix decomposition technique. Furthermore, the Zeno behavior is eliminated with a lower bound of the event-triggered interval. Finally, an example is carried out to verify the validity of the theoretical results.

This paper focuses on the optimal output-feedback control and stabilization of a networked control system, where both time delay and packet loss are involved. Different from most previous work, the packet loss is Markovian, and the time delay occurs between the actuator and controller. The difficulty is rooted in the failure of the separation principle. The main tools are solving the delayed forward-backward stochastic difference equations (D-FBSDEs) and convergent analysis. Resorting to solving D-FBSDEs and completing the square, we acquire the analytical solution, including the necessary and sufficient solvability condition and the explicit controller, of the finite-horizon optimal control problem. Based on the finite-horizon result and convergent analysis, we then obtain the analytical solution of the infinite-horizon optimal control and stabilization problems. In addition, by analyzing the convergence of the optimal estimator, we establish a constraint relationship between the packet loss probability $��q�$ and the system matrix $��A�$. The results are evaluated by numerical examples.

This paper is concerned with the finite-time tracking control problem of fractional-order nonlinear systems (FONSs) with uncertainty and external disturbance. A novel design scheme of the adaptive neural network finite-time controller (ANNFTC) is developed by utilizing the theory of finite-time stability and fractional-order dynamic surface control (DSC) scheme combined with backstepping method. Radial basis function neural networks (RBF NNs) are employed to estimate the unknown nonlinear function. Furthermore, an auxiliary function is introduced to approximate the unknown upper bounds of the approximation error in RBF NNs and external disturbance. The ANNFTC ensures the finite-time boundedness of all signals in FONSs and enhances the system output's tracking performance. The effectiveness of the proposed approach is demonstrated through a simulation example, providing empirical evidence to support the theoretical framework presented in this paper.