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We formulate a Nash-based feedback control law for an Euler–Lagrange system to yield a solution to noncooperative differential game. The robot manipulators are broadly used in industrial units on the account of their reliable, fast, and precise motions, while consuming a significant lumped amount of energy. Therefore, an optimal control strategy needs to be implemented in addressing efficiency issues, while delivering accuracy obligation. As a case study, we here focus on a 7-DOF robot manipulator through formulating a two-player feedback nonzero-sum differential game. First, coupled Euler–Lagrangian dynamic equations of the manipulator are briefly presented. Then, we formulate the feedback Nash equilibrium solution to achieve perfect trajectory tracking. Finally, the performance of the Nash-based feedback controller is analytically and experimentally examined. Simulation and experimental results reveal that the control law yields almost perfect tracking and achieves closed-loop stability. 

We formulate a Nash-based feedback control law for an Euler-Lagrange system to yield a solution to non-cooperative differential game. The robot manipulators are broadly utilized in industrial units on the account of their reliable, fast, and precise motions, while consuming a significant lumped amount of energy. Therefore, an optimal control strategy needs to be implemented in addressing efficiency issues, while delivering accuracy obligation. As a case study, we here focus on a 7-DOF robot manipulator through formulating a two-player feedback nonzero-sum differential game. First, coupled Euler-Lagrangian dynamic equations of the manipulator are briefly presented. Then, we formulate the feedback Nash equilibrium solution in order to achieve perfect trajectory tracking. Finally, the performance of the Nash-based feedback controller is analytically and experimentally examined. Simulation and experimental results reveal that the control law yields almost perfect tracking and achieves closed-loop stability. 

We design a regulation-triggered adaptive controller for robot manipulators to efficiently estimate unknown parameters and to achieve asymptotic stability in the presence of coupled uncertainties. Robot manipulators are widely used in telemanipulation systems where they are subject to model and environmental uncertainties. Using conventional control algorithms on such systems can cause not only poor control performance, but also expensive computational costs and catastrophic instabilities. Therefore, system uncertainties need to be estimated through designing a computationally efficient adaptive control law. We focus on robot manipulators as an example of a highly nonlinear system. As a case study, a 2-DOF manipulator subject to four parametric uncertainties is investigated. First, the dynamic equations of the manipulator are derived, and the corresponding regressor matrix is constructed for the unknown parameters. For a general nonlinear system, a theorem is presented to guarantee the asymptotic stability of the system and the convergence of parameters’ estimations. Finally, simulation results are discussed for a two-link manipulator, and the performance of the proposed scheme is thoroughly evaluated. 

We formulate a predictor-based controller for a high-DOF manipulator to compensate a time-invariant input delay during a pick-and-place task. Robot manipulators are widely used in tele-manipulation systems on the account of their reliable, fast, and precise motions while they are subject to large delays. Using common control algorithms on such delay systems can cause not only poor control performance, but also catastrophic instability in engineering applications. Therefore, delays need to be compensated in designing robust control laws. As a case study, we focus on a 7-DOF Baxter manipulator subject to three different input delays. First, delay-free dynamic equations of the Baxter manipulator are derived using the Lagrangian method. Then, we formulate a predictor-based controller, in the presence of input delay, in order to track desired trajectories. Finally, the effects of input delays in the absence of a robust predictor are investigated, and then the performance of the predictor-based controller is experimentally evaluated to reveal robustness of the algorithm formulated. Simulation and experimental results demonstrate that the predictor-based controller effectively compensates input delays and achieves closed-loop stability. 

In this paper, we examine the autonomous operation of a high-DOF robot manipulator. We investigate a pick-and-place task where the position and orientation of an object, an obstacle, and a target pad are initially unknown and need to be autonomously determined. In order to complete this task, we employ a combination of computer vision, deep learning, and control techniques. First, we locate the center of each item in two captured images utilizing HSV-based scanning. Second, we utilize stereo vision techniques to determine the 3D position of each item. Third, we implement a Convolutional Neural Network in order to determine the orientation of the object. Finally, we use the calculated 3D positions of each item to establish an obstacle avoidance trajectory lifting the object over the obstacle and onto the target pad. Through the results of our research, we demonstrate that our combination of techniques has minimal error, is capable of running in real-time, and is able to reliably perform the task. Thus, we demonstrate that through the combination of specialized autonomous techniques, generalization to a complex autonomous task is possible.