In this study, an adaptive proportional-derivative (PD) control scheme is proposed for trajectory tracking of multidegree-of-freedom Robot Manipulators in the presence of model uncertainties and external disturbances whose upper bounds are unknown but bounded. The developed controller takes the advantages of linear control in the sense of simplicity and easy design, but simultaneously possesses high robustness against model uncertainties and disturbances while avoiding the necessity of precise knowledge of the system dynamics. Due to the linear feature of the proposed method, both the transient and steady-state responses are easily controlled to meet desired specifications. Also, an adaptive law for control gains using only position and velocity measurements is introduced so that parameter uncertainties and disturbances are successfully compensated, where the prior knowledge about their upper bounds is not required. Stability analysis is conducted using the Lyapunov’ s direct method and brief guidelines on how to select control parameters are also provided. Simulation results corroborate that the adaptive PD control law proposed in this paper can achieve a fast convergence rate, small tracking errors, low control effort, and small computational cost and its performance is compared with that of an existing nonlinear sliding mode control method.

Background and Objectives: This paper presents a robust passivity-based voltage controller (PBVC) for Robot Manipulators with n degree of freedom in the presence of model uncertainties and external disturbance. Methods: The controller design procedure is divided into two steps. First, a model-based controller is designed based on the PBC scheme. An output feedback law is suggested to ensure the asymptotic stability of the closed-loop error dynamics. Second, a regressor-free adaptation law is obtained to estimate the variations of the model uncertainties and external disturbance. The proposed control law is provided in two different orders. Results: The suggested controller inherits both advantages of the passivity-based control (PBC) scheme and voltage control strategy (VCS). Since the proposed control approach only uses the electrical model of the actuators, the obtained control law is simple and also has an independent-joint structure. Moreover, the proposed PBVC overcomes the drawbacks of torque control strategy such as the complexity of manipulator dynamics, practical problems and ignoring the role of actuators. Moreover, computer simulations are carried out for both tracking and regulation purposes. In addition, the proposed controller is compared with a passivity-based torque controller where the simulation results show the appropriate efficiency of the proposed approach. Conclusion: The robust PBVC is proposed for EDRM in presence of external disturbance. To the best of our knowledge, it is the first time that a regressor-free adaptation law is obtained to approximate the lumped uncertainties according to the passivity-based VCS. Moreover, the electrical model of the actuators is utilized in a decentralized form to control each joint separately.

In this paper we present a robust hybrid motion/force control1er for rigid Robot Manipulators. The main contribution of this paper is that the proposed hybrid control system is able to at complies motion objectives in free directions and force objectives in constrained directions under parametric uncertainty both in Robot dynamics and stiffness constraint constant. Also, the given scheme is proved global1y stable in the sense that the control objectives are achieved asymptotically, when a signum function is used in the control law, though giving rise to chattering effects. To avoid this problem a saturation function is used. In this case the motion and force errors are proved to be bounded functions. Using the proposed control structure there is no need to measure the derivative of the interaction forces. Some simulation results are given to illustrate the control system performance.

In this paper, a new method for robust control of Robotic arm position using particle swarm optimization is proposed. The whole Robotic system, including the Robot arm and motors, is considered in the control problem. The main purpose of this paper is to obtain an optimal estimate of the parameters of the control law in order to achieve the minimum tracking error that congestion optimization is used. Also, the design of the control law is based on the nominal model. The real model uses intelligent systems. Optimal robust control is confirmed by convergence analysis. The stability of the system is demonstrated using Lyapunov's direct method, and the simulation results show the effectiveness of the proposed methods applied to a spherical Robot driven by permanent magnet dc motors. Using the simulation results, the optimal values of the parameters in the torque controllers have not converged to their true values due to the large modeless dynamics, while they have converged to their true values in the voltage control because it has only parametric uncertainty. The torque control law also requires position vector, velocity vector, and acceleration vector feedback. These feedbacks cannot be easily obtained. In contrast, the law of voltage control requires feedback from the position vector, velocity vector, current vector, and time derivative. These feedbacks can be easily accessed.

The issue of position/force control of collaborative Robotic systems moving a payload is proposed in this paper. The proposed approach must be able to maintain the orientation/position of the payload on the reference trajectory while applying a limited force to the object through the Robot's end-effector. With this in mind, linear/nonlinear PID control schemes have been proposed to achieve accurate and robust tracking performance. Lyapunov's stability analysis is utilized to confirm the stability of the controlled system. It proves that the controlled system is stable, while the object’s orientation/position tracking errors are uniformly ultimately bounded (UUB) in any bounded region of state space. It also presents some conditions for proper selection of the linear/nonlinear PID controllers’ gains in the form of two theorems. The proposed controllers apply to two coordinated 3DOF Robotic arms that carry a payload. The simulation results tested two types of trajectories, including simple and complex paths. The results are also compared to those of a strong state-of-the-art approximator, the Chebyshev Neural Network (CNN).

In this paper, a joint position force controller is used to control a 6R general purpose Robot manipulator. The manipulator comes into interaction with a spherical object in a numerically simulated environment. A controller has been implemented using the MATLAB Simulink software which uses the Simmechanics second generation toolbox. A useful numerical contact model is used for modelling the interaction between the manipulator’ s end effector and the environment which generates the interaction feedback force s. The control algorithm presented in this paper is developed in the Cartesian space and the original control algorithm was modified to satisfy the desired input position in the base coordinate frame. The control algorithm was verified using a virtual envi ronment, before hardware implementation. The novelty of the controller is determining the input tactile forces for the Robot without actually causing a collision between the end effector and the object in the environment which can lead to fracture and dama ge to the environment or the manipulator. The modeling process of interaction with the spherical environment was investigated using Simmechanics to model precise mechanical characteristics of manipulator that are unknown to the designers and provide a grea t advantage in the simulation for them. The considered position and tactile force were tracked successfully with good accuracy. The results show that the proposed manipulator system controls the position and force with more than 95% accuracy and the accuracy of desired tracing trajectory is 99%.

This paper presents a stable new algorithm for force/position control in Robot Manipulators. In this algorithm, position vectors are measured by sensors and then used in the control law. Since using force sensor has some issues such as high costs and technical problems, an approach is presented to overcome these issues. In this respect, force sensor is replaced by an adaptive fuzzy estimator to estimate the external force based on position and velocity measurements. In this approach, force can be properly estimated using universal approximation property of fuzzy systems. Therefore, Robots can be controlled in different environments even when no exact mathematical model is available. Since this approach is adaptive, accuracy of the system can be improved with time. Through a theorem the stability of the control system is analyzed using Lyapunov direct method. At last, satisfactory performances of the proposed approach are verified via some numerical simulations and the results are compared with some previous approaches.

Traditional Robot Manipulators that have large links need powerful actuators and their massive structures strongly limit their operating speed. Flexible Manipulators having lightweight links are designed to overcome these disadvantages and in this case their flexibility is an important and unavoidable characteristic. In this paper, a new approach to finite element modeling of flexible Manipulators, using Hamiltonian mechanics and simulation of their dynamic behavior is presented. The finite element model includes all non-linear terms, such as dynamic interactions between linkages. A computer program is developed in the MATLAB medium to simulate the effects of flexibility on the Robot’s motion quality. Our results indicate the importance of flexibility and existence of considerable errors in the end-effector's positions.