Robust control system synthesis for constrained systems and its experimental validation

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Real control systems have constraints such as limitations on input and output signals. It is important to take into account system constraints since a control system may be destabilized or expected performance would be lost if system constraints are neglected in control system design. This research focused on robust control system design for constrained systems, in particular, 1) model predictive control with disturbance attenuation, 2) compensation law for constrained systems, and we have the following results. 1) We first introduce a min-max type objective function for systems with additive disturbance and characterize the maximizing disturbance sequence. Based on this result, we have obtained a reduced order model predictive control with disturbance attenuation property for input constrained linear systems. We further derived an MPC for systems with both input and state constraints by assuming the bounded disturbance. 2) We have investigated the relationship between the pair of a continuous-time input signal and controller state and corresponding output for a closed-loop system. We then clarified the pairs which have dominant effect on input-output relationship of the system. A compensation law that satisfies systems constraints is obtained based on this result.