A Supervisory Lateral Slip Prevention Controller for Autonomous Vehicles

2019 ◽  
Author(s):  
Nikunj Kumbhani ◽  
Saeid Bashash
Keyword(s):  
Control System ◽  
Autonomous Vehicles ◽  
Feedback Linearization ◽  
Integrated Control ◽  
Linearization Method ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Supervisory Controller ◽  
Simulated Environment ◽  
Predictive Controller

Abstract This paper analyzes the behavior of autonomous vehicles in simulated environment and develops an integrated control system for maneuvering and slip prevention in curvy roads. First, a longitudinal and lateral control system is designed for the vehicle using the feedback linearization method. The longitudinal controller enforces tracking the desired velocity, while the lateral controller steers the vehicle toward, and maintains it on a desired lane. A two-level supervisory controller is then developed to prevent lateral slip while driving on curvy roads. On the lower level, the steering wheel angle is actively limited based on the vehicle speed to avoid oversteering. On the supervisory level, a predictive controller is integrated into the system to optimally slow down the vehicle ahead of a detected road curvature. Simulation results indicate the effectiveness of the proposed schemes in maintaining desirable maneuvering conditions and preventing lateral slips.

Stability of Controlled Road Vehicles: A Preliminary Fundamental Study

2015 ◽  
Author(s):  
Fabio della Rossa ◽  
Massimiliano Gobbi ◽  
Giampiero Mastinu ◽  
Carlo Piccardi ◽  
Giorgio Previati
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Preliminary Investigation ◽  
Basin Of Attraction ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Vehicle Model ◽  
The Stability ◽  
Positive Effect

A comparison of the lateral stability behaviour between an autonomous vehicle, a vehicle with driver and a vehicle without driver (fixed steering wheel) is made by introducing a simple mathematical model of a vehicle running on even road. The mechanical model of the vehicle has two degrees of freedom and the related equations of motion contain the nonlinear tyre characteristics. The driver is described by a well-known model proposed in the literature. The autonomous vehicle has a virtual driver (robot) that behaves substantially like a human, but with its proper reaction time and gain. The road vehicle model has been validated. The study of vehicle stability has to be based on bifurcation analysis and a preliminary investigation is proposed here. The accurate computation of steady-state equilibria is crucial to study the stability of the three kinds of vehicles here compared. The stability of the bare vehicle without driver (fixed steering wheel) is studied in a rather complete way referring to a number of combinations of tyre characteristics. The (known) conclusion is that the understeering vehicle is stable at each lateral acceleration level and at each vehicle speed. The additional (partially unknown) conclusion is that the vehicle (model) with degradated tyres may exhibit a huge number of different bifurcations. The driver has many effects on the stability of the vehicle. One positive effect is to eliminate the many possible different equilibria of the bare vehicle and keep active one single equilibrium only. Another positive effect is to broaden the basin of attraction of stable equilibria (at least at relatively low speed). A negative effect is that, even for straight running, the driver seem introducing a subcritical Hopf bifurcation which limits the maximum forward speed of some understeering vehicles (that could run faster with fixed steering wheel). Both the mentioned positive and negative effects appear to be applicable to autonomous vehicles as well. Further studies could be useful to overcome the limitations on the stability of current autonomous vehicles that have been identified in the present research.

Design of Variable Pump Displacement Controller based on FLSMC Method

2021 ◽  
pp. 2158008
Author(s):  
Dabing xue ◽  
Zhiqiang Chao ◽  
Xixia Liu ◽  
Huaying Li ◽  
Shousong Han ◽  
...  
Keyword(s):  
Control System ◽  
Feedback Linearization ◽  
Sliding Mode ◽  
Linearization Method ◽  
Load Parameter ◽  
Tracking Accuracy ◽  
Control Moment ◽  
Comparison Results ◽  
Displacement Pump ◽  
The Impact

To reduce the effect of nonlinear factors and improve the tracking accuracy of the control system, a controller based on feedback linearization sliding mode control (FLSMC) method is proposed. This paper takes a variable displacement pump driven by a constant speed motor as the research object to verify the effectiveness of the designed controller. First, a high-order nonlinear model of the variable pump displacement control mechanism is established. Meanwhile, the load characteristic of the control cylinder is obtained by analyzing the swashplate control moment. Then the author uses the feedback linearization method to linearize the system model and designs a sliding mode controller to eliminate the impact of load parameter changes. Finally, the proposed FLSMC controller is used in simulation and experiment, and the PID controller is used as a comparison. Results show that the FLSMC controller can effectively improve the robustness of the pump control system.

scholarly journals Designing Backstepping Control System for Hypersonic Vehicle Based on Feedback Linearization

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jianli Wei ◽  
Huan Chen
Keyword(s):  
Control System ◽  
Feedback Linearization ◽  
Control Method ◽  
Control Volume ◽  
Parametric Uncertainty ◽  
Linearization Method ◽  
Backstepping Control ◽  
Robust Adaptive Control ◽  
Adaptive Backstepping ◽  
Robust Adaptive

A hypersonic vehicle uses the airbreathing scramjet engine and the airframe and engine integrated design. Therefore, there is a strong cross-coupling effect among its aerodynamic force, thrust, structure, and control. The nonlinearity and uncertainty of the model cause difficulties in control system design. Considering the nonlinearity, coupling characteristics, and aerodynamic parametric uncertainty of its longitudinal dynamic model, we design the control law for its altitude system and velocity system based on the adaptive backstepping control method. Because of the feedback linearization method, we introduce the constraints of the flight vehicle’s actuator into the design, obtaining the robust adaptive control system constrained by the actuator of the flight vehicle. To avoid the high-order derivation problem of the feedback linearization method and the derivation of the virtual control volume in adaptive backstepping control method, we use the arbitrary-order robust exact differentiator to solve the high-order derivatives in feedback linearization and utilize the command filter to obtain the virtual control volume and its derivatives. The simulation results show that the robust adaptive control system we designed can achieve the error-free tracking of altitude and velocity command. It can well overcome the influence of structural parameters, aerodynamic parametric uncertainty, and disturbances; meanwhile, the control command can satisfy the constraints of the actuator.

