Adaptive Output Containment Tracking Control for Heterogeneous Wide-Area Networks with Aperiodic Intermittent Communication and Uncertain Leaders.

This paper proposes an adaptive distributed hybrid control approach to investigate the output containment tracking problem of heterogeneous wide-area networks with intermittent communication. First, a clustered network is modeled for a wide-area scenario. An aperiodic intermittent communication mechanism is exerted on the clusters such that clusters only communicate through leaders.

Adaptive optimal control of affine nonlinear systems via identifier-critic neural network approximation with relaxed PE conditions.

This paper considers an optimal control of an affine nonlinear system with unknown system dynamics. A new identifier-critic framework is proposed to solve the optimal control problem. Firstly, a neural network identifier is built to estimate the unknown system dynamics, and a critic NN is constructed to solve the Hamiltonian-Jacobi-Bellman equation associated with the optimal control problem.

Optimal H tracking control of nonlinear systems with zero-equilibrium-free via novel adaptive critic designs.

In this paper, a novel adaptive critic control method is designed to solve an optimal H tracking control problem for continuous nonlinear systems with nonzero equilibrium based on adaptive dynamic programming (ADP). To guarantee the finiteness of a cost function, traditional methods generally assume that the controlled system has a zero equilibrium point, which is not true in practical systems. In order to overcome such obstacle and realize H optimal tracking control, this paper proposes a novel cost function design with respect to disturbance, tracking error and the derivative of tracking error.

Optimal Tracking Control of Nonlinear Multiagent Systems Using Internal Reinforce Q-Learning.

In this article, a novel reinforcement learning (RL) method is developed to solve the optimal tracking control problem of unknown nonlinear multiagent systems (MASs). Different from the representative RL-based optimal control algorithms, an internal reinforce Q-learning (IrQ-L) method is proposed, in which an internal reinforce reward (IRR) function is introduced for each agent to improve its capability of receiving more long-term information from the local environment. In the IrQL designs, a Q-function is defined on the basis of IRR function and an iterative IrQL algorithm is developed to learn optimally distributed control scheme, followed by the rigorous convergence and stability analysis.