European Journal of Control最新文献

This paper studies the resilient consensus problems under $\ell$-hop communication over directed random networks. We develop novel multi-hop protocols to tackle both leaderless and leader–follower consensus with a single leader when the multiagent system is under both attack from Byzantine agents and failure of communication edges. Our unified framework features a multiple-input multiple-output system design, and generalizes the weighted mean subsequence reduced algorithms, in which extremal values in the $\ell$-hop neighborhood of a cooperative agent are censored based on the notion of minimum message cover. We establish conditions on dynamical network structures, under which secure consensus with vector states can be achieved in the sense of almost sure convergence.

Repetitive control (RC), which can track the periodic input or suppress the periodic disturbance with a zero steady-state error, is a promising control strategy for shunt active power filters (SAPF). However, the conventional RC (CRC) scheme will suffer a severe performance degradation caused by the grid frequency variations. In this article, a frequency-adaptive odd-harmonic repetitive control scheme with a single and fixed sampling rate is proposed for SAPF to deal with grid frequency variations and provide fast transient response for RC system. The frequency-adaptability of proposed repetitive control scheme can be implemented by updating the coefficients of the approximate expression for the delay unit in repetitive controller. The FIR filter based on Lagrange linear interpolation is used to transform the multirate repetitive control system into single-rate repetitive control system to address the problems caused by the multirate repetitive control system. Moreover, a fractional-order linear compensator with a simple structure is designed to compensate the magnitude and phase of the system accurately. Compared with other classical repetitive control strategies, the proposed repetitive control scheme can provide a better adaptation to the steady-state deviation and dynamic variation of grid frequency and ensure the control performance of SAPF system. Simulation and experimental results verify the effectiveness of the proposed control scheme.

The article proposes a $H_{\infty }$ fault-tolerant control approach for a series of uncertain linear parameter-varying (LPV) time-delay models to obtain disturbance suppressions. Specifically, by applying the Lyapunov functions, a series of feedback controllers are provided to ensure the robust performance of LPV models with actuator faults. Meanwhile, a convex optimization strategy is developed for resolving optimization problems in the presence of bilinear matrix inequalities (BMIs), where the robustness conditions are improved to guarantee the stability of LPV model under uncertain factors. By resolving a class of linear matrix inequalities (LMIs), the gain matrices for LPV systems can be obtained. Furthermore, the less conservative conditions are developed and supported by strict theoretical derivation. Ultimately, the validity of proposed approach is confirmed by simulation analyses of truck–trailer systems.

The roll out of connected and autonomous vehicle (CAV) technologies can be beneficial for road traffic in terms of road safety, traffic and energy efficiency. This paper addresses the platooning problem of heterogeneous CAVs with consideration of a time-varying leader speed and multi-dimensional uncertainties that include modeling uncertainties and local measurement disturbances. Resorting to a spatial domain modeling approach with appropriate coordination changes and the relaxation of nonconvex constraints, the traditional nonlinear optimal control problem formulation is convexified for improved computational efficiency and ease of implementation. Then, a convex and tube-based distributed model predictive control algorithm (DMPC) utilizing a predecessor-following communication topology is designed with certified theoretical properties, which can be boiled down to DMPC parameter tuning criteria. Finally, numerical results and comparisons against nominal and nonlinear DMPC-based methods are carried out to verify the performance and computational efficiency of the proposed method under different driving scenarios.

In this paper, the integral sliding mode-based event-triggered optimal fault tolerant tracking control of continuous-time nonlinear systems is investigated via adaptive dynamic programming. The developed control scheme consists of two parts, i.e., integral sliding mode control and event-triggered optimal tracking control. For the first part, an integral sliding mode controller is designed to eliminate the affect of actuator fault and the dynamics of nominal nonlinear systems is obtained. For the second part, a novel quadratic cost function with respect to the tracking error and its dynamics is developed such that the feedforward control law or the discount factor is not required, which reduces the complexity of the control method and guarantees the tracking performance. Moreover, a critic-only structure is established to obtain the solution of tracking Hamilton–Jacobi–Bellman equation. It should be noted that the optimal tracking control law is updated only at triggering moments in order to preserve computing and communication resources. Finally, the effectiveness of the present approach is demonstrated through simulation examples of a robotic arm system and a Van der Pol circuit system.

