Guaranteed feasible control allocation using model predictive control

Abstract

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.

Dual-layer optimization-based control allocation for a fixed-wing UAV

“…This implies that the infeasible virtual control commands may exist and would affect the stability of the whole control system. Actually, if we break the modular design objective, several advanced control methods considering actuator constraints are able to deal with this theoretical issue, e.g., the barrier Lyapunov function (BLF) based control [39,40] and MPC [20,41,42]. The BLF method exploits the property that a barrier function grows to infinity whenever its arguments approaches some limits.…”

“…Different from this classic paradigm, there are some interesting results considering the effect of the references. For example, in [42], the feasible region of desired virtual instructions is determined by characterizing the polyhedral feasible region of the pseudoinverse solution. In [41], the desired states and inputs are parameterized first, and are regarded as new decision variables in the optimization.…”

Time‐varying sliding mode controller for over‐actuated systems with constrained and uncertain actuators in flight control applications

“…27 Optimization-based control allocation is another commonly used method of accounting for actuator magnitude and rate constraints. 1,6,13,28,29,31,45,46 Furthermore, a control allocation approach by Naderi et al, 47 employs model predictive control to handle actuator magnitude constraints. In order to allocate control signals in the presence of uncertainty as well as actuator constraints, an adaptive control allocator for constrained systems has been developed by Tohidi et al 38,40 An adaptive control allocator which exploits a modified projection algorithm to handle magnitude and rate constraints in over-actuated systems is proposed by Tohidi et al 48 Although control allocation methods enable modularity for the overall control system design, as they separate the generation of the control signal and its distribution, control allocation errors can be significant in transients and degrade the performance.…”

Adaptive control allocation for constrained systems

“…. , r, and confine them to this set (Corradini, Cristofaro, & Orlando, 2010;Naderi, Sedigh, & Johansen, 2019;Tarbouriech, Garcia, da Silva Jr, & Queinnec, 2011). However, when an adaptive approach is utilized to solve (i), restricting control signals in an attainable set may not prevent actuator saturation.…”