A Novel Adaptive Steering Torque Control Approach for Human-Machine-Cooperation Autonomous Vehicles

2021 ◽  
pp. 1-1
Author(s):  
Jian Wu ◽  
Junda Zhang ◽  
Yang Tian ◽  
Liang Li
Keyword(s):  
Autonomous Vehicles ◽  
Torque Control ◽  
Control Approach ◽  
Steering Torque

scholarly journals GA-BPNN Based Hybrid Steering Control Approach for Unmanned Driving Electric Vehicle with In-Wheel Motors

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Yong Li ◽  
Xing Xu ◽  
Wujie Wang
Keyword(s):  
Electric Vehicle ◽  
Hierarchical Modeling ◽  
Back Propagation ◽  
Response Speed ◽  
Weight Coefficient ◽  
Steering System ◽  
Torque Control ◽  
Steering Control ◽  
Control Approach ◽  
Steering Torque

The steering system is a key component of the unmanned driving electric vehicle with in-wheel motors (IWM-EV), which is closely related to the operating safety of the vehicle. To characterize the complex nonlinear structure of the steering system of unmanned driving IWM-EV, a hierarchical modeling and hybrid steering control approach are presented. Firstly the 2-DOF model is introduced for the entire vehicle system, and then the models of the steering system and the in-wheel drive system are analyzed sequentially. The steering torque control system based on electronic differential (ED) and differential assist steering (DAS) is studied. The back propagation neural network (BPNN) is used to optimize the network structure, parameters, and the weight coefficient of the hybrid steering system. The genetic algorithm (GA) is employed to optimize the initial weight of BPNN and search within a large range. The GA-BPNN model is established with the yaw moment and differential torque as the input of BPNN. Simulation and experimental results show that the proposed GA-BPNN-based hybrid steering control approach not only accelerates the convergence speed of steering torque weight adjustment but also improves the response speed and flexibility of the steering system. Through optimizing and distributing the steering torque dynamically, the proposed GA-BPNN-based control approach has inherited the advantages of both vehicle stability under ED and the steering assistance under DAS, which further guarantees the safety and stability of unmanned driving IWM-EV.

scholarly journals Robust Sensorless Model-Predictive Torque Flux Control for High-Performance Induction Motor Drives

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 403
Author(s):  
Ahmed G. Mahmoud A. Aziz ◽  
Hegazy Rez ◽  
Ahmed A. Zaki Diab
Keyword(s):  
Induction Motor ◽  
High Performance ◽  
Core Loss ◽  
Parameters Estimation ◽  
Torque Control ◽  
Flux Control ◽  
Control Approach ◽  
Light Load ◽  
Wide Range ◽  
Lyapunov’S Theorem

This paper introduces a novel sensorless model-predictive torque-flux control (MPTFC) for two-level inverter-fed induction motor (IM) drives to overcome the high torque ripples issue, which is evidently presented in model-predictive torque control (MPTC). The suggested control approach will be based on a novel modification for the adaptive full-order-observer (AFOO). Moreover, the motor is modeled considering core losses and a compensation term of core loss applied to the suggested observer. In order to mitigate the machine losses, particularly at low speed and light load operations, the loss minimization criterion (LMC) is suggested. A comprehensive comparative analysis between the performance of IM drive under conventional MPTC, and those of the proposed MPTFC approaches (without and with consideration of the LMC) has been carried out to confirm the efficiency of the proposed MPTFC drive. Based on MATLAB® and Simulink® from MathWorks® (2018a, Natick, MA 01760-2098 USA) simulation results, the suggested sensorless system can operate at very low speeds and has the better dynamic and steady-state performance. Moreover, a comparison in detail of MPTC and the proposed MPTFC techniques regarding torque, current, and fluxes ripples is performed. The stability of the modified adaptive closed-loop observer for speed, flux and parameters estimation methodology is proven for a wide range of speeds via Lyapunov’s theorem.

Study on Driver and Vehicle System With Lane-Tracking Control System Using Steering Torque Input

2001 ◽  
Author(s):  
Masao Nagai ◽  
Hidehisa Yoshida ◽  
Kiyotaka Shitamitsu ◽  
Hiroshi Mouri
Keyword(s):  
Tracking Control ◽  
High Speed ◽  
Driving Simulator ◽  
Tracking System ◽  
Torque Control ◽  
Steering Wheel ◽  
Control Input ◽  
Steering Angle ◽  
Lane Tracking ◽  
Steering Torque

Abstract Although the vast majority of lane-tracking control methods rely on the steering wheel angle as the control input, a few studies have treated methods using the steering torque as the input. When operating vehicles especially at high speed, drivers typically do not grip the steering wheel tightly to prevent the angle of the steering wheel from veering off course. This study proposes a new steering assist system for a driver not with the steering angle but the steering torque as the input and clarifies the characteristics and relative advantages of the two approaches. Then using a newly developed driving simulator, characteristics of human drivers and the lane-tracking system based on the steering torque control are investigated.

