Adaptive dynamic programming-based optimal regulation on input-constrained nonlinear time-delay systems

The adaptive dynamic programming (ADP)-based optimal regulation strategy is put forward for input-constrained nonlinear time-delay systems. In the spirit of Lyapunov theories, the stability of the nominal system is investigated in terms of linear matrix inequalities (LMIs), which consequently gives rise to sufficient delay-dependent stability conditions. Afterward, a single neural network (NN) which serves as critic and actor NN simultaneously is employed for the realization of ADP-based optimal regulation. The NN weights are updated in real-time and the weight estimate errors are proved to be convergent. As a result, computational complexity is efficiently decreased together with the storage space. Numerical simulation shows the validation of our approach.Kindly check and confirm the inserted city name is correct. Amend if necessary.CorrectKindly check and confirm the Organization division and Organization name of Affiliation 2.Correcy

Automated summary

The study introduces an optimal regulation strategy based on adaptive dynamic programming for input-constrained nonlinear time-delay systems, utilizing a neural network for real-time weight updates to reduce computational complexity and storage space.