Trajectory Tracking Control Of Robotic Arm Based On Deep Reinforcement Learning And Zeroing Dynamics

As the manufacturing industry continues to evolve,researching high-precision and high-stability intelligent robotic arms has become a hot topic in the field of mechanical control.In practical applications,the impact of external factors such as the environment and load on robotic arm motion control presents multiple complex nonlinear challenges,including factors such as friction,gravity,inertia,and elasticity,which often result in unstable and inaccurate behavior.Therefore,addressing the impact of these nonlinear factors on robotic arm control has become an urgent issue.To tackle these challenges,several new control methods have been proposed,among which one of the most promising is the use of reinforcement learning and neural dynamicsbased control methods.Reinforcement learning is a machine learning method that enables autonomous learning and improvement of strategies,while neural dynamics is a control framework based on the principles of the biological neural system.These methods can enable robotic arms to adapt to changes in the environment and load through adaptive learning,and can automatically adjust control parameters during control,thereby improving the precision and stability of robotic arms.In this paper,we propose a deep reinforcement learning-based actor-critic algorithm to solve the calibration task by finding the optimal behavioral policy that optimizes the distance between the robotic arm end effector and the target point.To address the slow convergence,over-reliance on initial values,and local convergence issues of traditional calibration algorithms,we use neural dynamics adaptive reward and action functions to approximate the corresponding reward signals.Additionally,we further improve this function to have a progressive mechanism under the guidance of an attention mechanism to handle calibration tasks in complex environments.Although robotic arms based on deep reinforcement learning have a long convergence time and are not widely applicable in practical applications,in this paper,we solve the robotic arm trajectory tracking problem by using a unified expression form based on zeroing dynamics,starting from the robotic arm’s own dynamic system,and using Lie derivatives.Furthermore,we provide three other theorems for global exponential convergence of the zeroing dynamics controller,as well as the steady-state error bound of the zeroing gradient dynamics controller,and the radius bound of the steady-state error converges exponentially in an exponential sense.Finally,in addition to the applications discussed in this paper,the zeroing dynamics method has the potential to be applied to other control problems in robotics and beyond.Simulation experiments were conducted on a nonlinear ship dynamic system model with bilinear outputs,and its feasibility was analyzed.These experiments demonstrated the effectiveness of the proposed method in controlling the ship’s motion and improving its performance.As such,the zeroing dynamics method presents a promising approach for addressing challenging control problems in various fields.