A hierarchical adaptive control framework of path tracking and roll stability for intelligent heavy vehicle with MPC

2020 ◽  
Vol 234 (13) ◽  
pp. 2933-2946
Author(s):  
Yantao Tian ◽  
Kai Huang ◽  
Xuanhao Cao ◽  
Yulong Liu ◽  
Xuewu Ji
Keyword(s):  
Adaptive Control ◽  
Cooperative Control ◽  
Path Tracking ◽  
Control Mode ◽  
Steering Wheel ◽  
Change Scenario ◽  
Heavy Vehicle ◽  
Steering Control ◽  
Control Framework ◽  
Conflicting Objectives

Roll stability is a major concern in the path tracking process of intelligent heavy vehicles in emergency steering maneuvers. Due to the coupling of lateral and roll motions of heavy vehicle, steering actions for path tracking may come into conflict with that for roll stability. In this paper, a hierarchical adaptive control framework composed of a supervisor, an upper controller and a lower controller is developed to mediate conflicting objectives of path tracking and roll stability via steering control. In the supervisor, path tracking control mode or cooperative control mode of path tracking and roll stability is determined by the predicted rollover index, and a weight function is introduced to balance the control objectives of path tracking and roll stability in cooperative control mode. Then, in order to achieve multi-objective real-time optimization, model predictive control with varying optimization weights is used in the upper controller to calculate the desired front steer angle. The lower controller which integrates the real electrically assisted hydraulic steering system based on Proportional-Integral-Derivative control is designed to control steering wheel angle. Simulation and hardware-in-loop implementation results in double lane change scenario show that the proposed hierarchical adaptive control framework can enhance roll stability in emergency steering maneuvers while keeping the accuracy of path tracking for intelligent heavy vehicle within an acceptable range.

Driver Steering Control and a New Perspective on Car Handling Qualities

2005 ◽  
Vol 219 (10) ◽  
pp. 1041-1051 ◽  
Author(s):  
R S Sharp
Keyword(s):  
White Noise ◽  
Gaussian White Noise ◽  
Design Parameters ◽  
Steering Wheel ◽  
Steering Control ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Handling Qualities ◽  
Gaussian White Noise Process ◽  
Low Pass

The article is about steering control of cars by drivers, concentrating on following the lateral profile of the roadway, which is presumed visible ahead of the car. It builds on previously published work, in which it was shown how the driver's preview of the roadway can be combined with the linear dynamics of a simple car to yield a problem of discrete-time optimal-linear-control-theory form. In that work, it was shown how an optimal ‘driver’ of a linear car can convert the path preview sample values, modelled as deriving from a Gaussian white-noise process, into steering wheel displacement commands to cause the car to follow the previewed path with an attractive compromise between precision and ease. Recognizing that real roadway excitation is not so rich in high frequencies as white-noise, a low-pass filter is added to the system. The white-noise sample values are filtered before being seen by the driver. Numerical results are used to show that the optimal preview control is unaltered by the inclusion of the low-pass filter, whereas the feedback control is affected diminishingly as the preview increases. Then, using the established theoretical basis, new results are generated to show time-invariant optimal preview controls for cars and drivers with different layouts and priorities. Tight and loose controls, representing different balances between tracking accuracy and control effort, are calculated and illustrated through simulation. A new performance criterion with handling qualities implications is set up, involving the minimization of the preview distance required. The sensitivities of this distance to variations in the car design parameters are calculated. The influence of additional rear wheel steering is studied from the viewpoint of the preview distance required and the form of the optimal preview gain sequence. Path-following simulations are used to illustrate relatively high-authority and relatively low-authority control strategies, showing manoeuvring well in advance of a turn under appropriate circumstances. The results yield new insights into driver steering control behaviour and vehicle design optimization. The article concludes with a discussion of research in progress aimed at a further improved understanding of how drivers control their vehicles.

Effectiveness of the running system of a wheeled vehicle

2020 ◽  
pp. 16-22
Author(s):  
D.A. Dubovik
Keyword(s):  
Steering Wheel ◽  
Steering Control ◽  
Control Effectiveness ◽  
Power Drive ◽  
Running System ◽  
Wheeled Vehicle ◽  
Curvilinear Motion ◽  
Wheeled Vehicles ◽  
System Power ◽  
Effectiveness Coefficient

A method for quantitative assessment of the effectiveness of the running system of wheeled vehicles for the general case of curvilinear motion is proposed. An expression is obtained for calculating the coefficient of efficiency of the running system of a wheeled vehicle, taking into account the parameters of the power and steering wheel drives. The results of evaluating the effectiveness of the running system of an off-road vehicle with a wheel arrangement of 8Ѕ8 and two front steerable axles are presented. Keywords: wheeled vehicle, running system, power drive, drive wheels, steering control, effectiveness, coefficient of efficiency. [email protected]

Co-Simulation of Power Steering Control and Vehicle Dynamic Responses

2001 ◽  
Author(s):  
Gene Y. Liao
Keyword(s):  
Dynamic Model ◽  
General Purpose ◽  
Dynamic Responses ◽  
Steering Wheel ◽  
Vehicle Dynamic ◽  
Steering Control ◽  
Integrated Simulation ◽  
Full Vehicle ◽  
Power Steering ◽  
Vehicle Dynamic Model

