In this article, we introduce some recent research trends within the field of adaptive/approximate dynamic programming (ADP), including variations on structure ADP schemes, development algorithms and applications schemes. For algorithms, point focus is that iterative can be sorted into two classes: one class algorithm with initial stable policy; other without requirement policy. It generally believed latter has less computation at cost missing guarantee system stability during iteration...

In this paper, we aim to solve the infinite-time optimal tracking control problem for a class of discrete-time nonlinear systems using greedy heuristic dynamic programming (HDP) iteration algorithm. A new type performance index is defined because existing indexes are very difficult in solving kind problem, if not impossible. Via system transformation, transformed into an regulation and then, HDP algorithm introduced deal with rigorous convergence analysis. Three neural networks used...

In this paper, a new online scheme is presented to design the optimal coordination control for consensus problem of multiagent differential games by fuzzy adaptive dynamic programming, which brings together game theory, generalized hyperbolic model (GFHM), and programming. general, solution coupled Hamilton–Jacobi (HJ) equations. Here, first time, GFHMs are used approximate solutions (value functions) HJ equations, based on policy iteration algorithm. Namely, each agent, GFHM capture mapping...

In this paper, an adaptive fuzzy output feedback control approach is proposed for single-input-single-output nonlinear systems without the measurements of states. The addressed in paper are assumed to possess unmodeled dynamics presence unstructured uncertainties and dynamic disturbances, where not linearly parameterized, no prior knowledge their bounds available. Fuzzy logic used approximate uncertainties, a state observer developed estimate unmeasured By combining backstepping technique...

This paper is concerned with the robust exponential synchronization problem of a class chaotic delayed neural networks different parametric uncertainties. A novel impulsive control scheme (so-called dual-stage control) proposed. Based on theory functional differential equations, global error bound together some new sufficient conditions expressed in form linear matrix inequalities (LMIs) derived order to guarantee that dynamics can converge predetermined level. Furthermore, estimate stable...

This paper investigates the optimal consensus control problem for discrete-time multi-agent systems with completely unknown dynamics by utilizing a data-driven reinforcement learning method. It is known that relies on solution of coupled Hamilton-Jacobi-Bellman equation, which generally impossible to be solved analytically. Even worse, most real-world are too complicated obtain accurate mathematical models. To overcome these deficiencies, data-based adaptive dynamic programming method...

In this paper, several sufficient conditions are established for the global asymptotic stability of recurrent neural networks with multiple time-varying delays. The Lyapunov-Krasovskii theory functional differential equations and linear matrix inequality (LMI) approach employed in our investigation. results shown to be generalizations some previously published less conservative than existing results. present also applied constant time

In this paper, two adaptive neural network (NN) decentralized output feedback control approaches are proposed for a class of uncertain nonlinear large-scale systems with immeasurable states and unknown time delays. Using NNs to approximate the functions, an NN state observer is designed estimate states. By combining backstepping technique design principle, approach developed. order overcome problem “explosion complexity” inherent in approach, dynamic surface (DSC) introduced into first...

In this paper, the event-based control problems for nonlinear stochastic systems are investigated. First, a novel condition input-to-state stability is established. Then, dynamic event-triggered approach proposed and of resulting closed-loop system also proved. Next, new self-triggering mechanism developed additional internal variable designed according to predicted value state error, which ensures that stochastically stable. It shown lower bounds interexecution times by self-triggered...

This paper focuses on the problem of adaptive neural network (NN) control for a class nonlinear nonstrict-feedback systems via output feedback. A novel NN backstepping output-feedback approach is first proposed systems. The monotonicity system bounding functions and structure character radial basis function (RBF) NNs are used to overcome difficulties that arise from structure. state observer constructed estimate immeasurable variables. By combining technique with approximation capability...

This paper investigates the issues of day-ahead and real-time cooperative energy management for multienergy systems formed by many bodies. To address these issues, we propose an event-triggered-based distributed algorithm with some desirable features, namely, execution, asynchronous communication, independent calculation. First, body, seen as both supplier customer, is introduced system model development. On this basis, bodies cooperate each other to achieve objective maximizing social...

