Data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach

Optimal synchronization with L2-gain performance: An adaptive dynamic programming approach

This paper studies an optimal synchronous control protocol design for nonlinear multi-agent systems under partially known dynamics and uncertain external disturbance. Under some mild assumptions, Hamilton-Jacobi-Isaacs equation is derived by the performance index function and system dynamics, which serves as an equivalent formulation. Distributed policy iteration adaptive dynamic programming is developed to obtain the numerical solution to the Hamilton-Jacobi-Isaacs equation. Three theoretical results are given about the proposed algorithm. First, the iterative variables is proved to converge to the solution to Hamilton-Jacobi-Isaacs equation. Second, the L2-gain performance of the closed loop system is achieved. As a special case, the origin of the nominal system is asymptotically stable. Third, the obtained control protocol constitutes an Nash equilibrium solution. Neural network-based implementation is designed following the main results. Finally, two numerical examples are provided to verify the effectiveness of the proposed method.

Finite-time guarantee-cost H∞ consensus control of second-order multi-agent systems based on sampled-data event-triggered mechanisms

This study presents a solution to the challenges of tracking consensus and guarantee-cost H∞ control in a specific set of second-order multi-agent systems with external disturbances. A proposed event-triggered control method based on periodic sampling data is presented for second-order multi-agent systems that include external disturbances. In contrast to the real-time monitoring of system state information used in the previous event-triggered mechanism, this approach collects system state information through periodic sampling. This ensures that the interval between two consecutive triggering moments is at least one sampling cycle, thereby preventing the controller from triggering infinitely within a finite time frame. A finite-time controller based on the sampled-data event-triggered mechanism is designed, and sufficient conditions to ensure the finite-time stability of the closed-loop system at a specified attenuation level are established using theoretical methods such as matrix analysis. For the given sampled-data event-triggered control protocol with a finite-time controller, a quadratic guarantee-cost function is introduced, and by designing control inputs and determining the parameters such as the finite-time upper bound T∗ and the H∞ performance index γ , the exact value of the upper bound of the system's guarantee-cost function under the action of the designed controller is derived. Finally, the feasibility of the proposed control scheme is verified through numerical simulation.

Data-driven set-point learning control with ESO and RBFNN for nonlinear batch processes subject to nonrepetitive uncertainties

This paper proposes an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme for a class of nonlinear batch processes with a priori P-type feedback control structure subject to nonrepetitive uncertainties, by only using the process input and output data available in practice. Firstly, the unknown process dynamics is equivalently transformed into an iterative dynamic linearization data model (IDLDM) with a residual term. A radial basis function neural network is adopted to estimate the pseudo partial derivative information related to IDLDM, and meanwhile, a data-driven iterative ESO is constructed to estimate the unknown residual term along the batch direction. Then, an adaptive set-point learning control law is designed to merely regulate the set-point command of the closed-loop control structure for realizing batch optimization. Robust convergence of the output tracking error along the batch direction is rigorously analyzed by using the contraction mapping approach and mathematical induction. Finally, two illustrative examples from the literature are used to validate the effectiveness and advantage of the proposed design.

Event-triggered integral sliding mode control for uncertain networked linear control systems with quantization

In this paper, the integral sliding mode (ISM, SM) controller is designed to address the problem of implementing non-periodic sampled data for a class of networked linear systems with matched and unmatched uncertainties. Due to the redesigned gain of the nominal controller, the feedback control used by the nominal controller guarantees the asymptotic stability of the uncertain networked linear system. The discontinuous control uses intermittent control based on the reaching law to achieve the finite-time reachability of practical SM band. Based on the defined measurement error, the event-triggered (ET) condition can be derived, and furthermore, it guarantees a sufficient condition for the existence of the actual SM. On this basis, a quantization scheme is added to further decrease the network transmission burden of the linear system. No Zeno behavior occurs in the system owing to the existence of a positive lower bound of inter-event time. Compared with the conventional integral sliding mode control (ISMC, SMC), the proposed control law can not only relieve the network burden, but also decrease the transmission energy loss. Finally, simulation results of a numerical example and a mass-spring damping system demonstrate the effectiveness of the proposed method.