IET Control Theory and Applications最新文献

This paper develops a matrix-separation-based Lyapunov functional method to study the extended dissipativity analysis and synthesis issue of discrete-time Lur'e-type delayed systems. The advanced idea of matrix-separation is reflected in Lyapunov functional candidates and estimates summation terms as precisely as feasible. First, we introduce a novel summation inequality based on the matrix-separation method to provide a bound for the augmented summation that involves common state variables. An improved delay-product-type Lyapunov functional is devised by extending non-positive definite summations as separate subblocks of the state-augmented summation. Then, by using the matrix injection method to handle the cubic delay term in the functional forward difference, a delay-variation-dependent stability criterion and an extended dissipativity criterion are developed under the matrix-separation-based method. Subsequently, sufficient conditions for the control design of Lur'e-type systems are derived. Finally, the effectiveness and superiority of the proposed method are verified through its application to Chua's circuits, neural networks, and a stochastic numerical example.

This brief studies the problem of dynamic output feedback (DOF) asynchronous $�H_{\infty }$ control for singular Markovian jump systems (SMJSs) via event-triggered scheme (ETS). To save network resources, a dynamic ETS is proposed. Moreover, due to various factors, the controller modes and the system modes are not always synchronized and a hidden Markov model (HMM) is used to describe this phenomenon. First, some conditions are formulated to ensure that the system is stochastically admissible (SA) with $�H_{\infty }$ performance. Second, DOF controllers are obtained based on the derived conditions. Finally, two examples are presented to show the effectiveness of the proposed approach.

The frequency synchronisation problem of wide-area control from the higher level of networked power systems is considered. We investigate the asymptotic convergence and robustness of the passivity-based distributed averaging control used in damping the inter-area oscillations of power networks and coping with the time-varying power demand. A strict composite storage function is constructed that allows us to quantify the asymptotic convergence rate of the closed-loop system. Furthermore, the power sharing and $�L_2$ gain performance are utilized as a tool to quantify the impact of time-varying power demand on control areas. We simulate the controller performance on a four-area network which is equivalent to the IEEE New England 39-Bus system.

This paper investigates strategies for achieving optimal output synchronization of heterogeneous multi-agent systems in the presence of false data injection attacks. We formulate a performance index with an infinite time horizon using a zero-sum game framework, treating control input and false data injection attack input as two opposing players. Specifically, the control input's objective is to minimize the performance index, while the false data injection attack input aims to maximize it. Adhering to the optimality principle, we derive the optimal control policy, contingent upon the solution to a related algebraic Riccati equation. Moreover, we propose sufficient conditions that ensure the existence of a solution to the algebraic Riccati equation. Additionally, we have devised a data-driven reinforcement learning algorithm to seek the solution, and its convergence is assured. Furthermore, it has been demonstrated that the solution to this game corresponds to a Nash equilibrium point. Finally, the validity of the proposed methodology is substantiated through simulation results.

Additive metal manufacturing (AM), particularly Wire Arc Additive Manufacturing (WAAM), offers a compelling alternative to traditional machining methods. While AM presents advantages such as reduced material waste and lower production costs, challenges remain in effectively controlling the process to prevent defects and optimise material deposition. This article proposes a multivariable control system for WAAM utilising Quantitative Feedback Theory (QFT) to maintain the shape of the heat-affected zone (HAZ) during transitions in direction flips during layer deposition. By modelling these direction flips as predictable disturbances, the full potential of QFT to integrate feedback and feedforward actions is exploited. The resulting multivariable control laws seek to minimise temperature variation in two critical points around the welding pool by adequately manipulating the power and speed of the heat source. A benchmark system is established to evaluate the effectiveness of the proposed control system. The results demonstrate significant improvement in temperature control, leading to enhanced layer construction quality and reduced need for height corrections or cooling pauses.