This paper deals with the problem of control synthesis discrete-time Takagi-Sugeno fuzzy systems by employing a novel multiinstant homogenous polynomial approach. A new scheme and class Lyapunov functions, which are polynomially parameter-dependent on both current-time normalized weighting functions past-time proposed for implementing object relaxed synthesis. Then, stabilization conditions derived less conservatism than existing ones. Furthermore, relaxation quality obtained is further...

With an increasing penetration of wind power in the modern electrical grid, replacement large conventional synchronous generators by plants will potentially result deteriorated frequency regulation performance due to reduced system inertia and primary response. A series challenging issues arise from aspects planning, operation, control protection. Therefore, it is valuable develop variable speed turbines (VSWTs) equipped with capabilities that allow them effectively participate addressing...

In this paper, a novel method is developed for the stability problem of class neural networks with time-varying delay. New delay-dependent criteria in terms linear matrix inequalities recurrent delay are derived by newly proposed augmented simple Lyapunov-Krasovski functional. Different from previous results using first-order convex combination property, our derivation applies idea second-order and property quadratic function which given form lemma without resorting to Jensen's inequality. A...

In this paper, a novel iterative two-stage dual heuristic programming (DHP) is proposed to solve the optimal control problems for class of discrete-time switched nonlinear systems subject actuators saturation. First, nonquadratic performance functional introduced confront constraints saturating actuator. Then, DHP algorithm developed Hamilton-Jacobi-Bellman (HJB) equation system with Moreover, convergence and optimality are strictly proven. To implement efficiently, there two neural networks...

The problem of H∞ state feedback control affine nonlinear discrete-time systems with unknown dynamics is investigated in this paper. An online adaptive policy learning algorithm (APLA) based on dynamic programming (ADP) proposed for real-time the solution to Hamilton-Jacobi-Isaacs (HJI) equation, which appears problem. In algorithm, three neural networks (NNs) are utilized find suitable approximations optimal value function and saddle point disturbance policies. Novel weight updating laws...

An optimal control method is developed for unknown continuous-time systems with disturbances in this paper. The integral reinforcement learning (IRL) algorithm presented to obtain the iterative control. Off-policy used allow dynamics be completely unknown. Neural networks are construct critic and action networks. It shown that if there disturbances, off-policy IRL may not converge or biased. For reducing influence of a compensation controller added. proven weight errors uniformly ultimately...

Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in course modernization providing a flexible way to integrate distributed renewable energy resources (RES) into grid. However, RES, such as solar wind, can be highly intermittent stochastic. These uncertain combined with load demand result random variations both supply sides, which make it difficult effectively operate...

This paper discusses the problem of adaptive neural network output feedback control for a class stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown uncertainties, dead-zones, unmodeled dynamics and without direct measurements state variables. In this paper, networks (NNs) are employed to approximate then by representing dead-zone time-varying system with bounded disturbance. An NN observer is designed estimate unmeasured states....

This paper focuses on the distributed optimal cooperative control for continuous-time nonlinear multiagent systems (MASs) with completely unknown dynamics via adaptive dynamic programming (ADP) technology. By introducing predesigned extra compensators, augmented neighborhood error are derived, which successfully circumvents system knowledge requirement ADP. It is revealed that consensus protocols actually work as solutions of MAS differential game. Policy iteration algorithm adopted, and it...

In this article, the bipartite fixed-time output consensus problem of heterogeneous linear multiagent systems (MASs) is investigated. First, a distributed observer proposed, by which follower can estimate leader's state. The value same as state in modulus but may not sign due to existence antagonistic interactions between agents. Then, an adaptive further proposed. It fully without involving any global information. This only system matrix also Next, nonlinear control laws are developed based...

This article addresses the adaptive bipartite event-triggered output consensus issue for heterogeneous linear multiagent systems. We consider both cooperative interaction and antagonistic between neighbor agents in fixed switching topologies. An compensator consisting of time-varying coupling weights dynamic mechanism is first proposed to estimate leader's state a fully distributed manner. Different from existing methods, has three advantages: 1) it does not depend on any global information...

This article devotes to solve the fault-tolerant control problem based on interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy stochastic switched uncertain time-delayed systems with signal quantization. Stochastic can model a dynamic structure susceptible abrupt faults, making it more practically significant in power or economic systems. The core design is an observer-based scheme that estimate incomplete measurable variables and eliminate influence of fault dynamically well enhancing robust...