Research on allocation efficiency of the redistributed pseudo inverse algorithm

Abstract

Allocation efficiency is an important performance index to measure the quality of the allocation algorithm. In order to compute the efficiency, the volume of the subset of attainable moments must be solved. The efficiency of the redistributed pseudo inverse (RPI) algorithm depends on the choice of the pseudo-inverse matrix. The subset of attainable moments of RPI is a complex non-convex polyhedron. By analyzing twodimensional and three-dimensional allocation problems with a "micro-element" method, here we propose an approximate calculation algorithm to compute the volume of the non-convex polyhedron. In order to improve the allocation efficiency of RPI, genetic algorithm is used to find the best pseudo-inverse matrix. The simulation results show that the best pseudo-inverse matrix can be easily chosen by the proposed method and the high allocation efficiency is achieved.

Control allocation—A survey

“…(Virnig & Bodden 1994, Shi, Zhang, Li & Liu 2010) is to solve the unconstrained control allocation problem, such as (12) (or a simpler version). If the solution satisfies the constraints, no further steps are needed.…”

Sliding‐mode fault‐tolerant control using the control allocation scheme

“…Furthermore, several methods have been proposed for CA including convex optimization‐based algorithms, daisy chaining, direct CA, and redistributed pseudoinverse methods …”

“…However, it is impossible to represent these optimization‐based methods in an explicit form. As a result, it would be hard to analyze the robustness of the closed‐loop system against the uncertainties occurring due to the errors that may exist in the computational algorithms used to allocate the virtual control signals, as well as the uncertainties emanating from the visibility of the allocator to the controller. The other approach that is able to cope with actuator saturation limits is redistributed pseudoinverse‐based CA . However, in the existing redistributed pseudoinverse‐based algorithms, the number of iterations; thus, computation time needed to find the optimal solution may not take into account the limitations of computational resources in terms of meeting real‐time computation constraints. Using the sliding‐mode control (SMC) to develop a FTC method has been considered in the literature.…”

“…7,8 Furthermore, several methods have been proposed for CA including convex optimization-based algorithms, [9][10][11] daisy chaining, 12 direct CA, 13 and redistributed pseudoinverse methods. 14,15 This paper aims to overcome several major challenges in the field of CA-based FTC as listed in the following.…”

Allocation-based control for four-wheel independently driven and braked electric vehicle considering actuators' dynamic characteristics

“…Some relatively simple control allocation techniques, such as pseudo inverse method, suffer from difficulties in guaranteeing that the limits mentioned above will not be violated [4]. Redistributed pseudo-inverse(RPI) method often used in dealing with constrained control allocation problem [5], but it is not effective enough when all actuators reach their limitations. Direct allocation method generates control commands for actuators that result in the approximation of the desired body forces [6], however the process of numerical solution becomes tedious and computationally prohibitive when the dimension of control variables is large.…”