Longitudinal Velocity Regulation of UGVs: A Composite Control Approach for Acceleration and Deceleration

Wang, Haoyu; Zuo, Zhiqiang; Wang, Yijing; Yang, Hongjiu; Hu, Chuan

doi:10.1109/tits.2023.3274106

Cited by 4 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [39], the authors proposed a composite longitudinal controller for speed regulation of unmanned vehicles. A composite longitudinal control strategy integrating a steadystate controller and an active disturbance rejection state feedback controller is designed for speed regulation control during acceleration and deceleration.…”

Section: A Related Workmentioning

confidence: 99%

Adaptive Cruise Control of the Autonomous Vehicle Based on Sliding Mode Controller Using Arduino and Ultrasonic Sensor

Alika,

Mellouli,

Tissir

2024

Journal of Robotics and Control

View full text Add to dashboard Cite

This article will focus on adaptive cruise control in autonomous automobiles. The adaptive cruise control inputs are the safety distance which determines thanks to conditions set depending on the distance value, the measured distance, the longitudinal speed of the autonomous automobile itself, the output is the desired acceleration. The objective is to follow the vehicles in front with safety, according to the distance measured by the ultrasonic sensor, and maintain a distance between the vehicles in front greater than the safety distance which we have determined. For this, we used super twisting sliding mode controller (STSMC) and non-singular terminal sliding mode controller (NTSMC) based on neural network applied to the adaptive cruise control system. The neural network is able to approximate the exponential reaching law term parameter of the NTSMC controller to compensate for uncertainties and perturbations. An autonomous automobile adaptive cruise control system prototype was produced and tested using an ultrasonic sensor to measure the distance between the two automobiles, and an Arduino board as a microcontroller to implement our program, and four DCs motors as actuators to move or stop our host vehicle. This system is processed by code and Simulink Matlab, the efficiency and robustness of these controllers are excellent, as demonstrated by the low longitudinal velocity error value. The safety of autonomous vehicles can be enhanced by improving adaptive cruise control using STSMC and NTSMC based on neural network controllers, which are chosen for their efficiency and robustness.

show abstract

Section: A Related Workmentioning

confidence: 99%

Adaptive Cruise Control of the Autonomous Vehicle Based on Sliding Mode Controller Using Arduino and Ultrasonic Sensor

Alika,

Mellouli,

Tissir

2024

Journal of Robotics and Control

View full text Add to dashboard Cite

show abstract

“…For solving problems of pressure fluctuation and low accuracy of deceleration control when Electric Stable System (ESC) is used as braking actuator of Automated Cruise Control (ACC) system, a hierarchical adaptive cruise control structure is proposed in [16], the lower layer of which is a deceleration controller based on nonlinear model predictive control (MPC); the simulation results show that compared with the traditional PID deceleration controller, the deceleration control accuracy is effectively improved. In [17], a composite longitudinal control strategy is designed, which combines a steady-state controller with an auto-disturbance rejection state feedback controller for speed control during acceleration and deceleration.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Neural Network PID-Based Constant Deceleration Control for Automated Mine Electric Vehicles Using EMB System

Li,

Ma,

Jiang

2024

Sensors

View full text Add to dashboard Cite

It is urgent for automated electric transportation vehicles in coal mines to have the ability of self-adaptive tracking target constant deceleration to ensure stable and safe braking effects in long underground roadways. However, the current braking control system of underground electric trackless rubber-tired vehicles (UETRVs) still adopts multi-level constant braking torque control, which cannot achieve target deceleration closed-loop control. To overcome the disadvantages of lower safety and comfort, and the non-precise stopping distance, this article describes the architecture and working principle of constant deceleration braking systems with an electro-mechanical braking actuator. Then, a deceleration closed-loop control algorithm based on fuzzy neural network PID is proposed and simulated in Matlab/Simulink. Finally, an actual brake control unit (BCU) is built and tested in a real industrial field setting. The test illustrates the feasibility of this constant deceleration control algorithm, which can achieve constant decelerations within a very short time and maintain a constant value of −2.5 m/s2 within a deviation of ±0.1 m/s2, compared with the deviation of 0.11 m/s2 of fuzzy PID and the deviation of 0.13 m/s2 of classic PID. This BCU can provide electric and automated mine vehicles with active and smooth deceleration performance, which improves the level of electrification and automation for mine transport machinery.

show abstract

Data-Driven Active Disturbance Rejection Control of Plant-Protection Unmanned Ground Vehicle Prototype: A Fuzzy Indirect Iterative Learning Approach

Chen,

Zhao,

Chen

et al. 2024

IEEE/CAA J. Autom. Sinica

View full text Add to dashboard Cite

This letter proposes a fuzzy indirect iterative learning (FIIL) active disturbance rejection control (ADRC) scheme to address the impact of uncertain factors of plant-protection unmanned ground vehicle (UGV), in which ADRC is a data-driven model-free control algorithm that only relies on the input and output data of the system. Based on the established nonlinear time-varying dynamic model including dynamic load (medicine box), the FIIL technology is adopted to turn the bandwidth and control channel gain online, in which the fuzzy logic system is used to update the gain parameters of iterative learning in real time. Simulation and experiment show the FIIL-ADRC scheme has better control performance.With the advancement of agricultural intelligence, the artificial spraying method that threatens health is being replaced by the plant protection UGV. The speed and steering angle control of plant protection UGV is particularly important in the process of field operation. The accuracy of steering angle determines whether the UGV needs to be manipulated artificially at the boundary corner of the field [1]. In addition, with the spraying of the liquid, the total mass of the vehicle will be reduced, and the sloshing of the liquid in the medicine box, air resistance, nonlinear friction and the unmodeled part of the system will cause multi-source and unknown interference to UGV. Based on the conditions above, the model of the plant protection UGV is hard to establish. When the model cannot be precise enough, the control effect cannot be further improved, data-driven control schemes can solve this problem [2], [3]. However, the general data-driven control approaches are mostly based on error elimination control methods. When there are unknown external disturbances and internal uncertainties, they cannot predict disturbances well to provide control compensation, and the control effect cannot meet the ideal control requirements. Compared with the general datadriven control approaches, the advantage of ADRC is great when dealing with unknown disturbance, which is more suitable than other model-free data-driven control methods. Therefore, ADRC scheme, a model-free data-driven control method, is used in this letter as basis to control plant protection UGVs. The core idea of ADRC scheme is to treat uncertain factors as total disturbance, and estimate the total disturbance online by the extended state observer (ESO), and compensate the control input [4]. At present, the research on ADRC of UGVs has made some achievements [5].

show abstract

Longitudinal Velocity Regulation of UGVs: A Composite Control Approach for Acceleration and Deceleration

Cited by 4 publications

References 29 publications

Adaptive Cruise Control of the Autonomous Vehicle Based on Sliding Mode Controller Using Arduino and Ultrasonic Sensor

Adaptive Cruise Control of the Autonomous Vehicle Based on Sliding Mode Controller Using Arduino and Ultrasonic Sensor

Fuzzy Neural Network PID-Based Constant Deceleration Control for Automated Mine Electric Vehicles Using EMB System

Data-Driven Active Disturbance Rejection Control of Plant-Protection Unmanned Ground Vehicle Prototype: A Fuzzy Indirect Iterative Learning Approach

Contact Info

Product

Resources

About