A hybrid dynamical system with robust switching control by action dependent heuristic dynamic programming

In this paper a hybrid dynamical system with linear plant characteristics but unknown state, disturbance and observation inputs is considered and controlled by switching between fixed linear output feedback controllers. Using state estimation based on Kalman filtering and solving a Riccati equation, a dynamic programming solution based on the estimated state can be obtained and a switching sequence for the output feedback controllers can be deduced. However, solving the dynamic programming equation is difficult in practice due to the 'curse of dimensionality'. Action Dependent Heuristic Dynamic Programming (ADHDP), also known as Q-learning, is applied to achieve an approximate dynamic programming solution based on piecewise quadratic interpolation and explicit determination of extremal values.