A novel event-triggered optimal tracking control (ETOTC) method is developed for discrete-time nonlinear systems in this study. For the time-invariant desired trajectory, we prove that error asymptotically stable, and an upper bound of real performance index can be predetermined by a design parameter. time-varying triggering condition reduces communication costs relaxing restriction asymptotic stability closed-loop system, uniformly ultimately bounded (UUB). The ETOTC entails obtaining next...

This article presents a novel event-triggered near-optimal control (ETNOC) method for discrete-time (DT) constrained nonlinear systems. First, the tracking error system is constructed to convert problem regulation problem. By introducing system, asymmetric constraints design original can be converted symmetric system. Second, triggering condition developed using time-triggered optimal value function and law. It proven that closed-loop (CLS) asymptotically stable under ETNOC method, there...

This article uses parallel control to investigate the problem of event-triggered near-optimal (ETNOC) for unknown discrete-time (DT) nonlinear systems. First, achieve control, an augmented system (ANS) with performance index (API) is proposed introduce input into feedback system. The stability relationship between ANS and original analyzed, it shown that, by choosing a proper API, optimal API can be seen as (OPI). Second, based on novel scheme proposed, then ETNOC method developed using...

In this article, we study the optimal control problem of continuous-time (CT) time-invariant nonlinear systems with stochastic disturbances. A new adaptive dynamic programming (ADP) method is developed to solve Hamilton–Jacobi–Bellman equation (HJBE). Under conditional expectation, value function and law are successively approximated simultaneously. The asymptotic stability closed-loop system in probability analyzed by Lyapunov direct method, convergence ADP given. Finally, four simulations...

This paper studies the event-triggered optimal tracking control (ETOTC) problem of continuous-time (CT) unknown nonlinear systems. In order to solve ETOTC problem, an augmented system composed error dynamics and reference is used introduce a new discounted performance index function (DPIF). A novel (ET) adaptive dynamic programming (ADP) method developed ET Hamilton-Jacobi-Bellman equation (HJBE). The presented implemented via identifier-critic architecture, which consists two neural...

Recently, increasing attention has been paid to nuclear power control with the appeals of clean energy and demands regulation integrate into grid. However, a system is discrete-time (DT) nonlinear complicated system, where parameters entangle intrinsic states. Furthermore, need for huge computational ability due high-level order property in reactor model causes many difficulties industries. In this study, new scheme optimal tracking DT systems provided accomplish 2500-MW pressurized water...

In this study, a novel nonlinear parallel control method is proposed for cascaded systems using the backstepping technique. Unlike existing state feedback methods, input taken into system. First, an augmented system constructed to facilitate constructing Lyapunov function. Then, technique can be applied obtain law, and stability analysis shown theory. Finally, simulation conducted demonstrate effectiveness of method.

This paper is concerned with the neuro-control for continuous-time nonlinear systems subject to stochastic disturbance. Due disturbance, traditional value function in existing literature cannot meet control problems, since mixed second partial derivatives are employed construct modified of conditional expectation. To solve Hamilton-Jacobi-Bellman equation, a novel online policy iteration algorithm an Ito correction term developed establishing critic neural network approximate optimal...

A new optimization method for time-varying nonlinear systems named continuous-time policy iteration (CTPI) is designed, which a kind of adaptive critic design (ACD). Iterative control law in CTPI established to solve the generalized Hamilton-Jacobi-Bellman (HJB) equation each iteration. In order avoid partial differential equation, neural networks are used algorithm. The properties also analyzed. monotonically non-increasing and convergence proven, main contributions. Finally, numerical...

The authors present an adaptive dynamic programming-based (ADP-based) scheme to solve the tracking control problems of flexible joint manipulator, which can be transformed Hamilton-Jacobi-Bellman (HJB) equation. For nominal error system, a critic neural network is established approximate value function and online learning algorithm proposed update weight network. occurs at same time as system running. dynamics estimation are proven uniformly ultimately bounded via Lyapunov method. Finally,...

This study proposes a new event-triggered optimal control (ETOC) method for discrete-time (DT) constrained nonlinear systems. First, triggering condition is proposed. We show the asymptotic stability of closed-loop system using proposed and analyze degeneration degree real performance index. Second, to perform ETOC effectively, parallel (PC) combined with adaptive dynamic programming (ADP) applied. Finally, validity validated by simulation.