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Ensuring the stability of a closed-loop system and the exponential convergence of neural weights to their optimal values with rigorous analysis are challenging and significant problems in reinforcement learning (RL)-based control tasks, since precisely converged weights imply the acquisition of some accurate knowledge of the controlled object, which enables us to design a knowledge-based controller, observer, or planner. To address these issues, this paper combines recent advances in deterministic learning theory with a classical RL method, direct heuristic dynamic programming (HDP), to develop a novel direct HDP-based neural controller, and accurate modelling of unknown nonlinear dynamics in the control process can be achieved. The implementation involves the design of an actor-critic structure that transforms the state tracking and neural weight estimation problems into the stability problem of a class of linear time-varying systems. The exponential stability of the error systems is rigorously proved using Lyapunov’s direct method. During the learning process, RL is used to design an NN structure parameter adaptation scheme to achieve better tracking performance. Finally, the validity of the proposed scheme is verified by a series of simulation results.

This paper addresses the problem of fixed-time control and pre-described tracking performance design for time-varying uncertain nonlinear systems. Unlike the pure robust control, the proposed so-called nominal substitution method avoids unnecessarily high gain and noise amplification. The control problem for n-dimensional time-varying uncertain nonlinear systems is discussed to show how to combine the nominal substitution method with adaptive fuzzy fixed-time control and a singularity-avoidance virtual controller design. To achieve pre-described tracking performance in a fixed-time convergence environment, a projection operator-based adaptive algorithm and a series of symbolic-like functions are introduced in the control design. It is shown that the proposed control method can guarantee that the tracking error asymptotically converges to a user-defined accuracy within a pre-described fixed time. Two simulation results, including comparison experiments and analysis of practical examples, verify the effectiveness of the proposed method.

This paper studies the event-based decentralized adaptive finite-time tracking control problem of the interconnected nonlinear time-varying systems. A novel tracking control strategy associating event-triggered techniques, dynamic surface control, and finite-time control is presented. Correspondingly, the newly designed controller not only ensures finite-time convergence but also decreases the communication burden between the controller and the actuator. Moreover, the complexity explosion problem caused by the backstepping design procedure can be excluded. In addition, the difficulty caused by the system uncertainty is solved by utilizing bound estimation methods and constructing a suitable smooth function. Simulation results verify the effectiveness of our proposed control strategy.

This paper deals with the global asymptotic stability of the zero solution of a certain class of incommensurate nonlinear fractional time-varying systems. By following the fractional comparison approach, two distinct new theorems are developed for the analysis of such class of systems where the state equations are associated with different fractional orders in the range 0–1. Based on these theorems, several new results are put forward for testing the asymptotic stability of such class of systems. Finally, three illustrative examples are provided where it is shown that some proposed asymptotic stability conditions are practically convenient for the analysis and control of such systems.

Space−time modulated metamaterials support extraordinary rich applications, such as parametric amplification, frequency conversion, and non-reciprocal transmission. The non-Hermitian space−time varying systems combining non-Hermiticity and space−time varying capability, have been proposed to realize wave control like unidirectional amplification, while its experimental realization still remains a challenge. Here, based on metamaterials with software-defined impulse responses, we experimentally demonstrate non-Hermitian space−time varying metamaterials in which the material gain and loss can be dynamically controlled and balanced in the time domain instead of spatial domain, allowing us to suppress scattering at the incident frequency and to increase the efficiency of frequency conversion at the same time. An additional modulation phase delay between different meta-atoms results in unidirectional amplification in frequency conversion. The realization of non-Hermitian space−time varying metamaterials will offer further opportunities in studying non-Hermitian topological physics in dynamic and nonreciprocal systems. Exquisite control of loss and gain in non-Hermitian systems allows waves to propagate in unusual and useful ways. Here, unidirectional amplification is achieved in an acoustic metamaterial by dynamically varying the gain and loss with modulation phase delay between different metaatoms.

