Abstract:We offer a neural network-based control method to control the vibration of the engineering mechanical arm and the trajectory in order to solve the problem of large errors in tracking the path when the engineering mechanical arm is unstable and under the influence of the outside world. A mechanical arm network is used to perform tasks related to learning the unknown dynamic properties of a engineering mechanical arms keyboard without the need for prior learning. Given the dynamic equations of the engineering me… Show more
“…Ten the boundary value w(t) of the interference term can be determined by the constants c 0 , c 1 and c 2 . Te weight estimates W and V are bounded, and the operation space and subtask tracking errors can be adjusted to be arbitrarily small [15].…”
In order to solve the problems of unstable motion and large trajectory tracking error of the manipulator when it is disturbed by the outside world, the author proposes an adaptive neural network manipulator motion trajectory error method. The author gives the dynamic equation of the manipulator and uses the positive feedback neural network to study the dynamic characteristics of the manipulator. An adaptive neural network control system is designed, and the stability and convergence of the closed-loop system are proved by the Lyapunov function. A schematic diagram of the manipulator model is established, and MATLAB/Simulink software is used to simulate the dynamic parameters of the manipulator. At the same time, it is compared and analyzed with the simulation results of the PID control system. Simulation results show that in robot arm 3, the expected motion trajectory is θ3 = 0.4cos(2πt), the initial condition θ(0) = [000]τ, the control parameter K = diag(40,40),40), the disturbance parameter τ’ = 20cos(πt), robot arm link parameters l1 = 0.62 m, l2 = 0.41 m, l3 = 0.34 m, m1 = 3.5, m2 = 2.5 kg, m3 = 2 kg, g = 9.82 m/s2, under t = 2s, the motion trajectory of the robotic arm is disturbed by the outside world, and the adaptive neural network is used to control the motion trajectory with a small tracking error, input torque ripple is small. Conclusion. The manipulator adopts the adaptive neural network control method, which can improve the control accuracy of the motion trajectory and weaken the jitter phenomenon of the manipulator motion.
“…Ten the boundary value w(t) of the interference term can be determined by the constants c 0 , c 1 and c 2 . Te weight estimates W and V are bounded, and the operation space and subtask tracking errors can be adjusted to be arbitrarily small [15].…”
In order to solve the problems of unstable motion and large trajectory tracking error of the manipulator when it is disturbed by the outside world, the author proposes an adaptive neural network manipulator motion trajectory error method. The author gives the dynamic equation of the manipulator and uses the positive feedback neural network to study the dynamic characteristics of the manipulator. An adaptive neural network control system is designed, and the stability and convergence of the closed-loop system are proved by the Lyapunov function. A schematic diagram of the manipulator model is established, and MATLAB/Simulink software is used to simulate the dynamic parameters of the manipulator. At the same time, it is compared and analyzed with the simulation results of the PID control system. Simulation results show that in robot arm 3, the expected motion trajectory is θ3 = 0.4cos(2πt), the initial condition θ(0) = [000]τ, the control parameter K = diag(40,40),40), the disturbance parameter τ’ = 20cos(πt), robot arm link parameters l1 = 0.62 m, l2 = 0.41 m, l3 = 0.34 m, m1 = 3.5, m2 = 2.5 kg, m3 = 2 kg, g = 9.82 m/s2, under t = 2s, the motion trajectory of the robotic arm is disturbed by the outside world, and the adaptive neural network is used to control the motion trajectory with a small tracking error, input torque ripple is small. Conclusion. The manipulator adopts the adaptive neural network control method, which can improve the control accuracy of the motion trajectory and weaken the jitter phenomenon of the manipulator motion.
“…The working environment of construction machinery is dusty and harsh [37], meaning that the performance of construction machinery intelligent lubrication system will be affected by the working conditions. In order to guide the construction machinery intelligent lubrication system to achieve the expected lubrication effect, the construction machinery intelligent lubrication system is required to be able to automatically adjust the lubrication cycle, lubrication volume, and other parameters; this ensures that the equipment is always in the best lubrication state and reduces equipment wear and tear, so as to prolong the service life of the equipment [38].…”
Section: Construction Of Intelligent Lubrication System Performance E...mentioning
The infrastructure construction process cannot be separated from construction machinery; it will inevitably produce wear and tear in the work. The level of wear and tear is severe and could cause mechanical accidents. There are safety hazards involved with wear and tear; thus, the study of the lubrication systems of construction machinery is crucial. This paper addresses the problems with the intelligent lubrication systems of construction machinery and establishes a performance evaluation index system for the intelligent lubrication systems of construction machinery by analyzing and selecting appropriate evaluation indexes. Based on the built evaluation system, a performance evaluation model was established based on the hierarchical analysis (analytic hierarchy process, AHP)–entropy weight method and a topological object element model. The feasibility of the model was tested using the example of an off-road mining dump truck. This model analyzes the performance strengths and weaknesses of smart lubrication systems and suggests improvement measures and recommendations for weak links. It also provides a reference for analyzing the performance of smart lubrication systems for other mechanical devices.
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