This article investigates the finite-time event-triggered controller design with minimum learning parameters (MLP) for nonlinear systems using neural networks in the presence of uncertainty. Specifically, firstly, the neural networks are devised to compensate online for the uncertain nonlinear functions. Then, a finite-time prescribed performance function is employed in the controller design to achieve that the tracking error converges to within a prescribed region at any setting time. At the same time, the transient responses (e.g., maximum overshoot and convergence speed) can be enhanced for the tracking error. After that, unlike ordinary dynamic event-triggered strategy, the developed dynamic event-triggered methodology can further increase the triggering interval, which leads to the network bandwidth can be effectively saved. Moreover, one can prove that all the closed-loop signals remain bounded and the Zeno phenomenon can be excluded. Finally, the advantages of the proposed strategy can be illustrated by two examples.

The objective of this paper is to investigate an event-triggered output feedback model predictive control (MPC) approach for the nonlinear cyber-physical system (CPS) with a stochastic communication protocol (SCP) scheduling, which is approximated by an interval type-2 Takagi–Sugeno (IT2 T-S) fuzzy model. For the objective of enhancing network communication efficiency and relieving data collision caused by limited communication resource, an SCP protocol ruled by a Markov stochastic process is favourably utilized to govern the data scheduling of network. Based on an event-triggered output feedback control law, a mode-dependent IT2 fuzzy controller is formally designed, in which the feedback gain is optimized by solving an online constrained MPC optimization problem. By the utilization of defining the mean-square quadratic boundedness (MSQB) for confining the augmented system state into a robust invariant set, both the feasibility of controller and closed-loop stochastic stability are ensured and proved with the satisfaction of physical constraint in the mean-square sense. Finally, we validate the effectiveness of the proposed method by a numerical simulation example.

The difference in wheel speeds within a train carriage arises from variations in traction motor performance and rail adhesion conditions. This can potentially lead to uneven wheel wear and, subsequently, to imbalanced traction and unstable train operation. To tackle this issue, this paper proposes a control method based on fixed-time synergetic control theory to synchronize the linear speeds of wheels in a multi-interior permanent magnet synchronous motor (IPMSM) traction system. The method considers load differences caused by wear differences between the front and rear wheels, as well as the dynamic adhesion conditions of the rail. First, the model of the permanent magnet synchronous traction system (PMSTS) is established by combining the single-axle train model with the dynamic model of the IPMSM. Then, synergetic control theory is extended with fixed-time theory to ensure the convergence performance of the PMSTS under any adhesion condition. Furthermore, a new synergetic load torque observer is designed to estimate the motor-side load torque, with the observed information used to track maximum adhesion coefficient. Finally, the proposed method is validated for its effectiveness and advantages through a hardware-in-the-loop platform.

This paper investigates the multivariable control of wastewater treatment processes (WWTP). This paper integrates deep reinforcement learning (DRL) with PID control and proposes a multivariable adaptive PID control strategy based on multi-agent DRL (MADRL) for WWTP. The approach begins with the construction of a MADRL-PID controller structure, consisting of an agent and a PID controller module. The agent adjusts the PID controller values while the PID module calculates the control signal. To enhance the agent's ability to cooperatively tune multiple PID controllers, the algorithm's components–reward function, action space, environment, and state space–are designed according to the BSM1 simulation platform principles and the MADRL framework requirements. Additionally, to handle WWTP's non-linearities, uncertainties, and parameter coupling, the multi-agent deep deterministic policy gradient algorithm is selected as the foundation for training the agents. Experimental results demonstrate that the proposed algorithm exhibits greater adaptability than traditional PID control and achieves superior control performance.

This paper presents an $�H_{\infty }$ optimal tracking control approach for linear systems with unknown models and input constraints. The proposed method is based on data-based adaptive dynamic programming (ADP) that is computationally tractable and does not require model approximation. This study consists of two new algorithms: a model-based constrained control algorithm and a data-based algorithm for systems with completely unknown models. A lower bound for the $�H_{\infty }$ attenuation coefficient is determined to ensure optimality. Additionally, the approach allows for constraints on the amplitude and frequency of the control signal, which are incorporated using the idea of inverse optimal control (IOC). The effectiveness of the proposed method is demonstrated through a simulation example, showcasing its ability to achieve robust tracking performance and satisfy input constraints.