Search results

The purpose of this paper is to develop sliding mode control with linear and nonlinear manifolds in discrete‐time domain for robot manipulators.

First, a discrete linear sliding mode controller is designed to an n‐link robot based on Gao's reaching law. In the second step, a discrete terminal sliding mode controller is developed to design a finite time and high precision controller. The stability analysis of both controllers is presented in the presence of model uncertainties and external disturbances. Finally, sampling time effects on the continuous‐time system outputs and sliding surfaces are discussed.

Computer simulations on a three‐link SCARA robot show that the proposed controllers are robust against model uncertainties and external disturbance. It was also shown that the sampling time has important effects on the closed loop system stability and convergence.

The proposed controllers are low cost and easily implemented in practice in comparison with continuous‐time ones.

The novelty associated with this paper is the development of an approach to finite time and robust control of n‐link robot manipulators in discrete‐time domain. Also, obtaining an upper bound for the sampling time is another contribution of this work.

This paper aims to address a new iterative tuning method of PID control for robot manipulators.

This tuning method uses several properties of the robot control, such as any PD control can stabilize a robot in regulation case, the closed-loop system of PID control can be approximated by a linear system, the control torque to the robot manipulator is linearly independent of the robot dynamic.

Compared with the other PID tuning methods, this novel method is simple, systematic, and stable. The transient properties of this PID control are better than the other normal PID controllers.

In this paper, a new systematic tuning method for PID control is proposed. The paper applies this method on an upper limb exoskeleton, and real experiment results give validation of our PID tuning method.

This paper presents the real‐time visual servoing of a manipulator and its tracking strategy of a fish, by employing a genetic algorithm (GA) and the unprocessed gray‐scale image termed here as “raw‐image”. The raw‐image is employed to shorten the control period, since it has more tolerance of contrast variations occurring within an object, and between one input image and the next one. GA is employed in a method called 1‐step‐GA evolution. In this way, for every generational step of the GA process, the found results, which express the deviation of the target in the camera frame, are output for control purposes. These results are then used to determine the control inputs of the PD‐type controller. Our proposed GA‐based visual servoing has been implemented in a real system, and the results have shown its effectiveness by successfully tracking a moving target fish.

The aim of this paper is to prove that the manipulator is able to contact the environment compliantly, and reduce the instantaneous collision impact.

Cartesian impedance control law is introduced to interrelate the external force with the Cartesian position.

When the estimated external force sensor feedback is the input of the on‐line trajectory regeneration, a novel online motion plan could be performed in a task‐consistent manner keeping the interaction force within the acceptable tolerance. The proposed approach also proves that the manipulator is able to contact the environment compliantly, and reduce the instantaneous collision impact. The virtual decomposition control, simplifying the Cartesian impedance control application of the manipulator and guaranteeing the asymptotical stability of the entire system, is implemented to actualize the approach. Furthermore, adaptive dynamics joint controller is extended to all the joints for complementing the biggish friction.

With the proposed adaptive Cartesian impedance control and the online path planner, the robot will be manipulation‐friendly in an unstructured environment.

Manipulators are often subjected to joint flexibility caused by various causes in industrial applications, such as shaft windup, harmonic drives and bearing deformation. However, many industrial robots are only equipped with motor-side encoders because link-side encoders and torque transducers are expensive. Because of joint flexibility and resulted slow response rate, control performance of these manipulators is very limited. Based on this, the purpose of this paper is to use easy-to-install and cheap accelerometers to improve control performance of such manipulators.

First, a novel tip-acceleration feedback method is proposed to avoid amplifications of approximation errors caused by inversion of the Jacobian matrix. Then, a new control scheme, consisting an artificial neural network, a proportional-derivative (PD) controller and a reference model, is proposed to track motor-side position and suppress link-side vibration.

By using the proposed tip-acceleration feedback method, each link’s vibration can be suppressed correlatively. Through the networks, smaller motor-side tracking errors can be obtained and unknown dynamics can be compensated. Tracking and convergence performance of the network-based system can be improved by using the additional PD controller.

The originality is based on using accelerometers to improve link-side vibration suppression and control performance of flexible-joint manipulators. The previously used methods need expensive link-side sensors or accurate robot model, which is unavailable for many industrial robots only equipped with motor-side encoders. The report proposed a novel acceleration feedback method and used networks to solve such problems.

This paper is concerned with the repetitive trajectory tracking control for space manipulators under model uncertainties and vibration disturbances.

