Research On Adaptive Control Of Multi-joints Robot With Constraint Environment

Robot is a time-varying,coupled complex nonlinear system.Robot manipulators have received much attention from researchers all over the world in both theoretical research and real world applications,and many studies have been reported.But the further development of human society needs more intelligent robots.In this paper,adaptive neural network controllers are designed for the uncertain multi joint robotic manipulator systems with constraints,while the trajectory tracking control of the manipulator is realized.The following work has been completed:1.For the uncertain robotic manipulator system with time-varying output constraints on the joint space,the original constrained system is converted into a new system without constraint by a nonlinear state transformation.Then the Backstepping scheme is adopted for the new system,with the appropriate virtual controller is constructed at each step.The unknown nonlinear function of the robot system is approximated by RBF neural network,and finally the Adaptive Neural Network(ANN)control law satisfying the requirement is obtained.The stability analysis is carried out via the Lyapunov stability theory and it is proved that all the signals are semi-globally uniformly ultimately bounded(UUB)and the output constraints are not violated.Finally,a two link planar manipulator is taken as an example to verify the simulation.2.We investigated adaptive tracking control in task space for robot manipulators with uncertain system dynamics,input saturation,and time-varying output constraints simultaneously.An auxiliary system is constructed to compensate the effect of the input saturation,and an asymmetric barrier Lyapunov function(BLF)is applied to tackle timevarying output constraints,while radial basis function(RBF)neural networks(NN)are used to approximate the unknown closed-loop dynamics.By introducing a disturbance observer(DO),unknown external disturbances from humans and environment are estimated,and NN approximation errors are compensated.A novel adaptive NN tracking controller is designed to guarantee all signals in the closed-loop system are semi-globally UUB,while the tracking errors and observer errors converge to a small neighborhood of zero,and the time-varying output constraints are not violated.Finally,some simulation results are presented to verify the effectiveness and superiority of the proposed control scheme.3.On the basis of the previous two chapters,the adaptive tracking control of a flexiblejoint robot with time-varying output constraints,unknown time delays,uncertain system dynamics,input saturation and external disturbances simultaneously is investigated.Radial basis function neural networks(RBF NNs)are employed to approximate uncertain closed-loop system dynamics,while an asymmetric BLF is applied to tackle time-varying output constraints.Moreover,the appropriate Lyapunov-Krasovskii functionals(LKF)and a variable separation approach are used to compensate for the effect of unknown time delays,and a dynamic system is introduced to deal with input saturation.With the help of the Lyapunov stability analysis,it is shown that the proposed control scheme can achieve perfect tracking performance without violating output constraints and input saturation,while the semi-globally UUB of all signals in the closed-loop system is guaranteed.Finally,some simulation results are given to verify the effectiveness and superiority of the proposed control scheme by controlling the flexible-joint robots.Last but not the least,the content of this paper is summarized,and the research on related fields in the future is prospected.