European Journal of Control最新文献

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.

Vector Fitting (VF) is a successful methodology for both time and frequency domain estimation of dynamic systems. These algorithms have gained popularity as a system identification tool due to their ability to provide a practical procedure for accurately approximating the measured frequency response behavior of complex and highly resonant dynamic systems using rational models. Instrumental Variable (IV) techniques can also be added to the VF algorithm to enhance the optimality of the solutions in the presence of measurement noise. In this context, this paper presents a multiple-input multiple-output (MIMO) generalization of instrumental variable (IV-)VF. The proposed algorithm can be applied to accurately estimating models formed by either continuous- or discrete-time rational basis functions (RBFs). We show that such a generalization preserves the main implementation features observed in Standard VF, but with improved optimality property for its solutions. Additionally, in order to alleviate convergence problems that may arise from the fixed high-frequency normalization, we also present in this paper how to incorporate in the proposed IV-VF method the relaxed formulation found in standard VF for both the continuous and discrete-time cases. The resulting MIMO IV-VF algorithm can be easily understood by VF users, and its applicability is discussed by using an estimation case study with two power system pieces of equipment (an 88 kV Transformer and a 550 kV Instrument Transformer) for which actual frequency domain measurements were collected in the field. It is shown that the proposed algorithm significantly improves the modeling accuracy when compared to standard VF algorithms.