The current literature lacks uniform calculation methods for following trajectory control for autonomous vehicles,including the calculation of errors,determination of tracking points,and design of feedforward controll...The current literature lacks uniform calculation methods for following trajectory control for autonomous vehicles,including the calculation of errors,determination of tracking points,and design of feedforward controllers.Hence,a complete calculation method is proposed to address this gap.First,a control equation in the form of an error is obtained according to the dynamic equation of the vehicle coordinate system and the trajectory following model.Secondly,the deviation of the vehicle state is obtained according to the current vehicle s state and the following control model.Finally,a linear quadratic regulator(LQR)controller with feedforward control is designed according to the characteristics of the dynamic equation.With the proposed LQR,the simulation of computational time,anti-interference,and reliability analysis of the trajectory following control is performed by programming using MATLAB.The simulation outcomes are then compared with the experimental results from the literature.The comparison indicates that the proposed complete calculation method is effective,reliable,and capable of achieving real-time and anti-interference following control performance.The simulation results with or without feedforward control show that the steady-state error is eliminated and that good control performance is obtained by introducing feedforward control.展开更多
The goal of this paper is to enhance a practical nominal characteristic trajectory following(NCTF) controller that is specifically designed for two-mass point-to-point positioning systems. A nominal characteristics tr...The goal of this paper is to enhance a practical nominal characteristic trajectory following(NCTF) controller that is specifically designed for two-mass point-to-point positioning systems. A nominal characteristics trajectory contained in the NCTF controller acts as movement/motion reference and a compensator is utilized to force the object to detect and follow the reference/desired trajectory. The object must follow and track closely and should be as fast as possible. The NCTF controller is designed with two different intelligent based compensator approaches which are fuzzy logic and extended minimal resource allocation network. The proposed controller which is NCTF are compared with the conventional proportional integral compensator. Then the results of simulation are discussed for the positioning performances. The inertia variations due to the effect of the design parameters are also assessed to see the robustness of controllers. The results show that the NCTF control method designed from an intelligent based compensator has a better positioning performance in terms of percentage of overshoot, settling time, and steady state error than the classical based compensator.展开更多
Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward contr...Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.展开更多
A fuzzy control technique is applied to a functional neuromuscular stimulation (FNS) physical multiarticular muscle control system. The FNS multiarticular muscle control system based on the fuzzy controller was deve...A fuzzy control technique is applied to a functional neuromuscular stimulation (FNS) physical multiarticular muscle control system. The FNS multiarticular muscle control system based on the fuzzy controller was developed with the fuzzy control rule base. Simulation experiments were then conducted for the joint angle trajectories of both the elbow flexion and the wrist flexion using the proposed fuzzy control algorithm and a conventional PID control algorithm with the FNS physical multiarticular muscle control system. The simulation results demonstrated that the proposed fuzzy control method is more suitable for the physiological characteristics than conventional PID control. In particular, both the trajectory following and the stability of the FNS multiarticular muscle control system were greatly improved. Furthermore, the stimulating pulse trains generated by the fuzzy controller were stable and smooth.展开更多
基金The National Key Research and Development Program of China(No.2019YFB2006404)Guangxi Science and Technology Major Project(No.GUIKE AA18242036,No.GUIKE AA18242037).
文摘The current literature lacks uniform calculation methods for following trajectory control for autonomous vehicles,including the calculation of errors,determination of tracking points,and design of feedforward controllers.Hence,a complete calculation method is proposed to address this gap.First,a control equation in the form of an error is obtained according to the dynamic equation of the vehicle coordinate system and the trajectory following model.Secondly,the deviation of the vehicle state is obtained according to the current vehicle s state and the following control model.Finally,a linear quadratic regulator(LQR)controller with feedforward control is designed according to the characteristics of the dynamic equation.With the proposed LQR,the simulation of computational time,anti-interference,and reliability analysis of the trajectory following control is performed by programming using MATLAB.The simulation outcomes are then compared with the experimental results from the literature.The comparison indicates that the proposed complete calculation method is effective,reliable,and capable of achieving real-time and anti-interference following control performance.The simulation results with or without feedforward control show that the steady-state error is eliminated and that good control performance is obtained by introducing feedforward control.
文摘The goal of this paper is to enhance a practical nominal characteristic trajectory following(NCTF) controller that is specifically designed for two-mass point-to-point positioning systems. A nominal characteristics trajectory contained in the NCTF controller acts as movement/motion reference and a compensator is utilized to force the object to detect and follow the reference/desired trajectory. The object must follow and track closely and should be as fast as possible. The NCTF controller is designed with two different intelligent based compensator approaches which are fuzzy logic and extended minimal resource allocation network. The proposed controller which is NCTF are compared with the conventional proportional integral compensator. Then the results of simulation are discussed for the positioning performances. The inertia variations due to the effect of the design parameters are also assessed to see the robustness of controllers. The results show that the NCTF control method designed from an intelligent based compensator has a better positioning performance in terms of percentage of overshoot, settling time, and steady state error than the classical based compensator.
基金supported by Grant-in-Aid for Scientific Research(C) (No. 20560248) of Japan
文摘Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.
文摘A fuzzy control technique is applied to a functional neuromuscular stimulation (FNS) physical multiarticular muscle control system. The FNS multiarticular muscle control system based on the fuzzy controller was developed with the fuzzy control rule base. Simulation experiments were then conducted for the joint angle trajectories of both the elbow flexion and the wrist flexion using the proposed fuzzy control algorithm and a conventional PID control algorithm with the FNS physical multiarticular muscle control system. The simulation results demonstrated that the proposed fuzzy control method is more suitable for the physiological characteristics than conventional PID control. In particular, both the trajectory following and the stability of the FNS multiarticular muscle control system were greatly improved. Furthermore, the stimulating pulse trains generated by the fuzzy controller were stable and smooth.
