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In robotic applications, the correct execution of a task can be a challenge for robotics experts. Therefore, it is necessary to implement a robust control structure for trajectory tracking. This paper proposes a robust control design to track the trajectory of an industrial robot via a fractional order PID controller with the Computed Torque Control (CTC) technique based on the Grey Wolf Optimizer (GWO). To examine the proposed robust control, the Fanuc 710ic/70 robot manipulator model is used as a case study. To begin with, the dynamic formulation of the robot is described. Then, with respect to the control design, the CTC controller that helps overcome the nonlinearity problem of the system is designed to improve the tracking performance. It is proposed to combine the fractional order PID with the CTC technique. This hybridization increases the performance of the control strategy and overcomes external disturbances, sensor noise suppression and especially input control constraints. Typically, FOPID controller parameters are set without considering the constraints of the actuator control inputs. Therefore, it affects its performance over time. To solve this problem, the controller parameters are updated online and optimized according to the input constraints using the GWO technique. The effectiveness of the proposed control strategy is demonstrated by the simulation results in terms of stability, trajectory tracking and compliance with limited inputs. [ABSTRACT FROM AUTHOR]

During physical human–robot interaction, unwanted collisions occur, which also threaten human safety. That is why different collision detection algorithms are proposed for safe collaboration between human and robots. The current detection methods are model-based methods without using additional sensors. However, the estimation of the collision force using a model-based method still has the problem of an unavoidable trade-off between the collision sensitivity and the reduction of the peaking value, and, in addition, the immunity to the model uncertainties. In this paper, an improved momentum observer using fuzzy system is proposed for robot collision detection. First, to avoid using the robot’s inverse inertia matrix, the generalized moment state equations based on the robot’s dynamics are deduced. Second, the extended linear momentum observer is built based on robot generalized momentum equations. Third, a fuzzy system is designed to select an appropriate bandwidth for the observer. In the design of this fuzzy system, the estimation error is considered as a fuzzy input, and the observer’s bandwidth is considered as its output. This fuzzy generalized momentum observer (FGMO) is compared with the existing generalized moment observers (GMOs) and has been proven its ability to overcome the mentioned above issues due to its ability to intelligently adjust the online filter parameters during the observation process. Finally, simulations are conducted with a 6-degree-of-freedom (6-DOF) industrial robot manipulator, and the obtained results illustrate the effectiveness of proposed approach in term of sensitivity and peaking value. [ABSTRACT FROM AUTHOR]