BLF-based Output Constraint Control For Robot Manipulators With Input Saturation

With the extensive use of robot manipulators in manufacturing,research on the control of robot manipulators has also received increasing attention.When the robot manipulator executes a task,it often needs to satisfy the constraints of joint physical limit,collision avoidance,and output errors,to ensure the safety during the operation and the accuracy of the task.Input saturation nonlinearity is a non-linear constraint commonly found in control systems,which may cause the manipulator to violate the predefined output constraints.Therefore,it is necessary to consider the manipulator output constraint control in the case of input saturation.This thesis discusses the output constraint and input saturation control for the robot manipulator based on the barrier Lyapunov function(BLF).The main work of this thesis are as follows:First,a tan-BLF-based output control scheme for the robot manipulator is proposed,which takes into account factors such as output error constraints,unknown dynamics,and bounded external disturbances.This scheme combines the method of composite learning(CL),which utilizes the simultaneous tracking error and the prediction error constructed by historical infor-mation to form the update law of the radial basis function neural network so that the network weights can converge when the system signal meets the interval excitation condition.Lyapunov stability analysis verifies that the proposed control scheme can ensure that the trajectory tracking error remains within a predefined boundary.Experimental results demonstrate the superiority of the proposed controller in parameter estimation and tracking capabilities.Secondly,this thesis advances to explore the limitations of the BLF-based constraint con-troller design that subjects to the feasibility conditions.In consideration of input saturation,the designed BLF-based controller may cause system instability.Therefore,we regard the output error constraint as a kind of “soft constraint”,proposes a bounded barrier Lyapunov function(BBLF)and discusses the effectiveness of BBLF in processing hyperplane constraints.Simula-tion is carried out to verify its validity in handling output constraints.Finally,based on the tan-BLF-based control scheme,this thesis develops the output con-straint and input saturation control scheme based on BBLF.It adopts BBLF to process the output constraints and designs an auxiliary system to suppress the input saturation effect.In addition,the control scheme uses a simplified RBFNN structure to approximate and cancel the lumped uncertainty of the system.Simulation and experimental results prove the effectiveness of the control scheme in dealing with output constraints and input saturation.