Abstract
In this study, we proposed an interval type-2 fuzzy neural network (IT2FNN) based on an improved particle swarm optimization (PSO) method for prediction and control applications. The noise-suppressing ability of the proposed IT2FNN was superior to that of the traditional type-1 fuzzy neural network. We proposed dynamic group cooperative particle swarm optimization (DGCPSO) with superior local search ability to overcome the local optimum problem of traditional PSO. The proposed model and related algorithms were verified through the accuracy of prediction and wall-following control of a mobile robot. Supervised learning was used for prediction, and reinforcement learning was used to achieve wall-following control. The experimental results demonstrated that DGCPSO exhibited superior prediction and wall-following control.
Highlights
Neural networks (NNs) and fuzzy NNs (FNNs) have been widely applied in various applications involving system identification prediction and control
Dynamic system identification was performed to change the parameters of the interval type-2 fuzzy neural network (IT2FNN) using the proposed dynamic group cooperative particle swarm optimization (DGCPSO)
An IT2FNN based on DGCPSO learning was proposed for identification, prediction, and control applications
Summary
Neural networks (NNs) and fuzzy NNs (FNNs) have been widely applied in various applications involving system identification prediction and control. Fuzzy logic exhibits high toughness and noise-suppressing ability [3]. Reinforcement learning has been used in autonomous driving technology to provide convenience and to avoid crashes caused by driver errors. This approach has limited accuracy, and adaptive control is absent [4]. The steering angle and speed were appropriately adjusted to achieve navigation and collision safety. A proportional-integral controller was designed for adjusting the steering angle to a predetermined final desired position to achieve satisfactory navigation and obstacle avoidance [6]
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