A model reduction approach is presented for discrete-time linear time-variant input-delayed systems. According to this proposed approach, a dynamical variable is constructed by taking advantage of the current state and historical information of input. It is revealed that the behavior of this dynamical variable is governed by a discrete-time linear delay-free system. It is worth noting that the presented variable transformation does not require the system matrix to be invertible. Based on the reduced delay-free models, stabilizing control laws can be easily obtained for the original delayed system. For the case with a single input delay, the constructed variable is an exact prediction for the future state, and thus the stabilizing control law could be designed by replacing the future state with its prediction. Finally, three discrete-time periodic systems with delayed input are employed to illustrate how to utilize the presented model reduction approaches.

Due to high compressibility, small viscosity, low damping and stiffness, nonlinear friction, and valve port flow, pneumatic servo control systems are powerful nonlinear time-varying systems, making it difficult to guarantee their control accuracy, stability accuracy, and robustness. Digital pneumatic servo systems for independent metering control have been proposed to overcome these problems. In this paper, methods for achieving sufficient valve flow, nonlinear factor modeling, state estimation and compensation, and compound robust control strategies for these systems are summarized. Nonlinear factors include friction, pipeline loss, and cushioning. Furthermore, future research into digital pneumatic servo control technology is discussed based on the summarized research work and achievements of relevant scholars in pneumatic servo nonlinear control technology. Compared with traditional proportional servo control, the digital pneumatic control that is the focus of this paper has the advantages of low cost, simple control, and energy-savings, and it is highly consistent with the needs of Industry 4.0.

The cabin pressure control system is typically nonaffine-nonlinear time-varying system. In this paper, the fuzzy PID control method optimized by gray wolf algorithm is proposed to solve the problems of large overshoot and long adjustment times in traditional control methods. Simulation results show that the fuzzy PID control method optimized by gray wolf algorithm proposed in this paper improves the dynamic characteristics of the system, reduces cabin pressure error and adjustment time, stabilizes the control process, and improves the comfortableness of passengers.

For a long time, the accurate prediction of passenger flow can provide early warning information for various industries such as the public service industry, tourism industry, and industrial business, thus opportunely arranging passengers and providing homologous services to relieve the overloading of places and the accidents caused by overcrowding of people. In recent years, by using the wireless sensor network to sense the passenger data in advance, the technique of machine learning and neural networks has been utilized to assist the short‐term passenger flow prediction. In this study, building on convolutional neural network (CNN) and long short‐term memory network (LSTM), a complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and attention‐based CNN‐LSTM network to extract both temporal and spatial characteristics of passenger flow data, is proposed. Moreover, the problem of the inaccuracy of the noise part is properly solved by adding the CEEMDAN algorithm to the input layer. With the proposed network structure, the CNN‐LSTM network is replaced with the Conv‐LSTM network to reduce the information loss and get a further performance improvement. The result shows that 39% performance improvement can be achieved than the case with a single LSTM network, and 28% performance improvement can be achieved than the CNN‐LSTM network.

With the rapid development of power electronic technology and the permanent magnet industry, Permanent Magnet Synchronous Motor (PMSM) is widely used in high-tech industries such as aerospace and transportation with excellent performance. However, PMSM is a time-varying system with multiple variables and strong coupling, making its control strategy rather complex. It is becoming increasingly important to improve the control strategy. Addressing the issues of easy saturation, poor robustness in PI controllers, and the inconvenience of injecting third-harmonic from the outside into the three-phase three-leg topology to enhance torque density, which leads to insufficient control precision in the PMSM servo system, a nonlinear active disturbance rejection controller (NLADRC) is proposed as the control strategy for the third-harmonic injection-based field-oriented control (FOC). Compared to the PMSM servo system without the injection of third-harmonic, the proposed system exhibits better disturbance rejection capability and higher torque density. Through simulations of the PMSM servo system with the injection of third-harmonic, the proposed control strategy is verified to have good response speed and control performance.