This paper aims to highlight the critical role of robot manipulators in industrial applications and elucidate the challenges associated with achieving high-precision control. In particular, the detrimental effects of nonlinear friction on manipulators are discussed. To overcome this challenge, a novel friction compensation controller (FCC) that combines time-delay estimation (TDE) and an adaptive fuzzy logic system (AFLS) is proposed in this paper. The friction compensation controller is designed to take advantage of the time-delay estimation algorithm’s strengths in eliminating and estimating unknown dynamic functions of the system using information from the previous sampling period. Simultaneously, the adaptive fuzzy logic system compensates for the hard nonlinearities in the system and suppresses the errors generated by time-delay estimation, thus improving the accuracy of the robotic arm’s tracking. The numerical experimental results demonstrate that the proposed friction compensation controller can significantly enhance the tracking accuracy of the robotic arm, with the addition of the adaptive fuzzy logic system improving time delay estimation’s performance by an average of 90.59%. Moreover, the proposed controller is more straightforward to implement than existing methods and performs exceptionally well in practical applications.

This study proposes a real-time control force (RCF) algorithm for precise force control of hydraulic manipulator arms (HMAs) without the need for force or pressure feedback. The algorithm utilizes the nonlinear relationship between the hydraulic manipulator arm's servo valve pressure, voltage, and dynamics to calculate joint torque in real-time, thereby accurately controlling the force applied by the manipulator's arm. We theoretically prove the convergence of the algorithm on hydraulic manipulator arms with different degrees of freedom. The advantage of RCF controller is that it does not require force or pressure sensors, which avoids the deployment of complex sensor systems, and it can adapt to hydraulic manipulator arms with different degrees of freedom without the need to redesign the force control algorithm, greatly reducing the amount of manual intervention required for control. Experimental results demonstrate that the proposed algorithm enables the hydraulic manipulator to rapidly converge to the desired force within 0.2 seconds after experiencing rigid collisions. Additionally, the algorithm achieves a positional control precision of 1.4 mm and maintains force errors within 2N, effectively showcasing its outstanding force control performance. Therefore, we believe that this algorithm holds tremendous potential and practical value, as it can significantly reduce the cost and amount of human intervention required for force control of hydraulic manipulators.

Nuclear power plant emergency robots are robots used to respond to significant public safety incidents, such as uncontrolled radioactive sources and nuclear catastrophe leaks. However, there are no standardized evaluation criteria for the optimal design of the robots. We offer a quantitative analytic algorithm for optimizing nuclear power plant emergency robots to address this issue. The method optimizes the structural parameters of the robot in accordance with the workspace by analyzing, comparing, and evaluating the workspace. The approach comprises constructing a kinematic model of the mechanical arm and proposing an optimization algorithm based on the alpha shape to accurately describe the manipulator workspace; employing the proposed convex hull algorithm to quantitatively analyze and evaluate the workspace generated by different solutions in terms of area, volume, task demand, Structural Length Index and Global Conditioning Index; and determining the robotic arm joint parameters by selecting the optimum workspace design solution. Using the suggested algorithm, we optimize the design of the master and slave robotic arms of the nuclear power plant emergency robots. Theoretical calculations and simulation results demonstrate that the method is an effective and practical evaluation technique that not only accurately describes the workspace but also optimizes the design of the nuclear power plant emergency robots.

A new extended state observer combined with a Backstepping tracking control method were proposed in this paper for the omnidirectional rehabilitative training walker. The aim of this study is to obtain a stable tracking controller, and it considered the interaction forces of the user and walker to ensure that the omnidirectional walker can track accurately specified trajectory. The extended state observer was designed to estimate the unknown interaction forces of user and the walker using output position and speed states. Further, a Backstepping controller based on the observed value was constructed to resist the adverse effects of interaction forces. The asymptotic stability of the trajectory tracking error, the velocity tracking error and the state observation error were guaranteed. Through programming, simulation results show the effectiveness of the proposed design method.