Particle Swarm Optimizer based controller design for Vehicle Navigation System
The purpose of this paper is to develop particle swarm optimizer (PSO) based controller for vehicle navigation system (VNS). This paper regards the mathematical model of car-like mobile robot as the vehicle dynamics behavior to develop the controller for VNS. VNS controls the vehicle via two actual control values, one is the degree variation of the front wheel and the other is the acceleration variation of the vehicle versus time respectively. The proposed algorithm is to find the best feedback gain values of the controller for VNS after two analysis process. First, input-output linearization is applied to transform non-linear dynamic model of vehicle to a linear system through states transformation. Contained within the linear system is a set of pseudo control variables; there is a transformation relationship through the decoupling matrix between the vehicle's actual control variable and the pseudo control variable. Second, a pseudo control is generated by designing a state feedback controller of the linear system. Then the best feedback gain values are determined by particle swarm optimizer (PSO). At once the best feedback gains are found by the PSO, pseudo control could be transformed to the actual control applied to the vehicle through a decoupling matrix. In the paper, the controller for VNS constructs a precise and optimized vehicle's automatic navigation strategy and meets the requirement of system closed-loop stability. This paper uses Matlab as the simulation environment, the simulation results show that the proposed method makes vehicle steering in the crooked change road conditions. The proposed algorithm can adjust the vehicle's trajectory to the road trend and can provide a stable driving strategy for driver.