Locally Optimal Risk-Sensitive Controllers for Strict-Feedback Nonlinear Systems1,2

For a class of risk-sensitive nonlinear stochastic control problems with dynamics in strict-feedback form, we obtain through a constructive derivation state-feedback controllers which (i) are locally optimal, (ii) are globally inverse optimal, and (iii) lead to closed-loop system trajectories that are bounded in probability. The first feature implies that a linearized version of these controllers solve a linear exponential-quadratic Gaussian (LEQG) problem, and the second feature says that there exists an appropriate cost function according to which these controllers are optimal.