Quasi- Input-to-State stability of Multi-Objective model predictive control for nonlinear systems with bounded disturbances

The research studies at the robust control problem of multi-objective model predictive control (MO-MPC) for nonlinear systems that are constrained and are affected by unknown but limited disturbances. A quasi-input-to-state stability (QISS) framework is formulated to ensure near-optimal performance without requiring prior knowledge of steady states or the specification of terminal constraints. The proposed analysis employs robust invariant sets in conjunction with a quasi-steady-state condition to guarantee recursive feasibility and to establish sufficient conditions for QISS stability. Compared to conventional MO-MPC, the method significantly reduces computational time while maintaining rigorous stability assurances. The theoretical results are validated through simulations on a nonlinear boiler–turbine benchmark, demonstrating the computational efficiency.