Considering the temperature difference of displacement cooking characterized by severe non-linearity, large time delay, and real-time control, a cascade PID adaptive control strategy composed of a single neuron is pro...Considering the temperature difference of displacement cooking characterized by severe non-linearity, large time delay, and real-time control, a cascade PID adaptive control strategy composed of a single neuron is proposed to ensure cooking temperature uniformity. The control strategy introduces expert experiences to adjust the single neuron gain K, while a single neuron PID self-learning and adaptive ability, as well as cascade advantage can be combined to realize the real-time and fast temperature difference control. In the Simulink, the s-function of this control strategy is used to carry out a dynamic simulation experiment with temperature difference characteristics and verify the robustness and response to model mismatch. Compared to conventional temperature difference-flow PID cascade control and single neuron PID cascade control, this control strategy has better robustness and stronger adaptability. The results of real-time control on the THJSK-1 experiment platform indicate this control strategy is feasible.展开更多
Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these approaches.Autonomous cars are one such appli...Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these approaches.Autonomous cars are one such application.This is expected to have a significant and revolutionary influence on society.Integration with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles.The autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement.Cars are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic implications.However,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate action.The majority of self-driving car technologies are based on computer systems that automate vehicle control parts.From forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and cameras.Recent researches on computer vision and deep learning are used to control autonomous driving systems.For self-driving automobiles,lane-keeping is crucial.This study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the lane.We propose an advanced control for a selfdriving robot by using two controllers simultaneously.Convolutional neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering control.This study uses a Raspberry PI based camera to control the robot car.展开更多
Driving simulators play a crucial role in the testing of the autonomous driving.This paper focuses on the design of the autonomous driving vehicle testing platform and control algorithm issues based on a hardware-in-t...Driving simulators play a crucial role in the testing of the autonomous driving.This paper focuses on the design of the autonomous driving vehicle testing platform and control algorithm issues based on a hardware-in-the-loop driving simulator.The system framework of the platform can be summarized as follows:the visual simulation system,which is responsible for the simulation calculation of the driving vehicle and the driving scenes;the platform control system,which is responsible for the information dispatch of the platform and the autonomous driving control of the vehicle;and the interactive control system,which is responsible for the simulation signal acquisition and steering control tasks.At the same time,this paper presents a specific solution based on practice,using UC-win/Road to develop the visual simulation system,using Qt Creator to develop the platform control system,and using development boards to build the interactive control system.To solve the autonomous driving control problem of a simulated vehicle,this paper constructs a classic two-degree-of-freedom vehicle dynamics model for lateral control and studies the LQR control algorithm based on this model,providing the implementation procedure of the algorithm.For longitudinal control,considering that the vehicle needs dual control of speed and position,this paper adopts the cascaded PID control method,with position control and speed control as the outer and inner loops of the PID controller,respectively.Finally,the testing of the lateral and longitudinal control algorithms is conducted.In the lateral control test,a continuous curved road with different curvatures is designed to analyze the lane deviation and heading angle error of the vehicle at different curvature bends.In the longitudinal control test,the functions of vehicle cruise control and distance-based car-following are mainly examined.The lateral error of the system does not exceed 0.3 m,and the longitudinal error exceeds 1 km/h(0.278 m/s).The experimental results show that the autonomous driving control program designed in this paper is effective and verifies the feasibility of the proposed driving simulation platform design.展开更多
文摘Considering the temperature difference of displacement cooking characterized by severe non-linearity, large time delay, and real-time control, a cascade PID adaptive control strategy composed of a single neuron is proposed to ensure cooking temperature uniformity. The control strategy introduces expert experiences to adjust the single neuron gain K, while a single neuron PID self-learning and adaptive ability, as well as cascade advantage can be combined to realize the real-time and fast temperature difference control. In the Simulink, the s-function of this control strategy is used to carry out a dynamic simulation experiment with temperature difference characteristics and verify the robustness and response to model mismatch. Compared to conventional temperature difference-flow PID cascade control and single neuron PID cascade control, this control strategy has better robustness and stronger adaptability. The results of real-time control on the THJSK-1 experiment platform indicate this control strategy is feasible.
文摘Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these approaches.Autonomous cars are one such application.This is expected to have a significant and revolutionary influence on society.Integration with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles.The autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement.Cars are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic implications.However,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate action.The majority of self-driving car technologies are based on computer systems that automate vehicle control parts.From forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and cameras.Recent researches on computer vision and deep learning are used to control autonomous driving systems.For self-driving automobiles,lane-keeping is crucial.This study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the lane.We propose an advanced control for a selfdriving robot by using two controllers simultaneously.Convolutional neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering control.This study uses a Raspberry PI based camera to control the robot car.
基金supported by the National Natural Science Foundation of China(52472366)the National Key Research and Development Program of China(2023YFB4302600)Hubei Province Key Research and Development Program(2024BAB051).
文摘Driving simulators play a crucial role in the testing of the autonomous driving.This paper focuses on the design of the autonomous driving vehicle testing platform and control algorithm issues based on a hardware-in-the-loop driving simulator.The system framework of the platform can be summarized as follows:the visual simulation system,which is responsible for the simulation calculation of the driving vehicle and the driving scenes;the platform control system,which is responsible for the information dispatch of the platform and the autonomous driving control of the vehicle;and the interactive control system,which is responsible for the simulation signal acquisition and steering control tasks.At the same time,this paper presents a specific solution based on practice,using UC-win/Road to develop the visual simulation system,using Qt Creator to develop the platform control system,and using development boards to build the interactive control system.To solve the autonomous driving control problem of a simulated vehicle,this paper constructs a classic two-degree-of-freedom vehicle dynamics model for lateral control and studies the LQR control algorithm based on this model,providing the implementation procedure of the algorithm.For longitudinal control,considering that the vehicle needs dual control of speed and position,this paper adopts the cascaded PID control method,with position control and speed control as the outer and inner loops of the PID controller,respectively.Finally,the testing of the lateral and longitudinal control algorithms is conducted.In the lateral control test,a continuous curved road with different curvatures is designed to analyze the lane deviation and heading angle error of the vehicle at different curvature bends.In the longitudinal control test,the functions of vehicle cruise control and distance-based car-following are mainly examined.The lateral error of the system does not exceed 0.3 m,and the longitudinal error exceeds 1 km/h(0.278 m/s).The experimental results show that the autonomous driving control program designed in this paper is effective and verifies the feasibility of the proposed driving simulation platform design.
