Heuristic Dynamic Programming Nonlinear Optimal Controller

This chapter is concerned with the application of approximate dynamic programming techniques (ADP) to solve for the value function, and hence the optimal control policy, in discrete-time nonlinear optimal control problems having continuous state and action spaces. ADP is a reinforcement learning approach (Sutton & Barto, 1998) based on adaptive critics (Barto et al., 1983), (Widrow et al., 1973) to solve dynamic programming problems utilizing function approximation for the value function. ADP techniques can be based on value iterations or policy iterations. In contrast with value iterations, policy iterations require an initial stabilizing control action, (Sutton & Barto, 1998). (Howard, 1960) proved convergence of policy iteration for Markov Decision Processes with discrete state and action spaces. Lookup tables are used to store the value function iterations at each state. (Watkins, 1989) developed Q-learning for discrete state and action MDPs, where a ‘Q function’ is stored for each state/action pair, and model dynamics are not needed to compute the control action. ADP was proposed by (Werbos, 1990,1991,1992) for discrete-time dynamical systems having continuous state and action spaces as a way to solve optimal control problems, (Lewis & Syrmos, 1995), forward in time. (Bertsekas & Tsitsiklis, 1996) provide a treatment of Neurodynamic programming, where neural networks (NN) are used to approximate the value function. (Cao, 2002) presents a general theory for learning and optimization. (Werbos, 1992) classified approximate dynamic programming approaches into four main schemes: Heuristic Dynamic Programming (HDP), Dual Heuristic Dynamic Programming (DHP), Action Dependent Heuristic Dynamic Programming (ADHDP), (a continuous-statespace generalization of Q-learning (Watkins, 1989)), and Action Dependent Dual Heuristic Dynamic Programming (ADDHP). Neural networks are used to approximate the value function (the critic NN) and the control (the action NN), and backpropagation is used to tune the weights until convergence at each iteration of the ADP algorithm. An overview of ADP is given in (Si et al., 2004) (e.g. (Ferrari & Stengel, 2004), and also (Prokhorov & Wunsch, 1997), who deployed new ADP schemes known as Globalized-DHP (GDHP) and ADGDHP. ADP for linear systems has received ample attention. An off-line policy iteration scheme for discrete-time systems with known dynamics was given in (Hewer, 1971) to solve the discrete-time Riccati equation. In (Bradtke et al, 1994) implemented an (online) Q-learning policy iteration method for discrete-time linear quadratic regulator (LQR) optimal control O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg