Uncalibrated Visual Servoing Intelligent Control For Manipulator With Unknown Input And Output Constraints

In a visual servoing robot system,the image of control target is acquired by the visual sensor of photoelectric imaging system.Then the collected images are digitized by computers,obtaining the visual information such as image pixel distribution,brightness.Using this informations,the characteristic of image can be judged by size,shape and color,providing the visual servoing manipulator system with the advantages of high precision,high cost efficiency and flexible convenience,thus it is widely used in robot control.With the increasing growth of high-tech equipment and high-end smart technique,the degree of industrial automation will greatly depend on the application of visual servoing robot control.In this paper,the problem of visual servoing manipulator control is addressed,where the input actuator constraints and the unknown output constraints are taken into account.The research results will provide an innovative theoretical basis for the control of visual servoing robot control and provide alternative methods for practical engineering applications.This paper is structured as follows.Chapter 1 reviews the research status of visual servoing manipulator control.Chapter 2 summarizes the related background knowledge.The uncalibrated visual servoing control for manipulator with actuator input constraints is presented in Chapter 3 to Chapter 5.The adaptive neural network control for visual servoing robotic system with unknown output constraints and unmodeled dynamics is involved in Chapter 6.In Chapter 7,the problem of visual servoing control for robot with time-varying actuator constraints and system uncertainties is addressed.The detailed research contents are as follows:1.The problem of image-based visual servoing(IBVS)manipulator system with unknown hysteresis nonlinearity is investigated.An adaptive IB VS control scheme,which takes the unknown actuator hysteresis into account,is presented.Without the prior knowledge of both the intervals of hysteresis parameters and the bound of the dynamic disturbance term,a novel adaptive algorithm is developed to estimate the unknown parameters on-line.The image tracking errors are guaranteed to converge to a small neighborhood of the origin.2.The problem of adaptive tracking control for uncalibrated visual servoing manipulator system with actuator fuzzy dead-zone constrain and unknown dynamic is addressed.The fuzzy logic system is employed to approximate the unmodeled nonlinear manipulator dynamic and external disturbances without a prior knowledge of the system.By using.the recursive Newton-Euler,the total number of fuzzy rules can be reduced significantly.By defuzzifying the fuzzy slope of actuator fuzzy dead-zone model to a determined value,a fuzzy adaptive controller is constructed to eliminate the harmful effect caused by fuzzy dead-zone constrain,which is also compatible to determined dead-zone.3.The adaptive fuzzy visual tracking control problem for telecontrol led manipulator system with input quantized by the proposed saturation switch quantizer(SSQ)is addressed.The major superiority of this newly SSQ lies in its adjustable communication rate and quantization density,simultaneously taking the input saturation effect into account.By establishing a nonlinear decomposition-based scheme for the output of SSQ,the control difficulty caused by discrete quantized input is overcome successfully.In addition,the requirement of visual velocity in controller construction is removed by introducing a visual velocity observer,and thus,large image noises and computational burden are both avoided.Subsequently,without the exact knowledge of robot dynamics,a novel adaptive fuzzy visual servoing controller is developed to guarantee the boundedness of closed-loop signals and the tracking performance.4.The problem of neural network control for visual servoing robotic system is addressed,where the unmodeled dynamics and output nonlinearity are taken into account simultaneously.An adaptive neural network module is constructed to approach the unknown dynamics,upon which,the robot dynamics are not required to be linearly decomposable and structurally known.The major superiority of this module lies in its conciseness and the computational-reduction operation.Moreover,the output nonlinearity is considered,and its undesirable effect is subsequently tackled without a prior knowledge of the model parameters in output mechanism.It is proven by the Lyapunov method that the image-space tracking error is driven to an adjustable neighborhood of origin.5.The control issue for distributed visual servoing manipulators on strongly connected graph with communication delays is addressed,in which case that the uncertain robot dynamics and kinematics,uncalibrated camera model and actuator constraints are simultaneously considered.An adaptive cooperative image-based(ACrIB)approach is established to overcome the control difficulty arising from nonlinear coupling between visual model and robot agents.To estimate the coupled camera-robot parameters,a novel adaptive strategy is developed and its superiority mainly lies in the containment of both individual image-space errors and the synchronous errors among networked robots,thus,the consensus performance is significantly strengthened.Moreover,the proposed cooperative controller with a Nussbaum-type gain is implemented to both globally stabilize the closed-loop systems and realize the synchronization control objective under the existence of unknown and time-varying actuator constraints.