In the secondary control of the microgrid (MG), unknown disturbances, load variations, and the plug-and-play operation of distributed generators (DGs) can result in voltage and frequency exceeding safe boundaries during transient operations, posing greater challenges for voltage and frequency restoration control. To ensure the stability and safety of the islanded MG, a safe secondary voltage and frequency control scheme based on control barrier functions (CBFs) is proposed in this paper. The secondary control problem of the MG is transformed into a multi-agent distributed consensus tracking problem. The objective of consensus tracking is determined by the control Lyapunov function, and the safe constraints on states are handled through CBFs. Meanwhile, input constraints are considered to avoid actuator saturation. Then, a quadratic programming (QP)-based voltage and frequency controller is designed to achieve the secondary control objective of the MG. Moreover, in response to disturbances and uncertainties in the system, a robust finite-time disturbance observer is designed and integrated into the proposed CBF-CLF-QP framework. This integration enables the controller to proactively compensate for disturbances and uncertainties, improving the robustness of the system. In contrast to traditional methods, the proposed method exhibits smaller voltage and frequency fluctuations under load variations, plug-and-play operations, and uncertain disturbances, thereby ensuring smoother power output and reducing regulation time. Finally, the proposed method is ultimately verified through simulations and experiments on a hardware test platform with the StarSim rapid control prototype.

The three-phase inverter is one of the critical components in microgrids and distributed generation systems. The design of a high-performance control system for three-phase inverters is profoundly challenged by abrupt load variations and uncertainties in system parameters. To achieve fast and accurate voltage tracking under parameter uncertainties and load disturbances, this paper proposes a practical robust control scheme combining finite-time disturbance observer and finite-time control (FTC) techniques. In contrast to conventional control schemes employing load current observers, the extended finite-time disturbance observer is designed to achieve rapid finite-time estimation of lumped disturbances caused by load variations and parameter uncertainties. Meanwhile, a robust FTC scheme is proposed, which employs a continuous nonsingular terminal form to eliminate chattering and achieve tracking of the reference voltage within a finite time. The finite-time stability of the entire closed-loop system is proven based on Lyapunov theory. Compared with traditional methods, the proposed method has a faster dynamic response and a better performance in the suppression of disturbances, while requiring no additional sensors to increase costs. Finally, simulations and experiments are performed on a three-phase inverter hardware test platform with the StarSim rapid control prototype. The effectiveness of the proposed control strategy is verified with the experimental results.

Dynamic artificial neural network models refer to the dynamic model structures that include artificial neural networks such as multi-layer perceptrons, which are used to model nonlinear system dynamics.$H_{\infty }$tracking control for dynamic artificial neural network models with communication delays and external disturbance is investigated in this article. First, we formulate the dynamic artificial neural network model as a sampled-state error dependent model for networked control systems with the event-triggered mechanism, which is designed to reduce the consumption of network resources. The network-induced delay considered in this paper is time varying with a known upper bound. Furthermore, by Lyapunov-based techniques, we present sufficient conditions such that the closed-loop system satisfies the$H_{\infty }$tracking performance. In addition, we propose a method to co-design the tracking controller and event-triggering parameters in the form of linear matrix inequalities. The effectiveness of our proposed methods is validated through a simulation example and a turbofan engine hardware-in-the-loop experiment. Note to Practitioners—When mathematical models of the plant dynamics are not available, neural network modeling can serve as a useful method for controller design, provided we have numerical information about the system behavior. The novel stability conditions and the established controller design method can be adapted for different classes of dynamic artificial neural network models, including neural state space models, global input-output models and dynamic recurrent neural networks. This characteristic reduces the constraints on model selection, thus expanding the application scenarios. Instead of the stabilization problem,$H_{\infty }$tracking control problem is considered in this paper, which has a wider range of applications. In view of the growing need to reduce unnecessary consumption of communication resources in some digital control systems such as smart power grid, aircraft, industrial automatic production and so on, different from existing results, a discrete-time version of periodic event-triggered mechanism is adopted in analysis and control of dynamic artificial neural networks. In addition, disturbances and time delays which widely exist in engineering are also considered. Therefore, the proposed method is more suitable for practical applications. Finally, the established method is applied to the turbofan engine multivariable trajectory tracking control problem, and the effectiveness is illustrated by experiments. In future research, we will address the design problem of dynamic artificial neural network observer for the situation where the system states are unmeasurable.