The problem of dynamic event-based non-fragile filtering for discrete-time singular Markovian jump systems is investigated in this paper, considering network-induced delays and packet dropouts. The random packet dropout is described by a Bernoulli-distributed stochastic variable with a known distribution law. To save limited network resources, an improved dynamic event-triggered scheme is proposed. By utilizing the Lyapunov-Krasovskii stability theory, a sufficient criterion is derived to ensure the stochastic admissibility of the resulting filter error system with a desired $H_{\infty }$ performance index. Based on the derived criterion, a co-design algorithm for the non-fragile filter and the dynamic event-triggered scheme is proposed by solving a set of linear matrix inequalities. The effectiveness and advantages of the proposed method are illustrated by two simulation examples.

This research specifically addresses the problem of sliding mode control (SMC) in a particular group of T–S fuzzy systems that experience output disturbances and time delays using a networked control system. First, a model for a proportional–derivative sliding mode observer (SMO) is established, subsequently followed by the design of a controller based on SMO to maintain closed-loop system stability. The authors establish a novel mechanism to optimize the bandwidth utilization of the communication network by introducing a newly adjusted event-triggering parameter. After that, addressed a linear matrix inequality (LMI) problem to determine the controller gain and observer coefficients. This was done to guarantee the asymptotic stability of the closed-loop plant. Furthermore, the authors were able to directly identify the disturbances for these plants using the proposed descriptor SMO. Moreover, a parameter-based novel event-triggered scheme, along with a new resultant closed-loop system, will be employed to modify trigger frequency parameters within a unified framework. The controller gains will be determined by solving a linear matrix inequality, subject to certain conditions that ensure sufficiency. These conditions will be deduced to ensure asymptotic stability. In the final section, the authors presented examples from the Wind Energy System (WES) that illustrate the feasibility and effectiveness of our proposed methodology.

The real-time implementation of the explicit MPC suffers from the evaluation of the, potentially large, lookup table. The paper revisits the original approach and presents an efficient reachability-sets-driven-based explicit MPC method addressing this issue by splitting the look-up table into the set of the “relevant” subsets. Simultaneously, effective binary encoding is introduced to minimize the runtimes and the memory footprint. Further acceleration is achieved by introducing the “smart” order of the considered critical regions. Then, the significant real-time complexity reduction is ensured by online pruning and traversing the sorted lookup table associated with the optimal control law evaluation. Technically, the number of critical regions to be explored is reduced and the order is redefined to accelerate the point location problem and minimize the computational effort. While the optimality of the control law is still preserved, the cost that we need to pay for the accelerated point location problem lies in an additional offline computation effort and a minor increase in memory requirements of the underlying controller. The benefits of the proposed method are demonstrated using an extensive case study. The complexity reduction strategy was investigated on two fast-dynamic benchmark systems and the computational burden was analyzed by implementing the designed controllers on an embedded hardware.

This article figures out the asynchronous output feedback sliding mode dissipative control issue for Markovian jump delay systems (MJDSs) with uncertain parameters and external disturbances. Different from the massive methods of constructing sliding mode surfaces (SMS), a new dynamic output feedback (DOF) integral SMS is developed, where the SMS is composed of a virtual controller with quantized output and the controller is asynchronous with the system mode. Meanwhile, the reachability of the SMS is guaranteed. In addition, the transition rates (TRs) considered in this paper are generally bounded, which means that the designed method applies to the more general case of TRs, such as fully known and partially unknown TRs. The sufficient criteria of stochastic stability and strict dissipation for the MJDSs are deduced. The design scheme of the controller gain matrix is proposed by the linear matrix inequality (LMI) technique. Finally, two examples are shown to demonstrate the effectiveness and feasibility of the designed technique.

This paper proposes a leader–follower formation control protocol using fast finite-time (FFT) theory, based on second-order nonlinear multi-agent systems (MASs) with input saturation constraints. The artificial potential field method is addressed to implement the formation control with obstacle avoidance of the MASs. An adaptive FFT strategy is constructed that all the agents follow required formation performance. Neural networks are considered to approximate uncertain functions, which improved convergence and ensuring safety of distributed formation control. Finally, the validity of the theoretical approach is demonstrated by FFT stability theory validated by simulation examples.