European Journal of Control最新文献

The embedded closed-loop in integrated systems can be compromised when attackers execute a successful malicious attack. Hence, over the past decade, interest in enhancing the safety of Cyber-Physical Systems (CPSs) has risen. This article examines the resilient control problem for CPSs with numerous transmission channels under Denial-of-Service (DoS). To begin with, a partial observer technique is developed in response to the Multi-Channel DoS (MCDoS) condition. Secondly, the changing frequency of MCDoS is characterized while maintaining the Input-to-State Stability (ISS) of the closed-loop system. Next, an event-based control strategy is developed to address Full-Scale DoS (FSDoS). Finally, the optimal control parameters are designed to make the system resilient against maximum MCDoS changing frequency.

Set-membership state estimation problem for a certain class of discrete-time nonlinear systems is tackled in this work. First, this problem is formulated as a reachability analysis problem. Then, to avoid the wrapping effect, explicit expression of the general solution of this class of systems is used to design new set-valued state estimation schemes. Moreover, for practical considerations, two periodically re-initialized algorithms based on both interval analysis and zonotope computation are introduced. The merit of the proposed approach is illustrated on a numerical example and its performance is compared to that of other methods borrowed from the literature.

This study focuses on developing a continuous differential neural network (DNN) approximating min–max robust control by applying dynamic neural programming. The suggested controller is applied to a class of nonlinear perturbed systems providing satisfactory dynamics for a given cost function depending on both the trajectories of the perturbed system and the designed constrained control actions. The min–max formulation for dynamic programming offers reliable control for restricted modeling uncertainties and perturbations. The suggested design considers control norm restrictions in the optimization problem. DNN’s approximation of the worst (with respect to the admissible class of perturbations and uncertainties) value function of the Hamilton–Jacobi–Bellman (HJB) equation enables the estimation of the closed-loop formulation of the controller. The robust version of the HJB partial differential equation is studied to create the learning law class for the time-varying weights in the DNN. The controller employs a time-varying Lyapunov-like differential equation and the solution of the corresponding learning laws. A recurrent algorithm based on the Kiefer–Wolfowitz technique can be used by modifying the weights’ initial conditions to fulfill the specified cost function’s end requirements. A numerical example tests the robust control proposed in this study, validating the robust optimal solution based on the DNN approximation for Bellman’s value function.

This note presents the distributed consensus and formation control for a group of AUVs, comprising one leader and three followers arranged in a diamond formation. The study addresses significant control challenges, including external disturbances, noise, model uncertainties, actuator faults, stochastic switching topologies, time-varying communication delays, and positional information between agents. Stochastic switching topologies are assumed to follow a Markov chain. To effectively address these challenges and ensuring satisfactory performance levels, two control architectures have been formulated to facilitate the imposition of desired trajectories upon the system states. The initial architecture amalgamates sliding mode control methodology with adaptive algorithms, designed explicitly for tracking missions in the presence of fully functional actuators. In response to potential actuator failures, the second control architecture integrates passive fault-tolerant techniques, significantly enhancing system reliability under such circumstances as well as switching topology. The proposed framework demonstrates its effectiveness in managing the high nonlinearity and coupled dynamics of AUVs. Simulation results validate the efficacy of the developed strategy. It successfully establishes a desired consensus and formation among agents, reduces chattering phenomena, accurately tracks reference trajectories, handles disturbances and uncertainties within an unknown domain, and achieves finite-time convergence.

Given the urgent need to simplify the end-of-line tuning of complex vehicle dynamics controllers, the Twin-in-the-Loop Control (TiL-C) approach was recently proposed in the automotive field. In TiL-C, a digital twin is run in real time on-board the vehicle to compute a nominal control action; an additional controller is used to compensate for the mismatch between the simulator and the actual vehicle. As the digital twin is assumed to be the best replica available of the real plant, the key issue in TiL-C becomes the tuning of the compensator, which must be performed relying on data only. In this paper, we investigate the use of different black-box optimization techniques for the calibration of the compensator. More specifically, we compare the initially proposed Bayesian Optimization (BO) approach with Virtual Reference Feedback Tuning (VRFT), a one-shot direct data-driven design method, and with Set Membership Global Optimization (SMGO), a recently proposed black-box optimization method. The analysis will be carried out within a professional multibody simulation environment on a novel TiL-C application case study – the yaw-rate tracking problem – to further prove the TiL-C effectiveness on a challenging problem. Simulations will show that the VRFT approach is capable of providing a well-tuned controller after a single iteration, while 10 to 15 iterations are necessary for refining it with global optimizers. Also, SMGO is shown to reduce the computational effort required by BO significantly.

