Control Algorithms for Superposition Steering Systems to Adapt and Improve Steering Characteristics

Klier, Willy; Großheim, Reinhard; Schuster, W.

doi:10.4271/2005-01-1266

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…ACTIVE FRONT STEERING (AFS) technology enables an angle to be superimposed upon the steering angle generated by a human driver. It allows vehicle handling and stability to be improved [1]. Recently, AFS was used for automatic collision avoidance control [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

Game-Theoretic Modeling of the Steering Interaction Between a Human Driver and a Vehicle Collision Avoidance Controller

Cole

2015

IEEE Trans. Human-Mach. Syst.

168

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Development of vehicle active steering collision avoidance systems calls for mathematical models capable of predicting a human driver's response so as to reduce the cost involved in field tests whilst accelerate product development. This article provides a discussion on the paradigms that may be used for modelling a driver's steering interaction with vehicle collision avoidance control in path-following scenarios.Four paradigms, namely decentralized, noncooperative Nash, noncooperative Stackelberg and cooperative Pareto are established. The decentralized paradigm, developed based on optimal control theory, represents a driver's interaction with the collision avoidance controllers that disregard driver steering control. The noncooperative Nash and Stackelberg paradigms are used for predicting a driver's steering behaviour in response to the collision avoidance control that actively compensates for driver steering action. These two are devised based on the principles of equilibria in noncooperative game theory. The cooperative Pareto paradigm is derived from cooperative game theory to model a driver's interaction with the collision avoidance systems that take into account the driver's target path. The driver and the collision avoidance controllers' optimization problems and their resulting steering strategies arise in each paradigm are delineated. Two mathematical approaches applicable to these optimization problems, namely the distributed Model Predictive Control and the Linear Quadratic dynamic optimization approaches are described in some detail. A case study illustrating a conflict in steering control between driver and vehicle collision avoidance system is performed via simulation. It was found that variation of driver path-error cost function weights results in a variety of steering behaviours which are distinct between paradigms.

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Section: Introductionmentioning

confidence: 99%

Game-Theoretic Modeling of the Steering Interaction Between a Human Driver and a Vehicle Collision Avoidance Controller

Cole

2015

IEEE Trans. Human-Mach. Syst.

168

View full text Add to dashboard Cite

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“…It can be seen that ( 22) is structurally comparable to (11) concerned in the derivation of driver model GI. However, unlike (11) which has to be solved numerically (e.g. using the QR algorithm), ( 22) can be solved analytically.…”

Section: Driver Steering Control Model Giimentioning

confidence: 80%

“…Substituting (7) back to (12) gives: 14) consists of a number of M1 optimum steering angles, ranging from 1 () k  to 11 ( 1) kM  +− . These steering angles are calculated in the condition that they are equally weighted under the least-squares problem (11). In reality, only one steering angle action can be applied to a vehicle at time step k. To this end, the 'receding horizon' idea is adopted, which involves taking the first element 1 () k  as the driver's steering angle action at time step k. The steering angles from 1 ( 1) k  + up to 11 ( 1) kM  +− , despite having been solved under (11), were eventually discarded.…”

Section: S0mentioning

confidence: 99%

“…The first and second authors of the present paper developed a noncooperative-gametheoretic framework to model human drivers' steering interaction with an automated steering system [10]. The automated steering system studied in [10] is an Active Front Steering (AFS) system [11] [12] which is capable of applying steering control independently of the driver. In [10], the interaction between the driver and the AFS controller was formulated as a noncooperative-Nash-game problem, and a driver steering control model was developed under the assumption that the interaction reaches an open-loop Nash equilibrium.…”

Section: Introductionmentioning

confidence: 99%

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Two Nash-equilibrium-based steering control models for representing a driver’s interaction with vehicle automated steering

Cole

2021

Vehicle System Dynamics

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Automated steering technology offers significant benefits to the safety and efficiency of vehicles, but desire to keep the human driver in the loop requires better understanding of the interaction between driver and vehicle. An existing noncooperative-game-theoretic framework for modelling such interaction is revisited in this paper, leading to the development of two alternative driver steering control models. Both models bears Nash-equilibrium properties but involve different assumptions about driver steering control behaviour. A simulation study is performed to demonstrate the difference between the two driver models in terms of their simulated driver steering angles and vehicle lateral motions. An experiment using a fixed-base driving simulator is conducted to measure six test subjects' steering control behaviour in response to the lanechange manoeuvres generated by an automated steering control system. The two proposed driver models are fitted to the measured driver steering angle data to investigate their capabilities for representing individual driver's steering control behaviour. Key model parameters are identified following a system identification procedure. It is found that the two driver models have equivalent capability for capturing the trend exhibited in all the six test subjects' steering angle histories, but less good at reproducing the severe overshoot or oscillation involved in two subjects' measured steering angles. It is found that the inclusion of an arm neuromuscular system model can improve the performance of the proposed driver models on representing human driver steering control behaviour.

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“…This additional degree of freedom enables a continuous and drivingsituation-dependent adaptation of the steering characteristics. Features such as steering comfort, effort, and steering dynamics are optimized, and stabilizing steering interventions can be performed [1]. Figure 1 shows the AFS principle: the driver controls the course of the vehicle via the steering wheel.…”

Section: Introductionmentioning

confidence: 99%

Design and Comparison of AFS Controllers with PID, Fuzzy-Logic, and Sliding-Mode Controllers

Song

2013

Advances in Mechanical Engineering

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Three active front-steering (AFS) controllers were developed to enhance the lateral stability of a vehicle. They were designed using proportional-integral-derivative (PID), fuzzy-logic, and sliding-mode control methods. The controllers were compared under several driving and road conditions with and without the application of braking force. A 14-degree-of-freedom vehicle model, a sliding-mode antilock brake system (ABS) controller, and a driver model were also employed to test the controllers. The results show that the three AFS controllers allowed the yaw rate to follow the reference yaw rate very well, and consequently the lateral stability improved. On a split-road, the controllers forced the vehicle to proceed straight ahead. The results also verify that the driver model can sufficiently control the vehicle to allow it to follow a desired path.

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Control Algorithms for Superposition Steering Systems to Adapt and Improve Steering Characteristics

Cited by 5 publications

References 4 publications

Game-Theoretic Modeling of the Steering Interaction Between a Human Driver and a Vehicle Collision Avoidance Controller

Game-Theoretic Modeling of the Steering Interaction Between a Human Driver and a Vehicle Collision Avoidance Controller

Two Nash-equilibrium-based steering control models for representing a driver’s interaction with vehicle automated steering

Design and Comparison of AFS Controllers with PID, Fuzzy-Logic, and Sliding-Mode Controllers

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