Zhen Mei

In this paper, a fuzzy neural network based predefined-time trajectory tracking control method is proposed for the tracking problem of omnidirectional mobile robots (FM-OMR) with uncertainties. Considering the requirement of tracking error convergence time, a position tracking controller based on predefined-time stability is proposed. Compared with the traditional position tracking control method, the minimum upper bound of the convergence time can be explicitly set. In order to obtain more accurate angular velocity tracking, the inner loop controller combines Type 1 fuzzy neural network (T1FNN) to estimate the uncertainty. In addition, considering the problem of feedback channel noise, a Kalman filter combining velocity and position information is proposed. Finally, the simulation results verify the effectiveness of this method.

To solve the dimensional explosion problem, this paper proposes a new architecture for the fuzzy system, the deep hierarchical self-organizing interval type-2 fuzzy system (DHSOIT2FS). Each sub-fuzzy system is a self-organizing interval type-2 fuzzy system, constructed online, with rules constructed by a rule online update algorithm, consequent parameters updated by iterative least squares, and antecedent parameters are updated using a gradient descent algorithm. DHSOIT2FS uses a classic serial-layered structure to build the overall framework. The first layer uses the first two dimensions of data as input. Each subsequent layer uses the output of the previous layer with the next dimensional data as input until it is built. During the training process, each data point is trained with DHSOIT2FS before passing in the next data point to achieve online construction. The effectiveness of the approach in this paper is illustrated using two numerical simulation examples. The proposed method is also applied to a data-driven control example of a single-link robot and achieves good tracking results.