Swarm intelligence algorithms for coordinated control of the motor-pump-valve actuator system

The performances of the airborne actuator directly affect the qualities of the flying machine. The coordinated control actuator system of the motor-pump-valve parallel connection has predominant characteristics comparing with other actuator systems of coordinated control. The motor-pump-valve actuator system is a combinational optimization problem of coordinated control, for the system has three adjustable parameters: the speed of motor, the rotation angle of the pump swashplate and the opening degree of the valve. It was investigated that the optimum weight number distribution is implemented between the speed of the motor and the rotation angle of the pump swashplate and the opening degree of the valve by applying the combination of the improved swarm intelligence Algorithms and the proportional control. The applied swarm intelligence algorithms include Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO). The ant colony and the bee colony are able to self-organize, and ACO Algorithm and BCO Algorithm are optimization algorithms based on intelligent behavior of ants swarm and bees swarm. The Bee colony and the ant colony systems are highly flexible and fault tolerant in their foraging behavior. The validity of the combination of two improved algorithms and proportional control has been confirmed by simulation. Comparing with the simulation results, the improved algorithms have obvious advantages, and the total performance of the improved BCO Algorithm is much better than the one of the improved ACO algorithm.