IEEE Trans Neural Netw Learn Syst - Actor-critic-based optimal tracking for partially unknown nonlinear discrete-time systems.

Tópicos

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Resumo

This paper presents a partially model-free adaptive optimal control solution to the deterministic nonlinear discrete-time (DT) tracking control problem in the presence of input constraints. The tracking error dynamics and reference trajectory dynamics are first combined to form an augmented system. Then, a new discounted performance function based on the augmented system is presented for the optimal nonlinear tracking problem. In contrast to the standard solution, which finds the feedforward and feedback terms of the control input separately, the minimization of the proposed discounted performance function gives both feedback and feedforward parts of the control input simultaneously. This enables us to encode the input constraints into the optimization problem using a nonquadratic performance function. The DT tracking Bellman equation and tracking Hamilton-Jacobi-Bellman (HJB) are derived. An actor-critic-based reinforcement learning algorithm is used to learn the solution to the tracking HJB equation online without requiring knowledge of the system drift dynamics. That is, two neural networks (NNs), namely, actor NN and critic NN, are tuned online and simultaneously to generate the optimal bounded control policy. A simulation example is given to show the effectiveness of the proposed method.

Resumo Limpo

paper present partial modelfre adapt optim control solut determinist nonlinear discretetim dt track control problem presenc input constraint track error dynam refer trajectori dynam first combin form augment system new discount perform function base augment system present optim nonlinear track problem contrast standard solut find feedforward feedback term control input separ minim propos discount perform function give feedback feedforward part control input simultan enabl us encod input constraint optim problem use nonquadrat perform function dt track bellman equat track hamiltonjacobibellman hjb deriv actorcriticbas reinforc learn algorithm use learn solut track hjb equat onlin without requir knowledg system drift dynam two neural network nns name actor nn critic nn tune onlin simultan generat optim bound control polici simul exampl given show effect propos method

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