Design and verification of a distributed backward control approach which solves task assignment problem of an under-actuated nonlinear multi-agent system is investigated in this paper. This under-actuated nonlinear multi-agent system includes several quadrotors work together to transport a load aerially. All quadrotors are connected to the load by cables. In this paper, these cables are modelled as several series of masses, springs and dampers to consider their masses and curvatures. There is no real control on the load and cable masses. Therefore, a backward control approach is presented which is based on some virtual controls considered for the load and masses of the cables. By using this control approach, path tracking of the load is performed by controlling formation of the quadrotors during transportation. Solving consensus, formation control and task assignment problems in the case study in less than respectively one, six and five seconds, is the result of using the proposed approach. An Integral BackStepping-Sliding Mode controller is used for formation tracking of a multi-quadrotor system in the presence of external disturbances. Two theorems are presented to guarantee the stability and convergence of the proposed control systems. In addition, simulation example is used to illustrate the effectiveness of the presented control approach.

This article proposes an active disturbance rejection controller (ADRC) for regulating the dq axis currents of permanent magnet synchronous motors (PMSM). First, we establish actual PMSM model by considering ineluctable disturbances due to the variation of parameters, abrupt change of load, etc. Initially, we design dq axis current ADRCs incorporating an extended state observer (ESO). Compared to the commonly used proportional integral (PI) controller, the ADRC exhibits superior anti-interference capabilities in load and speed regulation. Secondly, we conduct a comprehensive analysis of ADRC’s key features, including ESO stability, dq-axis current tracking, and its anti-interference effectiveness in PMSM applications. Additionally, we offer methods for selecting parameters for both ADRC and ESO. Concurrently, we also examine the ADRC algorithm that utilizes a reduced-order extended state observer (RESO). The control performance of ADRC using RESO will be better than ESO. Finally, to verify the effectiveness of this method, we construct an experimental platform using the TMS320F28035. The results confirm the proposed method’s effectiveness.

This paper develops a hierarchical control structure for linear systems based on extended robust approximate simulation. In numerous real-world scenarios, systems are subject to external disturbances, and there is a restriction on accessibility to all of their states. Furthermore, these systems are modeled using linear dynamics with high state dimensions. Hence, the control synthesis problem for these concrete systems is computationally challenging, and constructing an abstract model with smaller state dimensions to design the controller would be beneficial. Therefore, we introduce the notion of a extended robust approximate simulation function between the concrete system and the abstract model under a new hierarchical control structure. With this respect, a controller is designed for the abstract model and refined for the concrete system by formulating an observer-based robust interface controller based on $H_{\infty }$ while guaranteeing the desired performance for the concrete system. It is worth noting that an Unknown Input Observer (UIO) is employed in the controller, which circumvents the full-state accessibility of the concrete system. The applicability of the presented approach is demonstrated by controlling bus voltages on a smart grid.

Switched system is a kind of important hybrid system, which is usually composed of finite subsystems and an organization switching rule. In the study of switched system, it is usually default that the subsystem and the sub-controller are switched synchronously. However, in practice, the switching of sub-controller often lags behind the subsystem, resulting in asynchronous switching between subsystem and sub-controller, which affects the stability of the system. To solve this problem, this paper discusses the asynchronous control problem of a class of time-varying delay switched systems based on mode-dependent average dwell time (MDADT). Firstly, by constructing a dynamic output feedback controller and using piecewise Lyapunov function, the sufficient conditions for exponential stability and $H_{\infty }$ performance of the closed-loop switching system are obtained. At the same time, the design algorithm of dynamic output feedback controller is given. Finally, a simulation of a switched system with two subsystems under ADT switching signal and MDADT switching signal is given, and the effectiveness of the proposed method is verified by comparison.