Performance improvement of direct torque control for induction motor drive via fuzzy logic-feedback linearization

2019 ◽  
Vol 38 (2) ◽  
pp. 672-692 ◽  
Author(s):  
Abdelkarim Ammar
Keyword(s):  
Fuzzy Logic ◽  
Robust Control ◽  
Feedback Linearization ◽  
Control Algorithm ◽  
Direct Torque Control ◽  
Torque Control ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Content Type ◽  
Control Approach

Purpose This paper aims to propose an improved direct torque control (DTC) for the induction motor’s performance enhancement using dual nonlinear techniques. The exact feedback linearization is implemented to create a linear decoupled control. Besides, the fuzzy logic control approach has been inserted to generate the auxiliary control input for the feedback linearization controller. Design/methodology/approach To improve the DTC for induction motor drive, this work suggests the incorporation of two nonlinear approaches. As the classical feedback linearization suffers while the presence of uncertainties and modeling inaccuracy, it is recommended to be associated to another robust control approach to compensate the uncertainties of the model and make a robust control versus the variations of the machine parameters. Therefore, fuzzy logic controllers will be integrated as auxiliary inputs to the feedback linearization control law. Findings The simulation and the experimental validation of the proposed control algorithm show that the association of dual techniques can effectively achieve high dynamic behavior and improve the robustness against parameters variation and external disturbances. Moreover, the space vector modulation is used to preserve a fixed switching frequency, reduce ripples and low switching losses. Practical implications The theoretical, simulation and experimental studies prove that the proposed control algorithm can be used on different AC machines for variable speed drive applications such as oil drilling, traction systems and wind energy conversion systems. Originality/value The proposed DTC strategy has been developed theoretically and realized through simulation and experimental implementation. Different operation conditions have been conducted to check the ability and robustness of the control strategy, such as steady state, speed reversal maneuver, low-speed operation and parameters variation test with load application.

Enabling impedance-based physical human–multi–robot collaboration: Experiments with four torque-controlled manipulators

2021 ◽  
pp. 027836492110536
Author(s):  
Niels Dehio ◽  
Joshua Smith ◽  
Dennis L. Wigand ◽  
Pouya Mohammadi ◽  
Michael Mistry ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Contact Forces ◽  
Torque Control ◽  
Human Interaction ◽  
Physical Interaction ◽  
Complex Situation ◽  
Control Approach ◽  
Model Based ◽  
Compliant Control ◽  
Multi Robot

Robotics research into multi-robot systems so far has concentrated on implementing intelligent swarm behavior and contact-less human interaction. Studies of haptic or physical human-robot interaction, by contrast, have primarily focused on the assistance offered by a single robot. Consequently, our understanding of the physical interaction and the implicit communication through contact forces between a human and a team of multiple collaborative robots is limited. We here introduce the term Physical Human Multi-Robot Collaboration (PHMRC) to describe this more complex situation, which we consider highly relevant in future service robotics. The scenario discussed in this article covers multiple manipulators in close proximity and coupled through physical contacts. We represent this set of robots as fingers of an up-scaled agile robot hand. This perspective enables us to employ model-based grasping theory to deal with multi-contact situations. Our torque-control approach integrates dexterous multi-manipulator grasping skills, optimization of contact forces, compensation of object dynamics, and advanced impedance regulation into a coherent compliant control scheme. For this to achieve, we contribute fundamental theoretical improvements. Finally, experiments with up to four collaborative KUKA LWR IV+ manipulators performed both in simulation and real world validate the model-based control approach. As a side effect, we notice that our multi-manipulator control framework applies identically to multi-legged systems, and we execute it also on the quadruped ANYmal subject to non-coplanar contacts and human interaction.

Adaptive Integrated Control for Omnidirectional Mobile Manipulators Based on Neural-Network

2011 ◽  
pp. 310-325
Author(s):  
Xiang-min Tan ◽  
Dongbin Zhao ◽  
Jianqiang Yi ◽  
Dong Xu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Integrated Control ◽  
External Disturbance ◽  
Torque Control ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Modeling And Control ◽  
Control Approach ◽  
Proposed Model

An omnidirectional mobile manipulator, due to its large-scale mobility and dexterous manipulability, has attracted lots of attention in the last decades. However, modeling and control of such systems are very challenging because of their complicated mechanism. In this paper, an unified dynamic model is developed by Lagrange Formalism. In terms of the proposed model, an adaptive integrated tracking controller, based on the computed torque control (CTC) method and the radial basis function neural-network (RBFNN), is presented subsequently. Although CTC is an effective motion control strategy for mobile manipulators, it requires precise models. To handle the unmodeled dynamics and the external disturbance, a RBFNN, serving as a compensator, is adopted. This proposed controller combines the advantages of CTC and RBFNN. Simulation results show the correctness of the proposed model and the effectiveness of the control approach.

Supervisory Control of Drilling

1996 ◽  
Vol 118 (1) ◽  
pp. 10-19 ◽  
Author(s):  
R. J. Furness ◽  
A. Galip Ulsoy ◽  
C. L. Wu
Keyword(s):  
Process Control ◽  
Torque Control ◽  
Feed Speed ◽  
Machining Time ◽  
Control Approach ◽  
Drilling Operation ◽  
Control Functions ◽  
Control Concept ◽  
Supervisory Process ◽  
And Control

A supervisory process control approach to machining is presented in this paper, and demonstrated by application to a drilling operation. The supervisory process control concept incorporates optimization and control functions in a hierarchical structure. This approach utilizes feedback measurements to parameterize the constraints of a process optimization problem whose solution determines both strategies and references for process control. For this particular drilling operation, a three-phase strategy (utilizing a combination of feed, speed, and torque control) evolved due to inherent variation in constraint activity as a function of hole depth. A controller comparison study was conducted which demonstrates the advantages of this approach compared to (1) uncontrolled “conventional” drilling, (2) feed and speed controlled drilling, and (3) torque and speed controlled drilling. Benefits of reduced machining time, improved hole quality, and the elimination of tool breakage are demonstrated, and the potential economic impact is highlighted for an example production application.

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