In this paper, we propose a switched model predictive control scheme based on the data-driven method without any terminal ingredients. Compared with conventional model-based model predictive control schemes, we utilize a series of input-output data to describe the system dynamics according to the behavioral systems theory. In order to improve system performance, multiple cost functions regulated by a suitable switching strategy are considered. For the nominal case with noise-free data, we prove that the proposed data-driven switched MPC schemes can ensure the exponential stability of the system provided that the switching signal satisfies the average dwell-time condition. Moreover, the robust stability is also analysed mathematically for the robust case considering bounded measurement noise. The effectiveness of the control algorithm is verified by the speed control of a commercial high bypass turbofan engine on a hardware-in-loop platform. The results of the experiment show the advantages of the proposed switched model predictive control scheme over conventional non-switched model predictive control schemes.Note to Practitioners—In this paper, we consider the problem of designing a control law to improve the performance of the system with multiple requirements that need a tradeoff. Since the performance criteria can easily be formulated in the cost function in model predictive control algorithm, it is a natural thought to design multiple cost functions which can be switched according to different performance requirements. However, the modelling process of the system is usually complicated and time-consuming for conventional model predictive control. In this paper, the data-driven method replaces the counterpart of the model in conventional model predictive control. The input-output measurements of the system can be directly used to forecast the dynamics of the system. The feasibility and stability of the control scheme are analyzed mathematically. We show that the proposed control law can ensure the above closed-loop performance of the controlled system, provided that the parameters of the controller are suitable and the switch of the cost function is sufficiently slow. The effectiveness of the proposed control scheme is demonstrated by the application of a high bypass ratio turbofan engine on a hardware-in-loop experiment platform. The results show that the turbofan engine can achieve a relatively rapid response with smaller overshoot.

Model predictive control (MPC) is popular in applications with slow dynamics because of its advantages in handling constraints and multivariable optimization. But for aeroengines, it is difficult to obtain an exact prediction model, which will lead to offsets in tracking. Besides, deploying MPC to embedded controllers for real-time control is a well-known challenge. Therefore, this paper presents a switched linear MPC, which incorporates the augmented prediction models with error integrator for offset-free tracking, the sparse-based quadratic programming formula for solving MPC, and a reset strategy for achieving bumpless transfer at the switching instant. Further, on the hardware board we developed, six hardware-related acceleration strategies are explored and evaluated for real-time performance. Then, eight cases of five objects are tested, whose results indicate a significant speedup of around 50 times. At last, the hardware-in-the-loop tests of the turbofan engine and the real bench tests of the micro-turbojet engine are performed, which verifies the superiority, real-time performance, and potential for practical applications.Note to Practitioners—This paper was motivated by the problem of applying the offset-free MPC to the embedded control system for aeroengines. The difficulty of accurate modelling and the requirement to compute the embedded MPC in a limited time are two major challenges. Accordingly, we explore six hardware acceleration strategies that are often ignored to ensure the real-time performance. Moreover, we investigate the switched MPC to obtain the desired dynamic response. However, the controller switching tends to induce fluctuations that are harmful to the engine. Hence, we present a reset strategy to ensure bumpless transfer performance. Preliminary physical experiments suggest that the proposed approaches are feasible to achieve the predetermined objectives. Further, the comparisons with the previous methods highlight our superiority. Overall, this paper contributes to optimize the computational performance of microcontrollers and enhance bumpless transfer performance for MPC. In our future work, the proposed approaches will be applied to flight experiments and more industrial devices.

This article studies a data-driven co-designing method for networked control systems under denial-of-service attacks and communication delays, using only noisy data of system trajectories instead of explicit system models. To this end, a delay system framework is first established by investigating the effect of both denial-of-service attacks and time-varying delays. In order to reduce the consumption of the network resource, a novel dynamic event-triggering scheme is proposed. The sensor is time-triggered while the controller and the actuator are event-triggered in the scheme. Based on the Lyapunov stability approach and a data-based representation of unknown systems, a data-driven method for co-designing the controller gain and event-triggering parameters is developed. In the presence of denial-of-service attacks with unknown patterns, the proposed data-driven co-designing method demonstrates the trade-off between the system performance metric and the denial-of-service resilience level. Furthermore, the impact of time-varying communication delays on the denial-of-service resilience level is investigated. Finally, several numerical experiments demonstrate the effectiveness of the proposed methods.

In this paper, a class of finite-time state zonotopes are proposed for the interval estimation of switched systems with asynchronous switching. First, considering the mismatch between the system mode and the observer mode, an augmented switching signal is adopted to deal with the asynchronism uniformly. Then, the state zonotopes are constructed in the synchronous and asynchronous intervals, respectively, to enclose the actual system state. Specific iteration procedures are provided for the generator matrix. Moreover, a new criterion is newly proposed to address that the estimation error is finite-time bounded with a pre-specified upper bound under the hybrid mode-dependent switching. Solvable conditions are presented to optimize the observer gain and ensure a finite-time$\mathcal {L}_{2}$gain in attenuating the unknown-but-bounded perturbation. Finally, the effectiveness of the proposed method is validated through the application to a switched converter.