The model uncertainties and link vibration of manipulators will degrade the tracking performance of space manipulators; in this paper, a new hybrid control scheme that consists of a composite hierarchical anti-disturbance controller and an iterative learning controller is developed to solve this problem.

The composite hierarchical controller can effectively attenuate model uncertainties and reject vibration disturbances, whereas the iterative learning controller is able to improve the tracking accuracy for repetitive reference trajectory.

The proposed scheme compensates for the shortcomings of iterative learning control which can only deal with repetitive disturbances, ensuring the accuracy and repeatability of space manipulators under model uncertainties and random disturbances.

The purpose of this paper is to present the control of a six degrees of freedom (DOF) robot arm (PUMA robot) using fuzzy PD + I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID and fuzzy PID controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using fuzzy PD + I controller combination than fuzzy PID controller.

Control of a six DOF robot arm (PUMA Robot) using fuzzy PD + I controller.

The performance of fuzzy PD + I controllers improves appreciably compared to their respective fuzzy PID only or conventional PID counterparts.

Complexity of the proposed fuzzy PID controller is minimized as possible and only two design variables are used to adjust the rate of variations of the proportional gain and derivative gain.

The purpose of the paper is to propose an efficient and accurate force/torque (F/T) sensing method for the robotic wrist-mounted six-dimensional F/T sensor based on an excitation trajectory.

This paper presents an efficient and accurate F/T sensing method based on an excitation trajectory. First, the dynamic identification model is established by comprehensively considering inertial forces/torques, sensor zero-drift values, robot base inclination errors and forces/torques caused by load gravity. Therefore, the sensing accuracy is improved. Then, the excitation trajectory with optimized poses is used for robot following and data acquisition. The data acquisition is not limited by poses and its time can be significantly shortened. Finally, the least squares method is used to identify parameters and sense contact forces/torques.

Experiments have been carried out on the self-developed robot manipulator. The results strongly demonstrate that the proposed approach is more efficient and accurate than the existing widely-adopted method. Furthermore, the data acquisition time can be shortened from more than 60 s to 3 s/20 s. Thus, the proposed approach is effective and suitable for fast-paced industrial applications.

The main contributions of this paper are as follows: the dynamic identification model is established by comprehensively considering inertial forces/torques, sensor zero-drift values, robot base inclination errors and forces/torques caused by load gravity; and the excitation trajectory with optimized poses is used for robot following and data acquisition.

This paper aims to design a neural controller based on radial basis function networks (RBFN) for electrically driven robots subjected to constrained inputs.

It is assumed that the electrical motors have limitations on the applied voltages from the controller. Due to the universal approximation property of RBFN, uncertainties including un-modeled dynamics and external disturbances are represented with this powerful neural network. Then, the lumped uncertainty including the nonlinearities imposed by actuator saturation is introduced and a mathematical model suitable for model-free control is presented. Based on the closed-loop equation, a Lyapunove function is defined and the stability analysis is performed. It is assumed that the electrical motors have limitations on the applied voltages from the controller.

A comparison with a similar controller shows the superiority of the proposed controller in reducing the tracking error. Experimental results on a SCARA manipulator actuated by permanent magnet DC motors have been presented to guarantee its successful practical implementation.

The novelty of this paper in comparison with previous related works is improving the stability analysis by involving the actuator saturation in the design procedure. It is assumed that the electrical motors have limitations on the applied voltages from the controller. Thus, a comprehensive approach is adopted to include the saturated and unsaturated areas, while in previous related works these areas are considered separately. Moreover, a performance evaluation has been carried out to verify satisfactory performance of transient response of the controller.

The purpose of this paper is to present the control of six degrees of freedom (PUMA560) robotic arm using visual servoing, based upon linear matrix inequality (LMI). The aim lies in developing such a method that neither involves camera calibration parameters nor inverse kinematics. The approach adopted in this paper includes transpose Jacobian control; thus, inverse of the Jacobian matrix is no longer required. By invoking the Lyapunov's direct method, closed‐loop stability of the system is ensured. Simulation results are shown for three different cases, which exhibit the system stability and convergence even in the presence of large errors.

The paper presents LMI‐based visual servo control of PUMA560 robotic arm.

The proposed method is implementable in the dynamic environment due to its independence to camera and object model.

Visibility constraint is not included during servoing – this may cause features to leave the camera field of view (fov).

LMI optimization is employed for visual servo control in an uncalibrated environment. Lyapunov's direct method is utilized which ensures system stability and convergence.