Command Filter-Based Adaptive Fuzzy Tracking Control of Stochastic Robotic Systems with Full State Constraints

For stochastic robotic systems actuated by direct current (DC) motors with full-state constraints, adaptive trajectory tracking is investigated in this paper. The main challenge is how to deal with the highly coupled terms with the stochastic disturbance and unknown nonlinear functions. First, as an approximate instrument of unknown nonlinear functions, the fuzzy logic system is adopted to tackle the uncertainty of stochastic robotic systems. Second, the command filter technique is used to solve the “complexity explosion" problem in the traditional backstepping process, and error compensation mechanisms are introduced to eliminate the error influence caused by command filters. Then, based on barrier lyapunov functions (BLFs), the adaptive fuzzy tracking controller is designed such that the state constraints are not breached almost surely, all signals in the closed-loop system are bounded almost surely and the first-order moment of the tracking error converges to an arbitrarily small neighborhood of zero. Finally, the tracking problem of the two-link planar manipulator in stochastic surroundings is analysed to demonstrate the effectiveness and advantages of the proposed control strategy.