A Q‐Learning‐Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi‐Mobile Robot Systems

Abstract

This paper proposes an adaptive formation tracking control algorithm optimized by Q-learning scheme for multiple mobile robots. In order to handle the model uncertainties and external disturbances, a desired linear extended state observer is designed to develop an adaptive formation tracking control strategy. Then an adaptive method of sliding mode control parameters optimized by Q-learning scheme is employed, which can avoid the complex parameter tuning process. Furthermore, the stability of the closed-loop control system is rigorously proved by means of matrix properties of graph theory and Lyapunov theory, and the formation tracking errors can be guaranteed to be uniformly ultimately bounded. Finally, simulations are presented to show the proposed algorithm has the advantages of faster convergence rate, higher tracking accuracy, and better steady-state performance.

Average Consensus Tracking of Weight-Balanced Multi-Agent Systems via Sampled Data

“…where ϕ i ∈ R is the constant reference input of agent i, η i ∈ R is the internal state of agent i, and the control gains are γ > 0, k p > 0, and k i > 0. In the average consensus tracking protocol (5), in order to eliminate the agent's tracking error, we introduce η i (t). It can be seen that x j (t k ) and x i (t k ) are the information measured at the sampling moment.…”

“…For the coordinated control of multiagent systems (MASs), the consensus problem has always been a hot research topic of the control community in the past several years, due to its extensive applications in many areas, such as flocking control, sensor networks, and formation control [3][4][5].…”

Path-Guided Finite-Time Formation Control of Nonholonomic Mobile Robots Based on an Extended State Observer

“…In [15], a terminal sliding mode control law was proposed to deal with model uncertainties at the dynamic level and external disturbances. In [16], a Q-learning-based adaptive sliding mode controller was proposed for the formation control of multiple mobile robots. However, the chattering of the sliding mode control in [15][16][17] causes serious wear to the mobile robots.…”

“…In [16], a Q-learning-based adaptive sliding mode controller was proposed for the formation control of multiple mobile robots. However, the chattering of the sliding mode control in [15][16][17] causes serious wear to the mobile robots. A radial basis function neural network was used to approximate the model uncertainties of multiple mobile robots in [18,19].…”

“…The stability analysis of the estimation error system ( 16) is presented in the following theorem: Theorem 2. Considering the estimation error system (16) and Assumption 3, the estimation errors are finite-time uniformly ultimately bounded as stable if the observer gains γ i1 , γ i2 , γ i3 and satisfies…”