IET Control Theory and Applications最新文献

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.

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.

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 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.

Considering the importance of solder paste printing in the production process of surface mounted technology (SMT), as well as the decisive impact of key process parameters on the solder paste printing effect. Traditional methods, whether manual or machine tuning, suffer from significant production capacity losses due to long downtime, and machines cannot adaptively adjust parameters based on human expert knowledge, thereby affecting the qualification rate of solder paste printing and the efficiency of SMT production lines. This paper proposes a human–machine integration optimization method for key printing process parameters. By establishing a printing quality prediction model and a key process parameter strategy model, a closed-loop control system has been formed to achieve machine autonomous parameter tuning with expert knowledge. And this paper has completed the establishment of the strategy model based on deep reinforcement learning methods, enabling the SMT production line to predict and adjust key process parameters in real time based on SPI data. In addition, the optimization method described in this paper retains the final decision-making authority of human operators to ensure emergency correction of prediction bias and decision failure history in the system. The final experimental results of this paper indicate that the proposed optimization method performs well in terms of qualification rate, correction effect, SPI data prediction, etc. These demonstrate the effectiveness and value of the proposed human-on-the-loop optimization method in SMT production lines.

As the demand for various practical applications continues to increase, challenges such as time consumption have compromised the real-time capabilities of formation agents. Model predictive control (MPC) is known for its computational complexity, which can result in synchronisation issues among followers and leaders. In this study, we propose a dual-layer formation control strategy. The upper layer focuses on trajectory planning and collision avoidance, utilising MPC and control barrier functions to derive the desired velocities. Within the MPC framework, this approach simplifies the control of second-order systems—incorporating both trajectories and velocities—into first-order systems that only require trajectory management. In the lower layer, we establish a new predefined-time leader-follower formation control for multiple vessels, designed to achieve the desired velocity. The proposed method is validated through simulations involving multiple unmanned surface vessels.

In safety-critical control systems such as autonomous vehicles and medical devices, managing the risk of rare but severe tail events under uncertainty is crucial. This paper addresses this challenge by proposing a risk-aware control framework that integrates the worst-case conditional value-at-risk (CVaR) with control barrier functions (CBFs). Specifically, we formulate risk-aware safety constraints based on the worst-case CVaR, and show that the resulting risk-aware controllers can be computed via quadratic programs (for half-space and polytopic safe sets) or a semidefinite program (for ellipsoidal safe sets). Numerical simulations on an inverted pendulum illustrate that the proposed approach ensures safety under various scenarios and significantly reduces the safety constraint violation compared to existing CBF approaches. Overall, we show that incorporating worst-case CVaR into CBF design offers a tractable solution for safety-critical applications under uncertainty.

This article solves dominance regions for boundary-guarding games based on optimal control, where autonomous vehicles with rotation constraints serve as defenders to guard the target zone. Based on the definition of transition point, the minimum reach time is explicitly expressed in unbounded and convex domains, respectively. Using the proposed explicit expression of minimum reach time, this article develops a numerical algorithm to generate dominance regions for boundary-guarding games. Finally, simulation results are provided to verify the algorithmic validity to generate dominance regions for rotationally-constrained autonomous vehicles.