Optimization of steady-state target tracking for hybrid dual-type CV systems based on integral slope balance constraints

To address the application gap in target tracking for complex systems featuring hybrid integral-type and stable-type controlled variables, this paper proposes an improved steady-state target tracking method within a double-layer model predictive control framework. To overcome the challenge of accurately characterizing the integral properties of existing decision variables, novel decision variables $\Delta u_a$ and $\Delta u_b$ are introduced. These variables enable the precise characterization of integral-type controlled variables' steady-state predictions by indirectly expressing $\Delta u_s$. A novel feasibility determination and soft constraint adjustment strategy is subsequently proposed based on $\Delta u_a$ and $\Delta u_b$. Furthermore, to resolve the infeasibility issues arising from the dynamic slope balance constraints of integral-type controlled variables, the paper introduces new steady-state slope balance constraints that ensure multi-period slope equilibrium. Simulation results demonstrate that the proposed double-layer model predictive control method significantly enhances the feasibility of the steady-state optimization problem compared to the traditional point-model-based approach. This study provides an innovative solution for steady-state optimization in target tracking of complex systems.