Model Predictive Control (MPC) Based Combined Wheel Torque and Steering Control Using a Simplified Tire Model

2010 ◽  
Author(s):  
Ganesh Adireddy ◽  
Taehyun Shim ◽  
Douglas Rhode ◽  
Jahan Asgari
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Effective Means ◽  
Torque Control ◽  
Tire Model ◽  
Steering Control ◽  
Computationally Efficient ◽  
Wheel Torque ◽  
Chassis Control ◽  
Vehicle Chassis

Wheel torque control and active front steer are effective means of improving vehicle handling and stability. In this paper, a vehicle chassis control system that controls both wheel torques at each wheel and front steer has been developed using model predictive control in order to enhance vehicle yaw motion and ability to track the desired trajectory. A simplified nonlinear tire model that is computationally efficient and easy to implement in the control algorithms and an 8 degree of freedom (DOF) vehicle model are used in the controller. The performance of this controller is compared to that based on well known Magic Formula tire model. The effectiveness and limitations of the proposed controller are discussed through simulation.

Wheel Torque Controller Development Using a Simplified Tire Model for Vehicle Handling Enhancement

2009 ◽  
Author(s):  
Ganesh Adireddy ◽  
Taehyun Shim ◽  
Douglas Rhode
Keyword(s):  
Predictive Control ◽  
Essential Element ◽  
Control Algorithms ◽  
Slip Angle ◽  
Tire Model ◽  
Computationally Efficient ◽  
Vehicle Handling ◽  
Yaw Rate ◽  
Wheel Torque ◽  
Tire Models

A tire model is an essential element in the vehicle controller development and various complexities of tire models have been developed and used. It is highly desirable for the control systems to use a tire model that is computationally efficient and easy to implement in control algorithms while providing desired performance. In this paper, a wheel torque controller was developed using a non-linear predictive control theory, 8 degree of freedom vehicle model, and a simplified nonlinear tire model in order to control the vehicle yaw rate and side slip angle. The performance of this controller was compared to that based on well known Magic Formula tire model. The effectiveness and limitations of the proposed controller were discussed through simulation.

Distribution of Wheel Drive/Brake Torque Using Non-Linear Predictive Control to Enhance Vehicle Handling

2006 ◽  
Author(s):  
Chinar Ghike ◽  
Taehyun Shim ◽  
Jahan Asgari
Keyword(s):  
Predictive Control ◽  
Effective Means ◽  
Torque Control ◽  
Slip Angle ◽  
Differential Distribution ◽  
Vehicle Handling ◽  
Brake Torque ◽  
Emergency Braking ◽  
Non Linear ◽  
Wheel Torque

Wheel torque control is an effective means of improving vehicle handling and stability. Brake-based electronic stability programs which intervene in extreme situations to regulate vehicle behavior are the most common form of wheel torque control. With the advent of advanced driveline technologies, wheel torque control can also be achieved by the differential distribution of available drive torque to all four wheels. Similarly, in combined cornering and braking maneuvers, the applied brake input can be differentially distributed to regulate vehicle handling. This paper proposes an integrated scheme for wheel torque control that combines differential drive/brake torque distribution with the emergency braking control to regulate the vehicle yaw rate and side slip angle. A wheel torque controller was developed using a non-linear predictive control theory, 8 degree of freedom vehicle model, and nonlinear tires. The simulated vehicle responses show improved vehicle handling performance.

scholarly journals Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes

2019 ◽  
Vol 11 (6) ◽  
pp. 168781401985978
Author(s):  
Ja-Ho Seo ◽  
Kwang-Seok Oh ◽  
Hong-Jun Noh
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Steering System ◽  
Driver Model ◽  
Control Technique ◽  
Change Scenario ◽  
Steering Control ◽  
Human Driver ◽  
Steering Effort

All-terrain cranes with multi-axles have large inertia and long distances between the axles that lead to a slower dynamic response than normal vehicles. This has a significant effect on the dynamic behavior and steering performance of the crane. Therefore, the purpose of this study is to develop an optimal steering control algorithm with a reduced driver steering effort for an all-terrain crane and to evaluate the performance of the algorithm. For this, a model predictive control technique was applied to an all-terrain crane, and a steering control algorithm for the crane was proposed that could reduce the driver’s steering effort. The steering performances of the existing steering system and the steering system applied with the newly developed algorithm were compared using MATLAB/Simulink and ADAMS with a human driver model for reasonable performance evaluation. The simulation was performed with both a double lane change scenario and a curved-path scenario that are expected to happen in road-steering mode.

Multi-parametric model predictive control for autonomous steering using an electric power steering system

2019 ◽  
Vol 233 (13) ◽  
pp. 3391-3402
Author(s):  
Junho Lee ◽  
Hyuk-Jun Chang
Keyword(s):  
Model Predictive Control ◽  
Quadratic Programming ◽  
Electric Power ◽  
Predictive Control ◽  
Parametric Model ◽  
Effective Control ◽  
Steering Control ◽  
Electric Power Steering ◽  
Parametric Quadratic Programming ◽  
Power Steering

Electric power steering systems have been used to generate assist torque for driver comfort. This study makes use of the functionality of electric power steering systems for autonomous steering control without driver torque. A column-type electric power steering test bench, equipped with a brushless DC motor as an assist motor, and the Infineon TriCore AURIX TC 277 microcontroller was used in this study. Multi-parametric model predictive control is based on a model predictive control–based approach that employs a multi-parametric quadratic programming technique. This technique allows the reduction of the huge computational burden resulting from the online optimization in model predictive control. The proposed controller obtains an optimal input based on multi-parametric quadratic programming at each sampling time. The weighting matrix definition, which is the main task when designing the proposed controller, was analyzed. The experimental results of the step response of the steering wheel angle verified the tracking ability of the proposed controller for different ranges of the prediction horizon. Since the computational loads are directly related to functional safety, the results of this study support the use of the multi-parametric model predictive control scheme as an effective control method for autonomous steering control.

Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

2019 ◽  
Vol 233 (10) ◽  
pp. 1422-1437
Author(s):  
Keji Chen ◽  
Xiaofei Pei ◽  
Daoyuan Sun ◽  
Zhenfu Chen ◽  
Xuexun Guo ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Model Predictive Control ◽  
Collision Avoidance ◽  
Predictive Control ◽  
Autonomous Vehicle ◽  
Steering Control ◽  
Safe Driving ◽  
Active Steering ◽  
Active Steering Control

Leveraging the advancements in sensor and mapping technologies, the collision-free autonomous vehicle becomes possible in the future. In this article, a case study of collision avoidance by active steering control is presented and verified by a driver-in-the-loop platform. The proposed control system integrates a risk assessment algorithm and a hierarchical model predictive control approach to ensure a safe driving. First, a fuzzy logic is used to estimate the potential conflict. Besides, a nonlinear model predictive control is introduced in the upper layer of the model predictive controller to generate a collision-free trajectory. Furthermore, the lower layer determines the optimal steering angle based on the linear time-variant model predictive control to follow the replanning path. The performance of the controller has been evaluated in the real-time driver-in-the-loop test. The results show that the autonomous vehicle is able to avoid the collision with the surrounding vehicle that is operated by a real driver, and the performance of collision avoidance is improved by means of the risk assessment.

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