Research On Adaptive Impedance Control Algorithm Of Manipulators In Unknown Environment

Manipulators are widely used in various occasions because of their high efficiency.By controlling their positions,they can complete tasks such as handling and spraying.With increasingly complex production requirements,robotic manipulators are expected to perform tasks that come into contact with the environment.For this type of work,the compliant control of the manipulators needs to be realized.In practical application,the specific information of the environment cannot be obtained accurately,and the dynamic model cannot be established accurately,which poses a great challenge to the realization of compliance control.Based on the impedance control theory and taking the multi-joint manipulators as the research object,this paper studies the compliance control under uncertain environment information and the inaccurate establishment of manipulator dynamics.Firstly,aiming at the problem that the transient performance is challenging to be controlled when the variable impedance method is adopted,the variable stiffness impedance control and the position control method based on RBFNN are combined.For the variable stiffness,the error of contact force is taken as the optimization goal,and the gradient descent method is used to solve the variation,which can avoid significant impact during contact.Combined with the PI control method,the position control law is designed.On this basis,RBFNN is introduced to approach the uncertain part of manipulator dynamics to reduce the position control error caused by parameter perturbation,external disturbance,and other factors.A series of simulations verify the proposed algorithm.Secondly,in view of the steady-state error existing in impedance control,an impedance control method combined with the neural network of the manipulators is proposed.A position correction module is connected in parallel with impedance,and the Back-Stepping Design Approach is combined with the adaptive neural network to obtain the position re-correction law when the environment is unknown.To solve the problem of torque saturation,an auxiliary system is designed to weaken its influence,and a position controller is designed combined with an adaptive neural network.Thus,the control voltage that should be applied to each motor can be obtained.A Simulation verified that the proposed algorithm could effectively improve the adverse effects on the compliance control in an unknown environment.Finally,an estimation method is adopted to estimate the environment’s position and stiffness because it is difficult to determine the reference trajectory in an unknown environment.A suitable reference trajectory in Cartesian space is calculated with the expected force.The desired position can be obtained by the modify of the impedance relationship.Thus,the problem that the impedance relationship cannot be accurately adjusted when the deviation of the reference trajectory is too significant can be avoided.To speed up the position control of the manipulators,a position controller that can converge in a fixed time is designed to prevent the slow convergence caused by the significant initial error.At the same time,to improve the position control accuracy,an estimator is intended to approximate the unmodeled dynamics,including joint friction,external disturbance,and parameter perturbation in the manipulator dynamics.As a result,its adverse effect on position control is reduced.By a simulation,the performance of compliance control of the manipulators in an unknown environment is verified.