Observer-critic structure-based adaptive dynamic programming for decentralised tracking control of unknown large-scale nonlinear systems

In this paper, a decentralised tracking control (DTC) scheme is developed for unknown large-scale nonlinear systems by using observer-critic structure-based adaptive dynamic programming. The control consists of local desired control, local tracking error control and a compensator. By introducing the local neural network observer, the subsystem dynamics can be identified. The identified subsystems can be used for the local desired control and the control input matrix, which is used in local tracking error control. Meanwhile, Hamiltonian-Jacobi-Bellman equation can be solved by constructing a critic neural network. Thus, the local tracking error control can be derived directly. To compensate the overall error caused by substitution, observation and approximation of the local tracking error control, an adaptive robustifying term is employed. Simulation examples are provided to demonstrate the effectiveness of the proposed DTC scheme.