Idioma:

Adaptive Learning in Tracking Control Based on the Dual Critic Network Design

Ni, Zhen ; He, Haibo ; Wen, Jinyu

IEEE transaction on neural networks and learning systems, 2013-06, Vol.24 (6), p.913-928

New York, NY: IEEE

Texto completo disponível

Citações Citado por

Enviar para

Título:
Adaptive Learning in Tracking Control Based on the Dual Critic Network Design
Autor: Ni, Zhen ; He, Haibo ; Wen, Jinyu
Assuntos: Adaptive critic design (ACD) ; adaptive dynamic programming (ADP) ; Adaptive systems ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Computer simulation ; Computer systems and distributed systems. User interface ; Connectionism. Neural networks ; Design engineering ; Dynamic programming ; Dynamical systems ; Erbium ; Exact sciences and technology ; internal goal ; Learning ; Lyapunov methods ; lyapunov stability analysis ; Networks ; Nickel ; online learning ; Optimization ; Reinforcement ; reinforcement learning ; Representations ; Simulation ; Software ; Tracking control ; Vectors ; virtual reality
É parte de: IEEE transaction on neural networks and learning systems, 2013-06, Vol.24 (6), p.913-928
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
Descrição: In this paper, we present a new adaptive dynamic programming approach by integrating a reference network that provides an internal goal representation to help the systems learning and optimization. Specifically, we build the reference network on top of the critic network to form a dual critic network design that contains the detailed internal goal representation to help approximate the value function. This internal goal signal, working as the reinforcement signal for the critic network in our design, is adaptively generated by the reference network and can also be adjusted automatically. In this way, we provide an alternative choice rather than crafting the reinforcement signal manually from prior knowledge. In this paper, we adopt the online action-dependent heuristic dynamic programming (ADHDP) design and provide the detailed design of the dual critic network structure. Detailed Lyapunov stability analysis for our proposed approach is presented to support the proposed structure from a theoretical point of view. Furthermore, we also develop a virtual reality platform to demonstrate the real-time simulation of our approach under different disturbance situations. The overall adaptive learning performance has been tested on two tracking control benchmarks with a tracking filter. For comparative studies, we also present the tracking performance with the typical ADHDP, and the simulation results justify the improved performance with our approach.
Editor: New York, NY: IEEE
Idioma: Inglês

Links

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

Adaptive Learning in Tracking Control Based on the Dual Critic Network Design

Ni, Zhen ; He, Haibo ; Wen, Jinyu

New York, NY: IEEE

Buscando em bases de dados remotas. Favor aguardar.