Study On Model Identification And Cooperative Control Of Dual-arm Robot Systems

Robotics has been rapidly developed as one of the most promising technologies among industrial and academic communities,and have been extensively studied in recent years.Meanwhile,coordinated control of two-arm or multi-arm robots is a key issue of robot research.This is because an accurate robot model is necessary to achieve dexterous,compliance and cooperative operations of a dual-arm robot.For example,an accurate robotic kinematic model is necessary for the transformation of the motion from the robot end-effector space to robot joint space.Many advanced control techniques such as robot force/position control,calculated torque control and impedance control are model-based.In this thesis,we establish the robot kinematics model and dynamic model using the DH method and the Newton-Euler method,respectively.Besides,the identifications of unknown kinematic and dynamic parameters are also investigated.Moreover,the control of the dual arm robot with a rigidly grasped object is studied with the guaranteed boundedness of the internal force of the grasping object.In addition,neural networks and fuzzy logic systems are employed to deal with the under unknown system dynamics.The key issue solved in the thesis as follows:(a)a novel finite-time convergence parameter identifier is proposed to estimate the unknown parameters while a model reduction method is used to solve the non-full rank problem of regression matrix,which is useful to achieve the effective estimation of the dynamic parameters.(b)a dual-arm robot controller is designed by using the barrier Lyapunov function to realize the transient control of the dual-arm robot.(c)A neural network control mechanism is developed by using the switching function to extend the uniformly ultimate boundedness(SGUUB)to the uniformly ultimate boundedness(GUUB).Overall,this thesis focuses on the problems of coordinated control of dual-arm robots control,kinematics and dynamics modelling with unknown parameters identification,model uncertainty and coordinated control design,to establish effective dual-arm robot controllers.Specifically,the main contributions of this paper include three aspects.1)For parameter identifications of robot systems,most existing works have focused on the estimation veracity,but few works of literature are concerned with the convergence speed.In this thesis,we developed a robot control/identification scheme to identify the unknown robot kinematic and dynamic parameters with enhanced convergence rate.Superior to the traditional methods,the information of parameter estimation error was properly integrated into the proposed identification algorithm,such that enhanced estimation performance was achieved.Besides,the Newton–Euler method was used to build the robot dynamic model,where a singular value decomposition(SVD)-based model reduction method was designed to remedy the potential singularity problems of the NE regressor.Moreover,an interval excitation condition was employed to relax the requirement of persistent excitation condition for the kinematic estimation.By using the Lyapunov synthesis,explicit analysis of the convergence rate of the tracking errors and the estimated parameters were performed.2)Robots with coordinated dual arms are able to perform more complicated tasks that a single manipulator could hardly achieve.In this thesis,a prescribed tracking performance at both transient and steady states is first specified,and then a controller is synthesized to rigorously guarantee the specified motion performance.In the presence of unknown dynamics of both the robot arms and the manipulated object,the neural networks approximation technique is employed to compensate for uncertainties.In order to extend the conventional SGUUB to GUUB,a switching mechanism is integrated into the control design to guarantee the control performance outside the active domain of the neural network.3)In this thesis,an adaptive fuzzy control scheme is developed for a dual arm robot,where an approximate Jacobian matrix was applied to address the uncertain kinematic control,while a decentralized fuzzy logic controller was constructed to compensate for uncertain dynamics of the robotic arms and the manipulated object.Also,a novel finite-time convergence parameter adaptation technique was developed for the estimation of kinematic parameters and fuzzy logic weights,such that the estimation was guaranteed to converge to small neighbourhoods around their ideal values in a finite time.Moreover,a partial persistent excitation property of Gaussian membership based fuzzy basis function was established to relax the conventional persistent excitation condition.This enables the designer to re-use these learned weight values next time without relearning.