Asian Journal of Control最新文献

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.

A novel event-triggered heuristic dynamic programming (HDP) algorithm is proposed for the near-optimal control of uncertain discrete-time nonlinear input-affine systems. Based on input-to-state stability (ISS) analysis, a new event-triggered mechanism (ETM) is designed. Under constant coefficients, a Lipschitz-like assumption that forms the basis of the event-triggering condition is considered to be conservative. To further reduce the conservativeness of the triggering condition and enlarge the average interevent time, an adaptive threshold parameter is utilized in the proposed ETM. In the HDP algorithm framework, model, critic, and action network are adopted to achieve state estimation, approximation to the optimal cost function, and solution to Hamilton–Jacobian–Bellman (HJB) equation. Under the proposed event-triggered HDP algorithm, the closed system is proved to possess semiglobal uniform ultimate boundedness (SGUUB). Finally, by conducting simulation, it shows that on the premise of satisfying control performance, the event-triggered strategy can realize reduction on the updating frequency of the controller.

In this paper, a novel controller designed for robust tracking control of a flexible-link manipulator operating in the presence of parameter uncertainties and external disturbances within the joint space is introduced. The proposed controller employs an adaptive sliding mode control approach, incorporating an improved barrier function, to ensure that trajectory errors remain within predefined performance bounds. This design enhances the tracking performance without overestimating control-switching gains. Additionally, a fixed-time adaptive sliding mode control, featuring a rapid nonsingular terminal sliding mode variable, is introduced to expedite the convergence rate of the system state during the initial stages. The efficacy of the proposed control scheme is established through the Lyapunov method, demonstrating finite-time convergence of the trajectory error to a specified neighborhood of zero. Experimental validation on a flexible-link system supports the effectiveness and advantages of the proposed control strategy, as evidenced via comparisons with two existing adaptive control schemes.

In this paper, a finite-time functional observer design for linear time-invariant descriptor systems is presented. A generalized observer based on additional degrees of freedom is developed. This observer can be considered of a general structure since the existing proportional observer (PO) and proportional-integral observer (PIO) constitute its particular cases. The main contribution is to generate a finite-time convergent functional observer for descriptor systems by combining two generalized functional observers. The obtained results are applied to the simultaneous state, sensor, and actuator faults estimation. A numerical example is given to illustrate the approach. The obtained results show that the observer is robust in presence of uncertainties, compared to its particular cases.

This article focuses on the fault detection filtering problem for Itô stochastic Takagi–Sugeno (T-S) fuzzy systems. In terms of line integral Lyapunov function, a sufficient condition of exponential stability in mean square and extended dissipativity for the systems under consideration is established which has less conservatism than the one based on quadratic Lyapunov function. The obtained sufficient condition is nonlinear, which makes the synthesis of fault detection filter being difficult and challenging. By choosing general matrix variables and constructing the appropriate orthogonal complement matrices, the nonlinear sufficient condition can be converted into linear one ingeniously via utilizing Finsler's lemma twice. Thus, the fault detection filter can be developed by virtue of parameterization. It should be pointed out that our filter determined by the parameterization approach includes the one obtained by the equivalent transformation method as a special case. Finally, we demonstrate the feasibility and superiority of our proposed approach through three numerical examples.

In view of the uncertainties, partial actuator faults, asynchronous switching as well as unknown external interference in multi-phase batch processes, a robust predictive minmax fault-tolerant control approach is presented. First, considering that a controller and a system state cannot switch synchronously during switching, a match-and-mismatch switching model is constructed. Then, an output tracking error is introduced to establish an equivalent extended switching model. In this extended model, the robust control issue is transformed into the minmax optimization issue by establishing the minmax performance index. On this basis, the control laws with control input and interference input are designed. Second, sufficient conditions are given to assure the system separately has asymptotic and exponential stabilities in each phase and batch. By calculating the given sufficient conditions online, the gains of the control laws, and the minimum and maximum operating time under match and mismatch cases are obtained. With the maximum operating time, the controller receives the switching signal beforehand to eliminate the mismatch case of switching. Additionally, a robust criterion is supplied to confirm the system's robustness. Finally, a simulation study of an injection molding process shows that the approach is valid and feasible.

This paper investigates the exponential stability in mean square sense for a class of stochastic switched semi-Markov jump systems (ssSMJS). In this model, a hierarchical control switched regulation is designed, considering the external stochastic disturbances and generally uncertain transition rates (TRs) of the entire system and making the system more general and practical. By using appropriate Lyapunov–Krasovskii functional, linear matrix inequality, Jensen inequality, and mode-dependent average dwell time (MDADT), sufficient conditions on the exponential stability in mean square sense for ssSMJS are derived. Furthermore, a nonfragile state feedback controller is designed. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed criterion.

This paper introduces a novel control scheme for the permanent magnet synchronous motor (PMSM) position servo system using a fractional-order active disturbance rejection control (ADRC) approach. Specifically, a first-order ADRC is employed in the current loop, while a fractional-order ADRC is designed for the position loop. In the latter, a linear extended state observer (LESO) is utilized to estimate and compensate for disturbances, while a fractional-order proportional derivative (FO[PD]) feedback controller is employed to enhance the control performance. Moreover, a parameter tuning method is presented based on frequency domain specifications. This technique utilizes the flat phase constraint as a design constraint to ensure that the phase is flat near the gain crossover frequency, thus ensuring that the closed-loop system remains robust to loop gain variations. Simulation and experimental results demonstrate that the proposed fractional-order ADRC control scheme yields improved dynamic response, disturbance rejection, and robustness to loop gain variations.

This paper presents a feedforward control algorithm that combines the benefits of optimal iterative learning control (OILC) and model-based feedforward control (MFC) using iterative feedforward tuning and input shaping filter (IFT-ISF) for industrial motion systems. OILC effectively compensates for tracking errors in repeating tasks under actuator constraints. However, its performance deteriorates when the trajectory changes. In contrast, MFC can achieve high performance for varying trajectory tracking tasks, but its performance may degrade for constrained systems if the control force exceeds the actuator saturation boundary. The proposed algorithm aims to overcome these limitations to achieve optimal trajectory tracking performance for varying trajectories under actuator constraints. Simulation and experimental results demonstrate that the proposed algorithm achieves optimal tracking performance while complying with the actuator constraints. The algorithm provides a data-driven approach without requiring the tedious process of model identification. By combining the benefits of OILC and IFT-ISF, the proposed algorithm can achieve high-performance trajectory tracking for both repeating and varying tasks under actuator constraints, making it suitable for industrial motion systems.