  An off policy reinforcement learning based control strategy is developed for
the optimal tracking control problem to achieve the prescribed performance of
full states during the learning process. The optimal tracking control problem
is converted as an optimal regulation problem based on an auxiliary system. The
requirements of prescribed performances are transformed into constraint
satisfaction problems that are dealt with by risk sensitive state penalty terms
under an optimization framework. To get approximated solutions of the Hamilton
Jacobi Bellman equation, an off policy adaptive critic learning architecture is
developed by using current data and experience data together. By using
experience data, the proposed weight estimation update law of the critic
learning agent guarantees weight convergence to the actual value. This
technique enjoys practicability comparing with common methods that need to
incorporate external signals to satisfy the persistence of excitation condition
for weight convergence. The proofs of stability and weight convergence of the
closed loop system are provided. Simulation results reveal the validity of the
proposed off policy risk sensitive reinforcement learning based control
strategy.
