Fractional-order position/force robot control

In this paper it is studied the implementation of fractional-order algorithms in position/force control of two cooperating robotic manipulaotrs. The performance and system robustness are analyzed in the time and frequency domains. The effect of backlash and flexibility at the robot joints is also investigated.N/

Fractional-order position/force control of two cooperating manipulators

In this paper it is studied the implementation of fractional-order algorithms in the position/force control of two cooperating robotic manipulators. The system performance is analyzed in terms of time and frequency response for different operating condicitons.N/

NASA Center for Intelligent Robotic Systems for Space Exploration

NASA's program for the civilian exploration of space is a challenge to scientists and engineers to help maintain and further develop the United States' position of leadership in a focused sphere of space activity. Such an ambitious plan requires the contribution and further development of many scientific and technological fields. One research area essential for the success of these space exploration programs is Intelligent Robotic Systems. These systems represent a class of autonomous and semi-autonomous machines that can perform human-like functions with or without human interaction. They are fundamental for activities too hazardous for humans or too distant or complex for remote telemanipulation. To meet this challenge, Rensselaer Polytechnic Institute (RPI) has established an Engineering Research Center for Intelligent Robotic Systems for Space Exploration (CIRSSE). The Center was created with a five year $5.5 million grant from NASA submitted by a team of the Robotics and Automation Laboratories. The Robotics and Automation Laboratories of RPI are the result of the merger of the Robotics and Automation Laboratory of the Department of Electrical, Computer, and Systems Engineering (ECSE) and the Research Laboratory for Kinematics and Robotic Mechanisms of the Department of Mechanical Engineering, Aeronautical Engineering, and Mechanics (ME,AE,&M), in 1987. This report is an examination of the activities that are centered at CIRSSE

Trajectory Tracking Control Design for Dual-Arm Robots Using Dynamic Surface Controller

This paper presents a dynamic surface controller (DSC) for dual-arm robots (DAR) tracking desired trajectories. The DSC algorithm is based on backstepping technique and multiple sliding surface control principle, but with an important addition. In the design of DSC, low-pass filters are included which prevent the complexity in computing due to the “explosion of terms”, i.e. the number of terms in the control law rapidly gets out of hand. Therefore, a controller constructed from this algorithm is simulated on a four degrees of freedom (DOF) dual-arm robot with a complex kinetic dynamic model. Moreover, the stability of the control system is proved by using Lyapunov theory. The simulation results show the effectiveness of the controller which provide precise tracking performance of the manipulator

Cooperative Transportation of an Object based on Fractional Order Controllers

Advanced control techniques have been used in simulations to overcome nonlinear phenomena in order to describe the performance of robots with two rotational (RR) degrees-of-freedom (dof). Given the current state-of-the-art, fractional order algorithms lead to better performance when compared to integer order controllers. Also, the development of simulation platforms allows evaluating the best control methodology. In this context, the use of RR robots attached to mobile platforms, denoted in this paper as RR mobile robots, demonstrates a remarkable ability in manipulating and transporting objects. The present work aims to study two cooperative RR mobile robots by analyzing the manipulator’s trajectory and the forces applied to the common load. Are considered two robotic platforms based on an 8-bit microcontroller with inverse kinematics based on the Denavit-Hartenberg formulation and fractional order PID controllers inspired in the Grünwald-Letnikov definition.N/

New Trends in the Control of Robots and Mechatronic Systems

In recent years, research into the control of robotic and mechatronic systems has led to a wide variety of advanced paradigms and techniques, which have been extensively analysed and discussed in the scientific literature [...

Adaptive Controller Algorithm for 2-DOF Humanoid Robot Arm

AbstractA computational model of human motor control for a nonlinear 2 degrees-of-freedom (DOF) robot arm to mimic humanlike behavior is developed and presented in this paper. The model is based on a simple mathematical model of a 2-segment compound pendulum which mimics the human upper arm and forearm. Using the Lagrangian and Euler-Lagrange equations, the 2-DOF dynamic equations were successfully derived and solved using Euler's method. Two types of controllers; a feedback Proportional-Derivative (PD) controller and a feedforward controller, were combined into the model. The algorithm exhibited learning of the necessary torque required in performing the desired Position Control via Specific Trajectory (PCST) rehabilitative task via feedback control and using it as the feedforward torque in subsequent trial motions. After 30 trials, the mean absolute error with respect to the desired motion of the upper arm, showed a decrease from 0.09533 to 0.005859, and the forearm motion from 0.3526 to 0.006138. This decrement trend in mean absolute errorwith increase in number of trials is consistent with the adaptive control strategy of the human arm known as the Feedback Error Learning (FEL) strategy

Study to design and develop remote manipulator system

Modeling of human performance in remote manipulation tasks is reported by automated procedures using computers to analyze and count motions during a manipulation task. Performance is monitored by an on-line computer capable of measuring the joint angles of both master and slave and in some cases the trajectory and velocity of the hand itself. In this way the operator's strategies with different transmission delays, displays, tasks, and manipulators can be analyzed in detail for comparison. Some progress is described in obtaining a set of standard tasks and difficulty measures for evaluating manipulator performance

Admittance-based controller design for physical human-robot interaction in the constrained task space

In this article, an admittance-based controller for physical human-robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform. Note to Practitioners-This work is motivated by the neglect of safety in existing controller design in physical human-robot interaction (pHRI), which exists in industry and services, such as assembly and medical care. It is considerably required in the controller design for rigorously handling constraints. Therefore, in this article, we propose a novel admittance-based human-robot interaction controller. The developed controller has the following functionalities: 1) ensuring reference trajectory remaining in the constrained task space: A differentiable reference trajectory is shaped by the desired admittance model and a soft saturation function; 2) solving uncertainties of robotic dynamics: A learning approach based on radial basis function neural network (RBFNN) is involved in controller design; and 3) ensuring the end-effector of the manipulator remaining in the constrained task space: different from other barrier Lyapunov function (BLF), integral BLF (IBLF) is proposed to constrain system output directly rather than tracking error, which may be more convenient for controller designers. The controller can be potentially applied in many areas. First, it can be used in the rehabilitation robot to avoid injuring the patient by limiting the motion. Second, it can ensure the end-effector of the industrial manipulator in a prescribed task region. In some industrial tasks, dangerous or damageable tools are mounted on the end-effector, and it will hurt humans and bring damage to the robot when the end-effector is out of the prescribed task region. Third, it may bring a new idea to the designed controller for avoiding collisions in pHRI when collisions occur in the prescribed trajectory of end-effector