• Journal of Drive and Control
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• v.14 no.2
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.406-413
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• 2007

This paper proposes a self-recurrent wavelet neural network(SRWNN) based adaptive backstepping control technique for the robust steering control of autonomous underwater vehicles(AUVs) with unknown model uncertainties and external disturbance. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the steering model of AUV. The adaptation laws for the weights of SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for the on-line control of AUV. Finally, simulation results for steering control of an AUV with unknown model uncertainties and external disturbance are included to illustrate the effectiveness of the proposed method.

• Journal of Biosystems Engineering
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• v.21 no.3
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• pp.283-292
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• 1996

Automatic guidance of farm tractors would improve productivity by reducing operator fatigue and increasing machine performance. To control tractors within $\pm$5cm of the desired path, fuzzy and adaptive steering controllers were developed to evaluate their characteristics and performance. Two input variables were position and yaw errors, and a steering command was fed to tractor model as controller output. Trapezoidal membership functions were used in the fuzzy controller, and a minimum-variance adaptive controller was implemented into the 2-DOF discrete-time input-output model. For unit-step and composite paths, a dynamic tractor simulator was used to test the controllers developed. The results showed that both controllers could control the tractor within $\pm$5cm error from the defined path and the position error of tractor by fuzzy controller was the bigger of the two. Through simulations, the output of self-tuning adaptive controller was relatively smooth, but the fuzzy controller was very sensitive by the change of gain and the shape of membership functions. Contrarily, modeling procedure of the fuzzy controller was simple, but the adaptive controller had very complex procedure of design and showed that control performance was affected greatly by the order of its model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.281-282
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• 2012

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.12
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• pp.997-1003
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• 2016

This paper presents an application of adaptive algorithms for yaw moment distribution with electronic stability control (ESC) and active rear steering (ARS) in integrated chassis control (ICC). Integrated chassis control consists of upper- and lower-level controllers. In the upper-level controller, the control yaw moment is computed with sliding mode control required to stabilize a vehicle. In the lower-level controller, adaptive algorithms are applied to determine the required brake pressure of ESC and the necessary steering angle of ARS, in order to generate the control yaw moment. Simulation is performed using the vehicle simulation package CarSim to validate the proposed method.

• Journal of the Ergonomics Society of Korea
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• v.35 no.1
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• pp.11-27
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• 2016

Objective: The purpose of this study was to develop and evaluate high technology adaptive driving controls, such as mini steering wheel-lever system and joystick system, for the people with physical disabilities in the driving simulator. Background: The drivers with severe physical disabilities have problems in operation of the motor vehicle because of reduced muscle strength and limited range of motion. Therefore, if the remote control system with driver-by-wire technology is used for adaptive driving controls for people with physical limitations, the disabled people can improve their quality of life by driving a motor vehicle. Method: We developed the remotely controlled driving simulator with drive-by-wire technology, e.g., mini steering wheel-lever system and joystick system, in order to evaluate driving performance in a safe environment for people with severe physical disabilities. STISim Drive 3 software was used for driving test and the customized Labview program was used in order to control the servomotors and the adaptive driving devices. Thirty subjects participated in the study to evaluate driving performance associated with three different driving controls: conventional driving control, mini steering wheel-lever controls and joystick controls. We analyzed the driving performance in three different courses: straight lane course for acceleration and braking performance, a curved course for steering performance, and intersections for coupled performance. Results: The mini steering wheel-lever system and joystick system developed in this study showed no significant statistical difference (p>0.05) compared to the conventional driving system in the acceleration performance (specified speed travel time, average speed when passing on the right), steering performance (lane departure at the slow curved road, high-speed curved road and the intersection), and braking performance (brake reaction time). However, conventional driving system showed significant statistical difference (p<0.05) compared to the mini steering wheel-lever system or joystick system in the heading angle of the vehicle at the completion point of intersection and the passing speed of the vehicle at left turning. Characteristics of the subjects were found to give a significant effect (p<0.05) on the driving performance, except for the braking reaction time (p>0.05). The subjects with physical disabilities showed a tendency of relatively slow acceleration (p<0.05) at the straight lane course and intersection. The steering performance and braking performance were confirmed that there was no statistically significant difference (p>0.05) according to the characteristics of the subjects. Conclusion: The driving performance with mini steering wheel-lever system and joystick control system showed no significant statistical difference compared to conventional system in the driving simulator. Application: This study can be used to design primary controls with driver-by-wire technology for adaptive vehicle and to improve their community mobility for people with severe physical disabilities.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.636-639
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• 1997

In this paper, a dynamic control scheme is proposed which not only compensates for the lateral dynamics and longitudinal dynamics but also deal with the yaw motion dynamics. Using the dynamic control technique, adaptive and learning algorithm together, the proposed controller is not only robust to disturbance and parameter uncertainties but also can learn the inverse dynamics model in steady state. Based on the proposed dynamic control scheme, a dynamic vehicle simulator is contructed to design and test various control techniques for 4-wheel steering vehicles.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1832-1833
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• 2006

This paper presents a design method of the wavelet neural network(WNN) controller based on a direct adaptive control scheme for the intelligent control of Autonomous Underwater Vehicle(AUV) steering systems. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome nonlinearities and uncertainty. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and original signal of AUV model that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by gradient-descent method. Through computer simulations, we demonstrate the effectiveness of the proposed control method.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.181-184
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• 1999

Leading vehicle states are useful and essential elements in adaptive cruise control (ACC) system, collision warning (CW) and collision avoidance (CA) system, and automated highway system (AHS). There are many approaches in ACC using Kalman filter. Mostly only distance to leading vehicle and velocity difference are estimated and used for the above systems. Applications in road vehicle in curved road need to obtain more informations such as yaw angle, steering angle which can be estimated using vision system. Since vision system is not robust to environment change, we used Kalman filter to estimate distance, velocity, yaw angle, and steering angle. Application to active tracking of target vehicle is shown.

• Journal of Auto-vehicle Safety Association
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• v.9 no.3
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• pp.19-23
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• 2017

This paper presents a lateral driver model with neuromuscular system to evaluate the performance of electric power steering (EPS). Output of most previously developed driver models is steering angle. However, in order to evaluate EPS system, driver model which results in steering torque output is needed. The proposed lateral driver model mainly consists of 2 parts: desired steering angle calculation and conversion of steering angle into steering torque. Desired steering angle calculation part results in steering angle to track desired yaw rate for path tracking. Conversion of steering angle into torque is consideration with neuromuscular system. The proposed driver model is investigated via actual driving data. Compared to other algorithms, the proposed algorithm shows similar pattern of steering angle with human driver. The proposed driver can be utilized to efficiently evaluate EPS system in simulation level.