Optimal direct adaptive soft switching multi-model predictive control using the gap metric for spacecraft attitude control in a wide range of operating points

In this paper, a new Multi-Input Multi-Output (MIMO) Multi-model Predictive Control (MMPC) with direct adaptive structure is used for spacecraft attitude control in a wide range of operating points and maneuvers. Because of the highly nonlinear dynamics, the linearized model characteristics are extremely depended to the operating points. In such cases, an expected performance, in the wide range of the operating points, never can be achieved using a single controller and single model (even instability may be anticipated). To handle this problem, in this paper, we divide the whole operating range of the spacecraft to construct sub-models (model bank) using a mathematical tool called as gap metric. Next, an adaptive MPC based on sub-models is designed. In this procedure, there are two problems: stability when switching among models and the minimum number of sub-models. Hard switching among models to update the controller's model causes extreme chattering on the control signal and reference tracking. The motivation of this paper is to present a new solution for the mentioned problems. To solve the first problem (remove chattering), an adaptive soft switching law to tune the controller parameters, when selecting new model, based on the Lyapunov theory is introduced as the main novelty of this paper. This guarantees the stability of the closed loop control system. For solution of the second problem, the number of optimal sub-models is obtained using different simulations. Finally, the effectiveness of the suggested method is proven via simulations.