Abstract Many general-purpose and specialized simulation codes are becoming more flexible which allows analyses to be carried out simultaneously in a coupled manner called co-simulation. Using co-simulation technique, this paper develops an integrated simulation of an Electric Power Steering (EPS) control system with a full vehicle dynamic model. A full vehicle dynamic model interacting with EPS control algorithm is concurrently simulated on a single bump road condition. The effects of EPS on the vehicle dynamic behavior and handling responses resulting from steer and road input are analyzed and compared with proving ground experimental data. The comparisons show reasonable agreement on tie-rod load, rack displacement, steering wheel torque and tire center acceleration. This developed co-simulation capability may be useful for EPS performance evaluation and calibration as well as for vehicle handling performance integration.

scholarly journals A Fault Tolerant Vehicle Stability Control Using Adaptive Control Allocation

2018 ◽  
Author(s):  
Ozan Temiz ◽  
Melih Cakmakci ◽  
Yildiray Yildiz
Keyword(s):  
Adaptive Control ◽  
Fault Tolerant ◽  
Stability Control ◽  
Steering Wheel ◽  
Slip Angle ◽  
Control Allocation ◽  
Control Input ◽  
Allocation Strategy ◽  
Virtual Control ◽  
Yaw Stability

This paper presents an integrated fault-tolerant adaptive control allocation strategy for four wheel frive - four wheel steering ground vehicles to increase yaw stability. Conventionally, control of brakes, motors and steering angles are handled separately. In this study, these actuators are controlled simultaneously using an adaptive control allocation strategy. The overall structure consists of two steps: At the first level, virtual control input consisting of the desired traction force, the desired moment correction and the required lateral force correction to maintain driver’s intention are calculated based on the driver’s steering and throttle input and vehicle’s side slip angle. Then, the allocation module determines the traction forces at each wheel, front steering angle correction and rear steering wheel angle, based on the virtual control input. Proposed strategy is validated using a non-linear three degree of freedom reduced two-track vehicle model and results demonstrate that the vehicle can successfully follow the reference motion while protecting yaw stability, even in the cases of device failure and changed road conditions.

Steering Control Based on the Yaw Rate and Projected Steering Wheel Angle in Evasion Maneuvers

2018 ◽  
Author(s):  
Yifan Ye ◽  
Jian Zhao ◽  
Jian Wu ◽  
Bing Zhu ◽  
Yang Zhao ◽  
...  
Keyword(s):  
Steering Wheel ◽  
Steering Control ◽  
Yaw Rate

A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework

2020 ◽  
Author(s):  
Meenakshi Narayan ◽  
Ann Majewicz Fey
Keyword(s):  
Time Series ◽  
Adaptive Control ◽  
Real Time ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Force Sensor ◽  
Sensor Data ◽  
Series Data ◽  
Model Free ◽  
Control Framework

Abstract Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).

Algorithmic support of low-altitude aircraft flight based on the hazard analysis of the flight situation

2021 ◽  
Author(s):  
V.A. Malyshev ◽  
A.S. Leontyev ◽  
S.P. Poluektov ◽  
Е.М. Volotov
Keyword(s):  
Adaptive Control ◽  
Flight Control ◽  
Control Algorithm ◽  
Hazard Analysis ◽  
Critical Value ◽  
Control Mode ◽  
Manual Control ◽  
Flight Safety ◽  
Low Altitude ◽  
Adaptive Control Algorithm

Low-altitude flight of an aircraft is an effective, but at the same time, a very complex tactical technique, during which the crew does not always have the opportunity to timely recognize the occurrence of an abnormal case, determine the way out of it and counteract an aviation accident development. Despite many advantages of the automatic mode of low-altitude flight performing, its practical implementation is associated with a number of features and disadvantages, which determined the preference for the manual mode of low-altitude flight control. These are the presence of telltale factors, limited ability of performing flights at night and in difficult weather conditions, insufficient reliability etc. The considered features determined the relevance of the of low-altitude flight safety ensuring problem in relation to the manual control mode. As a result of an experimental study of the low-altitude flight performing process in a manual control mode, it was found that when performing manually-controlled low-altitude flight, a hazard assessment of the flight situation becomes pivotal. However the crew being under such conditions is not always able to correctly assess the flight situation hazard due to a combination of objective reasons. The current state of the adaptive and on-board flight safety systems theory makes it possible to increase the safety of the manuallycontrolled low-altitude flight by using adaptive control algorithms based on the flight situation hazard assessment. To solve this problem an adaptive control algorithm is proposed that ensures the formation of a security corridor in the longitudinal control channel, where the upper limit is determined by the critical value of the aircraft detection hazard, and the lower limit is determined by the critical value of the error in maintaining a given flight altitude. For a continuous assessment of the flight situation hazard and the timely formation of control signals the complex information about the current true flight altitude and the foreground is needed. Taking into account the peculiarities of low-altitude flight a digital terrain map containing data on natural and artificial obstacles along the flight route is a more rational source of information, that will make it possible to predict the development of the flight situation hazard. The above reasoning makes it possible to form an aircraft low-altitude flight adaptive control algorithm. A distinctive feature of the proposed algorithm is the implementation of a combined control variety where the pilot is provided with ample manual control opportunities within the security corridor, and the automatic flight control system is assigned the role of a safety subsystem that ensures control and timely return of the flight situation to normal flight conditions. The presented algorithm will allow to increase the crew logical-analytical activity information support during continuous analysis of the existing flight situation due to the formation of protective control actions based on the current flight situation hazard analysis.

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