Trajectory Tracking Of Robotic Systems Actuated By DC Motors Under Stochastic Disturbances And Its Applications

For stochastic robotic systems actuated by direct current(DC)motors,this paper is devoted to mainly study the trajectory tracking control problem under different stochastic driving environments and its applications.The main work of this paper consists of the following aspects:(1)Multi-stage switched tracking control of robotic systems under stochastic disturbances Considering that the stochastic disturbance is Wiener process and its formal derivative is white noise,for stochastic robotic systems composed of Lagrange subsystems driven by DC motors,the multi-stage switched tracking control is investigated.In order to achieve the end-effector drive of robotic systems from one point to another,a multi-stage trajectory is planned by the initial and the end configurations for different stages.The adaptive backstepping controller is designed for stochastic Lagrangian subsystems,then a multi-stage switching control strategy is proposed for trajectory tracking of the robotic system.Based on stochastic switched stability,the closed-loop error switched system is globally asymptotically practically stable in probability,and the mean square of tracking error can be made arbitrarily small enough by parameters-tuning.The reasonability of assumptions and the e ciency of the controller are demonstrated by a mechanical system in stochastic surroundings.(2)Command filter-based adaptive fuzzy tracking control of robotic systems under full state constraints and stochastic disturbances For stochastic robotic systems actuated by DC motors,whose stochastic disturbance is a Wiener process,the command filter-based adaptive fuzzy tracking control with full state constraints is investigated.In the proposed approach,the command filter technique is used to solve the “complexity explosion” problem in the traditional backstepping process,and compensation signals are introduced to eliminate the error influence caused by command filters.The fuzzy logic system is adopted to approximate unknown nonlinear functions in robotic systems.Based on Barrier Lyapunov functions(BLFs),the adaptive fuzzy tracking controller is designed such that all signals in the closedloop system are bounded almost surely,the state constraints are not breached almost surely and the tracking error converges to an arbitrarily small neighborhood of zero.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.(3)Adaptive practical trajectory tracking control of cart-pendulum systems driven by colored noises For the cart-pendulum system actuated by DC motors driven by colored noises,adaptive practical trajectory tracking is investigated.The effect of random disturbances can be considered as torque or voltage based on the principles of relative motion and equivalent circuit,and thus the random model of the cart-pendulum system driven by colored noise is constructed via the Lagrange equation.The adaptive backstepping sliding mode controller is designed for trajectory tracking by the vectorial backstepping technique,sliding mode technique and the noise separating technique from coupled terms.Based on random system stability,the closed-loop error system is noise-to-state-practically stable in probability(NSp S-P),and the tracking error in mean square converges to an arbitrarily small neighborhood of zero by parameters-tuning.Simulation results illustrate the effectiveness of the proposed controller.