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In this study, we proposed a type 2 fuzzy neural controller (FNC) based on Bayesian dynamic group particle swarm optimization (BDGPSO) for the navigation control of Ackermann steering vehicles. The type 2 FNC has a five-layer network architecture. The advantages of the proposed BDGPSO algorithm are that it prevents PSO from falling into a local optimum, and it uses the Bayesian algorithm to determine the optimal inertia weight and learning factor combination. To evaluate the proposed type 2 FNC on the basis of the distance information returned by lidar, we used a new fitness function from the reinforcement-learning strategy. In an unknown testing environment, the fitness value and time required for the proposed type 2 FNC with angular and linear velocity outputs were 0.973510 and 25.555 s, respectively. These values indicated that the proposed approach outperforms other wall-following control methods. In addition, the proposed controller could successfully control the navigation of an Ackerman steering vehicle in an unknown actual environment.
Lin et al. (2023) studied this question.