Adaptive speed control method for electromagnetic direct drive vehicle robot driver based on fuzzy logic

Chen, Gang; Zhang, Weigong; Xu, Li; Yu, Bing

doi:10.1177/0020294019866841

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…A state feedback PID control technique with strong robustness was proposed by Netto [48] and is based on data from optimal path discovery. Fuzzy logic was introduced by Chen et al on the foundation of PID control to realize parameter adaptive tuning to increase control precision [49]. Chen et al used the sliding mode control method to increase the resilience of the system against parameter uncertainty and external disturbances [50].…”

Section: Research On Longitudinal Control For Driverless Vehiclesmentioning

confidence: 99%

Research Status and Development Trend of Unmanned Driving Technology in Coal Mine Transportation

Wang,

Bao,

Yuan

et al. 2022

Energies

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Unmanned driving technology has always been one focus of mine smart transportation, which is a crucial component of smart mines. However, the descriptions of both the intelligent transportation system and its industrial application are not comprehensive. In order to have an all-encompassing and comprehensive understanding of both the intelligent coal mine transportation system and its industrial application with the intelligent system, this paper summarizes and analyzes the current research status of unmanned driving technology in mines and the industrial application of current mining transportation vehicles. It begins by outlining the current research state of unmanned driving technology in mines and then assesses the advancement of unmanned driving technology. The unmanned transportation system in mines is also introduced, together with its components for perception, location, path planning, vehicle control, and multi-vehicle scheduling. In addition, each component is described in combination with the current artificial intelligent unmanned technology individually. Then, some typical categories of intelligent industrial vehicles are introduced for learning about the conditions of their actual application. There are almost four hundred coal mines that have researched unmanned driving technology, and some companies have applied the unmanned technology to realize transportation with an efficiency enhancement of 70~80%. Finally, currently existing challenges and future research are analyzed and proposed. This review may provide more comprehensive knowledge of the intelligent coal mine, accelerating the development of intelligent technology and helping to build a new management and control model.

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Section: Research On Longitudinal Control For Driverless Vehiclesmentioning

confidence: 99%

Research Status and Development Trend of Unmanned Driving Technology in Coal Mine Transportation

Wang,

Bao,

Yuan

et al. 2022

Energies

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“…However, these methods reached their limits as commercial plants have been complex. Thus, studies proposing a closed-loop control-based PID gain generator using fuzzy logic dominated [5], [18].…”

Section: A Robot Driver Systemmentioning

confidence: 99%

Deep Reinforcement Learning Based Dynamic Proportional-Integral (PI) Gain Auto-Tuning Method for a Robot Driver System

et al. 2022

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To meet the growing trend of stringent fuel economy regulations, automakers around the world are designing modules such as engines, motors, transmissions and batteries to be as efficient as possible. In order to verify the effect of these designs on the overall fuel efficiency of the vehicle, the vehicle equipped with each module is placed on the chassis dynamometer, driven to follow the target vehicle speed, and actual fuel efficiency is measured. These tests are traditionally performed by human operators, but are now being replaced by robots (physical or software) to ensure the accuracy and reliability of test results. Although the conventionally proposed proportional integral (PI)-based controller has a simple structure and is easy to implement, it requires the process of finding the optimal gain whenever the test conditions such as vehicle or drive cycle change, which is difficult and time consuming. In this study, we propose a proportional integral controller gain adjustment algorithm using deep reinforcement learning. The reinforcement learning agent learns to dynamically modify the PI gain value of the acceleration/deceleration pedal to better follow the target vehicle in a simulation environment. The perturbation is used in each training episode to reduce the difference between the simulation and real testing environment. Upon completion of the training process, the trained agent performs an adjustment process that generates a reference gain table. We then use this reference gain table to perform a real test. The performance of the proposed system was evaluated using Hyundai Tucson HEV (NX4) on an AVL chassis dynamometer. We also compared the performance of our proposed algorithm to traditional fuzzy logic-based PI controllers. The obtained experimental results show that the proposed control system achieved a performance improvement of aounrd 46.8% compared to the conventional PI control system in terms of root mean square error.

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“…Fuzzy control is a computer digital control technology based on fuzzy set theory, linguistic variables, and fuzzy logic inference. 30 Fuzzy-PID control is based on the PID method, takes error e and error change rate e c as input, uses fuzzy rules for reasoning and parameter adjustment to meet e and e c for PID parameter self-tuning at different times.…”

Section: Behavior Optionmentioning

confidence: 99%

Obstacle avoidance planning of autonomous vehicles using deep reinforcement learning

Feng

Hu³

et al. 2022

Advances in Mechanical Engineering

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Obstacle avoidance path planning in a dynamic circumstance is one of the fundamental problems of autonomous vehicles, counting optional maneuvers: emergency braking and active steering. This paper proposes emergency obstacle avoidance planning based on deep reinforcement learning (DRL), considering safety and comfort. Firstly, the vehicle emergency braking and lane change processes are analyzed in detail. A graded hazard index is defined to indicate the degree of the potential risk of the current vehicle movement. The longitudinal distance and lateral waypoint models are established, including the comfort deceleration and stability coefficient considerations. Simultaneously, a fuzzy PID controller is installed to track to satisfy the stability and feasibility of the path. Then, this paper proposes a DRL process to determine the obstacle avoidance plan. Mainly, multi-reward functions are designed for different collisions, corresponding penalties for longitudinal rear-end collisions, and lane-changing side collisions based on the safety distance, comfort reward, and safety reward. Apply a special DRL method-DQN to release the planning program. The difference is that the long and short-term memory (LSTM) layer is utilized to solve incomplete observations and improve the efficiency and stability of the algorithm in a dynamic environment. Once the policy is practiced, the vehicle can automatically perform the best obstacle avoidance maneuver in an emergency, improving driving safety. Finally, this paper builds a simulated environment in CARLA and is trained to evaluate the effectiveness of the proposed algorithm. The collision rate, safety distance difference, and total reward index indicate that the collision avoidance path is generated safely, and the lateral acceleration and longitudinal velocity satisfy the comfort requirements. Besides, the method proposed in this paper is compared with traditional DRL, which proves the beneficial performance in safety and efficiency.

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Adaptive speed control method for electromagnetic direct drive vehicle robot driver based on fuzzy logic

Cited by 11 publications

References 11 publications

Research Status and Development Trend of Unmanned Driving Technology in Coal Mine Transportation

Research Status and Development Trend of Unmanned Driving Technology in Coal Mine Transportation

Deep Reinforcement Learning Based Dynamic Proportional-Integral (PI) Gain Auto-Tuning Method for a Robot Driver System

Obstacle avoidance planning of autonomous vehicles using deep reinforcement learning

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