Neural-Network-Based Constrained Optimal Control Scheme for Discrete-Time Switched Nonlinear System Using Dual Heuristic Programming

A novel model of leaky integrator echo state network for time-series prediction

“…This is a nonlinear optimization problem with constraint, aimed at minimizing the training error [39,40]. With respect to train DAF-ESN, the objective is to minimize the error ε n ð Þ, that is to say, to train the output weight matrix W out so that the output y n ð Þ approaches the teacher output d n ð Þ as much as possible.…”

Distributed Optimal Economic Dispatch Based on Multi-Agent System Framework in Combined Heat and Power Systems

“…Moreover, both the physical and communication topology of the future CHP system are prone to have a huge and variable topology with partially-unknown characteristic, which may seriously degrade the effectiveness of centralized methods. In addition, each component in CHP system should possess the plug-and-play feature, which is one of the key characteristics of the future CHP system [11][12][13]. Thus, the centralized method is not very suitable to address the distributed features of the future CHP system.…”

Off-policy neuro-optimal control for unknown complex-valued nonlinear systems based on policy iteration

“…In [11][12][13][14], the optimal control problems for discrete-time nonlinear systems were discussed based on ADP with convergence analyses. In [15][16][17][18][19], the integral reinforcement learning algorithm was used to solve optimal control problems of continuous-time nonlinear systems.…”

“…In [15][16][17][18][19], the integral reinforcement learning algorithm was used to solve optimal control problems of continuous-time nonlinear systems. Hamilton-Jacobi-Bellman (HJB) equation is one of the important keys in optimal control problems [14,20]. In linear time-invariant systems, the HJB becomes the Riccati equation, which can be solved effectively.…”