Linna Zhou

This article addresses the data-driven asynchronous dynamic event-triggered H_∞ tracking control problem for singularly perturbed systems (SPSs) with unknown slow dynamics and unknown bounded disturbances. First of all, considering the two-time-scale characteristic of SPSs, an H_∞ tracking control problem for the slow subsystem and an asymptotic stability problem for the fast subsystem are formulated via time-scale decomposition. Secondly, an asynchronous dynamic event-triggered scheme (ETS) based on dual-rate sampling is proposed to reduce the communication burden. Then, a data-based parameterized model of the augmented system is given, which consists of the reference system and the slow subsystem with unknown dynamics. Further, by combining the data-based parameterized model and employing the full-block S-procedure, the data-based co-design method of tracking controller and event-triggered matrix is developed. The overall stability analysis of the full system under the composite controller is given. Finally, the proposed scheme is verified by a Chua’s circuit and a networked DC motor control system.

In this article, a novel model-free coordinated optimal regulation design methodology is proposed for the rigidly connected dual permanent magnet synchronous motor (PMSM) system via adaptive dynamic programming (ADP). First, we adopt the classical master-slave structure to maintain torque synchronization by virtue of field-oriented control. Then, a reduced-order model of the dual-PMSM system is established through the application of singular perturbation theory (SPT), which is of significance to decrease the learning time and computational complexity in the outer speed loop design. Afterwards, we design a coordinated adaptive optimal regulator in framework of ADP to drive the speed of girth gear asymptotic tracking the reference signal and accommodate the load torque disturbance, which is independent of the knowledge of model parameters of the system. According to SPT, we analyze the suboptimality, closedloop stability, and robustness properties of the obtained controller under mild conditions. Finally, comprehensive experimental studies are provided to verify that the proposed control strategy can achieve the speed regulation and the torque synchronization, as well as ameliorate the transient response.

Flexible and efficient wide-load tracking in coal-fired power generation systems (CPGSs) is crucial for integrating renewable energy. To address the challenges arising from the dynamic characteristics and task distribution differences during the wide-load operation of thermal power units, this article proposes a novel hierarchical model-agnostic meta reinforcement learning (HMAMRL) framework. This framework combines inner meta-learning for quick adaptation within task categories and outer meta-learning for sharing general task knowledge, ensuring robust generalization under different load conditions. Meanwhile, an adaptive multicriterion reward function design method is proposed to dynamically balance load tracking costs, coal consumption costs, and input fluctuation costs. Moreover, a truncated proximal policy optimization (TPPO) algorithm ensures precise load control within physical constraints. Experimental results on the 160 and 1000 MW CPGSs demonstrate the effectiveness and superiority of the proposed algorithm.

In this article, a task-oriented enhanced self-learning speed synchronization control method is proposed for flexible coupled dual-motor (FCDM) systems with unknown model parameters. Firstly, the two-time-scale characteristics of the FCDM system are analyzed. Using singular perturbation theory (SPT), the original system is turned into a reduced-order model. Secondly, aiming at the challenge that the state of the reduced-order system cannot be directly measured, an innovative state reconstruction mechanism is proposed, and the HJB equation is solved by reinforcement learning (RL) algorithm. The actor-critic structure is introduced to approximate the control policy and cost function respectively. Thirdly, in order to enhance the quality of training data and ensure the efficient execution of the algorithm, a variational auto encoder (VAE) is introduced to enrich the sparse data points close to the reference rotational speed. Finally, experiments are carried out on the semi-physical simulation platform based on dSPACE to verify the effectiveness of the proposed method.

The grid connection of renewable energy poses challenges to the coordinated control of coal-fired power generation systems. Model uncertainty makes model-driven methods less effective due to the lack of adaptive capability. Large inertia of thermal process leads to local aggregation of state information, and the direct grafting reinforcement learning methods will affect the learning efficiency due to insufficient data utilization. To this end, this article proposes dual-prioritized experience replay value distribution deep deterministic policy gradient (DPER-VDP3G) algorithm. Value distribution is introduced to reflect the influence of model uncertainty on the evaluation of coordinated control policy, thus improving the accuracy of prediction cost function. The DPER is designed to reduce the nonuniform sampling bias and remove redundant data to enhance sample diversity. Comparative experiments demonstrate the advantages of the proposed method for improving network training efficiency, ameliorating load tracking accuracy and speed, and reducing energy consumption.

This brief discusses the direct data-driven optimal set-point tracking (OST) control problem of linear discrete-time systems. A cost function without discount factor is defined in terms of control increment, which is different from the popular discount cost function for OST control problems. The standard linear quadratic tracking (LQT) solution is reformulated by convex duality, and the algebraic Riccati equation (ARE) for finding optimal solution of the infinite horizon OST problem is equivalently converted to a convex optimization (CO) problem. More importantly, an OST controller which ensures the tracking error converge to zero only using off-line historical data. Finally, an example of 2-degree-of-freedom (2-DOF) helicopter verifies the effectiveness of the obtained results.

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems (SPSs) with unknown dynamics based on reinforcement learning (RL). Taking into account the slow and fast characteristics among system states, the interconnected SPS is decomposed into the slow time-scale dynamics and the fast time-scale dynamics through singular perturbation theory. For the fast time-scale dynamics with interconnections, we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function. For the slow time-scale dynamics with unknown system parameters, an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data. By combining the slow and fast controllers, we establish the composite decentralized adaptive optimal output regulator, and rigorously analyze the stability and optimality of the closed-loop system. The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality. The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

In this article, based upon reinforcement learning (RL) and reduced control techniques, an ${H}_{\infty }$ output tracking control method is represented for nonlinear two-time-scale industrial systems with external disturbances and unknown dynamics. First, the original ${H}_{\infty }$ output tracking problem is transformed into a reduced problem of the augmented error system. Based on zero-sum game idea, the Nash equilibrium solution is given and the tracking Hamilton–Jacobi–Isaacs (HJI) equation is established. Then, to handle the issue of unmeasurable states of the virtual reduced system, full-order system state data are collected to reconstruct the reduced system states, and the model-free RL algorithm is proposed to solve the tracking HJI equation. Next, the algorithm implementation is given under the actor–critic–disturbance framework. It is proved that the control policy obtained from reconstructed state data can make the augmented error system asymptotically stable and satisfy the L $_{\mathbf{2}}$ gain condition. Finally, the effectiveness of the proposed method is illustrated by the permanent-magnet synchronous motor experiment.