Intelligent Filter of L1 Adaptive Controller Using ANFIS System Optimized with Genetic Algorithm

$\mathcal{L}_{1}$ adaptive controller is known for ensuring fast adaptation with the optimal transient performance for input and output signals using a low-pass filter with adjustable gain in the feedback loop. During choosing a filter gain, the main criteria are the tradeoff between the performance, robustness and fast adaptation. There are several methods with different complexities for determining bounds or initial values of filter gain, such values may require the use of many iterations of trial and error implemented in the controller. In these methods, the specified gain is kept constant, which leads to non-optimal performance in adaptation speed and robustness. In this paper, a new approach based on an adaptive-neural-fuzzy inference system (ANFIS) optimized with a genetic algorithm is presented to continuously determine this gain and optimal performance according to the error rate. The objective function optimized by the genetic algorithm is also determined in terms of position tracking error, speed tracking error, and control signal. To evaluate the performance, the proposed method is implemented on a nonlinear feedback system in the presence of unmatched uncertainties. The simulation results show that the controller performance improves more than the constant filter efficiency in this method.