Model-error control synthesis using approximate receding-horizon control laws

Model-Error Control Synthesis employs an optimal real-time nonlinear estimator to determine model error corrections to a nominal controller. Control compensation is achieved by using the estimated model error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. In this paper the capability of the model-error control synthesis is expanded by combining a nonlinear predictive filter using an approximate receding-horizon optimal solution with a Kalman filter in the overall control design. A robust stability analysis using the interlacing property from the Hermite-Biehler theorem is presented for the new approach. Simulation results for linear systems are shown to verify theoretical predictions.