This paper investigates the necessity of feasibility consideration in a fault tolerant control system using the constrained control allocation methodology where both static and dynamic actuator constraints are considered. In the proposed feasible control allocation scheme, the constrained model predictive control is employed as the main controller. This considers the admissible region of the control allocation problem as its constraints. Using the feasibility notion in the control allocation problem, provides the main controller with information regarding the actuators status which leads to the closed loop system performance improvement. Several simulation examples under normal and faulty conditions are employed to illustrate the effectiveness of the proposed methodology. The main results clearly indicate that closed loop performance and stability characteristics can be significantly degraded by neglecting the actuator constraints in the main controller. Also, it is shown that the proposed strategy substantially enlarges the domain of attraction of the MPC combined with the control allocation as compared to the conventional MPC.

This paper proposes a guaranteed feasible control allocation method based on the model predictive control. Feasible region is considered to guarantee the determination of the desired virtual control signal using the pseudo inverse methodology and is described as a set of constraints of an MPC problem. With linear models and the given constraints, feasible region defines a convex polyhedral in the virtual control space. In order to reduce the computational time, the polyhedral can be approximated by a few axis aligned hypercubes. Employing the MPC with rectangular constraints substantially reduces the computational complexity. In two dimensions, the feasible region can be approximated by a few rectangles of the maximum area using numerical geometry techniques which are considered as the constraints of the MPC problem. Also, an active MPC is defined red selected as the controller to minimize the cost function in the control horizon. Finally, several simulation examples are employed to illustrate the effectiveness of the proposed techniques.

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