Stochastic robustness synthesis applied to a benchmark control problem

Stochastic robustness synthesis is used to find compensators that solve a benchmark problem. The synthesis minimizes a robustness cost function that is the weighted quadratic sum of stochastic robustness metrics. These metrics — probability of instability, probability of actuator saturation, and probability of settling time violation — are estimated using Monte Carlo analysis. A simple search method minimizes the robustness cost by selecting values for the design parameters of a linear quadratic Gaussian regulator. The resulting compensators are robust, require low actuator authority, and compare well with previous designs.