Continuous interval type‐2 fuzzy Q‐learning algorithm for trajectory tracking tasks for vehicles

Abstract

Trajectory tracking is a fundamental but challenging task for vehicle automation. In addition to the system nonlinearity, the main difficulties in the trajectory tracking task are due to the environmental noise and the model uncertainties under different driving scenarios. Considering the uncertainties in the environment, the reinforcement learning method with continuous action and noise-resistance capability could be a promising way to overcome these issues. In this article, a novel continuous interval type-2 fuzzy Q-learning (CIT2FQL) algorithm is proposed to deal with the trajectory tracking task. By introducing the n-dimensional interval type-2 fuzzy inference system (n-D IT2FIS) in fuzzy Q-learning, our proposed method achieves the continuous Q-learning by combining the action interpolation with IT2FIS for the first time. We also proposed a simplified type-reduction method for n-D IT2FIS to improve the computing efficiency of the proposed method. Moreover, a radial basis function (RBF) layer is chosen as the basis function to achieve the q-value interpolation. Finally, a trajectory tracking task in a simulation environment is conducted to verify the effectiveness and robustness of the proposed method under different scenarios. The results demonstrate that the proposed method has better robustness and noise-resistance capability while maintaining good tracking performance compared with the state-of-the-art baseline algorithms including double deep Q network (DDQN), proximal policy optimization (PPO), and interval type-2 dynamic fuzzy Q-learning (IT2DFQL).

Optimization of Music Education Strategy Guided by the Temporal-Difference Reinforcement Learning Algorithm

“…The main core idea of the learning algorithm is to select the action with maximum income in a certain state according to the Q value in the Q-Table . Further, the immediate income obtained from the environmental feedback after the execution of the action is employed to update the Q value in this state [30]. The schematic diagram of the Q learning algorithm is displayed in Figure 2.…”

Enhanced gain‐scheduling fault estimation of discrete‐time Takagi–Sugeno nonlinear systems: A novel free matrix approach

“…Because of various technical reasons exist in actual measurements of electronic circuits, some of the system state variables can't be directly available in practice. [1][2][3] So, the topic of state estimation seems meaningful and valuable. 4,5 In recent years, nonlinear state estimation has been intensively addressed in the literature, such as References 6-8.…”

“…Because of various technical reasons exist in actual measurements of electronic circuits, some of the system state variables can't be directly available in practice 1‐3 . So, the topic of state estimation seems meaningful and valuable 4,5 .…”