Adaptive RBFNN Formation Control of Multi-mobile Robots with Actuator Dynamics - Li Yan-dong; Zhu Ling; Sun Ming (Institute of Advanced Engineering and Science, 2013)
We study the problem of formation control and trajectory tracking for multiple nonholonomic mobile robots with actuator and formation dynamics. An adaptive neural-network (NN) control strategy that integrated kinematic controller with input voltages controller of actuator was proposed. A control law was designed by backstepping technique based on separation-bearing formation control structure of leader-follower. The radial basis function neural network (RBFNN) was adopted to achieve on-line estimation for the dynamics nonlinear uncertain part for follower and leader robots. The adaptive robust controller was adopted to compensate modeling errors of NN. This strategy not only overcomed all kinds of uncertainties of mobile robots, but also ensured the desired trajectory tracking of robot formation in the case of maintaining formation. The stability and convergence of the control system were proved by using the Lyapunov theory. The simulation results showed the effectiveness of this proposed method. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2334