Speed-dependent coordinated control of differential and assisted steering for in-wheel motor driven electric vehicles

2017 ◽  
Vol 232 (9) ◽  
pp. 1206-1220 ◽  
Author(s):  
Te Chen ◽  
Xing Xu ◽  
Yong Li ◽  
Wujie Wang ◽  
Long Chen
Keyword(s):  
Control System ◽  
Electric Vehicles ◽  
Sliding Mode ◽  
Coordinated Control ◽  
Front Wheel ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Control Approach ◽  
Wheel Torque ◽  
Road Testing

In this paper, we present a coordinated control system of differential and assisted steering for in-wheel motor driven (IMD) electric vehicles (EVs) with two independent front-wheel drives. An electric differential (ED) control strategy is proposed to track the expected yaw rate based on sliding mode control (SMC). Meanwhile, to realize differential drive assisted steering (DDAS), a variable speed integral PID controller is used to follow the ideal steering wheel torque. The impacts of the coupling with the ED and DDAS systems on EVs are analyzed, and a coordinated control system with adaptive weighting dependent on vehicle speed is designed. Results of the simulation on the CarSim-Simulink joint platform for IMD EVs model show that the proposed coordinated control approach can effectively reduce the torque of a steering wheel while ensuring the vehicle’s stability. Finally, road testing results of IMD EVs are demonstrated to be comparable with joint simulations, indicating the correctness of this solution.

scholarly journals Nonlinear Steering Wheel Angle Control Using Self-Aligning Torque with Torque and Angle Sensors for Electrical Power Steering of Lateral Control System in Autonomous Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4384 ◽  
Author(s):  
Wonhee Kim ◽  
Chang Kang ◽  
Young-Seop Son ◽  
Chung Chung
Keyword(s):  
Control System ◽  
Autonomous Vehicles ◽  
Tracking Error ◽  
The Self ◽  
Steering Wheel ◽  
Measured Signal ◽  
Control Input ◽  
Nonlinear Controller ◽  
Lateral Control ◽  
Power Steering

The development of sensor technology enabled the use of composite sensors to measure the torque and angle of steering wheels at gradually decreasing costs while maintaining the required safety. The electric power steering (EPS) is vital to the safety of the car, therefore it is not worth sacrificing safety to save cost and the SWA control with angle sensor gradually becomes the mainstream. Existing methods to control steering wheel angle (SWA) for EPS consider the self-aligning torque as a disturbance that should be rejected. However, this torque is useful to return the SWA from an outward to the center position. Hence, we propose a nonlinear control of SWA using the self-aligning torque for EPS in the lateral control system of autonomous vehicles. The proposed method consists of a high-gain disturbance observer and a backstepping controller, where the former aims to estimate the self-aligning torque, and an auxiliary state variable prevents using the derivative of the measured signal. The nonlinear controller is designed via backstepping to bound the SWA tracking error. The self-aligning torque provides damping that can improve the controller tracking when following the same direction of the input torque on the steering wheel control. In this case, the control input can be reduced by the damping effect of the self-aligning torque. The performance of the proposed method is validated through EPS hardware-in-the-loop simulation.

Direct Current Deadbeat Predictive Controller for BLDC Motor Using Single DC-Link Current Sensor

2020 ◽  
Vol 38 (8A) ◽  
pp. 1187-1199
Author(s):  
Qaed M. Ali ◽  
Mohammed M. Ezzalden
Keyword(s):  
Control System ◽  
High Speed ◽  
Fast Response ◽  
Bldc Motor ◽  
Current Controller ◽  
Three Phase ◽  
Predictive Controller ◽  
Two Phases ◽  
Dc Current ◽  
Three Phase Inverter

BLDC motors are characterized by electronic commutation, which is performed by using an electric three-phase inverter. The direct control system of the BLDC motor consists of double loops; including the inner-loop for current regulating and outer-loop for speed control. The operation of the current controller requires feedback of motor currents; the conventional current controller uses two current sensors on the ac side of the inverter to measure the currents of two phases, while the third current would be accordingly calculated. These two sensors should have the same characteristics, to achieve balanced current measurements. It should be noted that the sensitivity of these sensors changes with time. In the case of one sensor fails, both of them must be replaced. To overcome this problem, it is preferable to use one sensor instead of two. The proposed control system is based on a deadbeat predictive controller, which is used to regulate the DC current of the BLDC motor. Such a controller can be considered as digital controller mode, which has fast response, high precision and can be easily implemented with microprocessor. The proposed control system has been simulated using Matlab software, and the system is tested at a different operating condition such as low speed and high speed.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

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