The Application of Model Predictive Control (MPC) to Fast Systems Like Autonomous Ground Vehicles (AGV)

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Car-like Vehicle Models
A car-like vehicle resembles completely an automobile. It consists of four wheels for locomotion and is capable of being steered from one place to another. Car-like vehicles model can be classified as rear-wheel, front-wheel and four-wheel driving ground vehicles.

For a rear wheel drive vehicle, the rear tires handle the engine dynamics while the front only needs to handle the steering forces. Figure 2, depicts the vehicle model schematic for a rear drive vehicle. The states of the model are x = [x y 〖 ]〗^T , where (x; y) are the centre point coordinates of the rear axle,  is the heading angle of the car body with respect to the x-axis. In figure 2, the angle  is the steering angle of the front wheels, and can be referred as a control input. The distance between the front and the rear axles is represented by l. The following mathematical model describes the kinematic relationship of the rear-wheel drive ground vehicle: [1] x ̇= v cos  y ̇ = v sin  (1)
 ̇ = v (tan φ )/l
Or, in compact representation, x ̇ = f(x,u); (2)
The steering angle  and line velocity v are used as a control input, i.e. u = [  v〖 ]〗^T.

Bicycle Model
A bicycle model can be used to represent a four wheel vehicle; any vehicle model can be described as a bicycle model [10]. In a bicycle model, the two front wheels are lumped into one wheel and the two back wheels are also lumped into one. For the bicycle model, the complete form of the dynamic model of the vehicle is given by [11];

β ̇=(2C_f)/(mv_x ) [δ_f-β-(l_f ( ) ̇)/v_x ]+(2C_r)/(mv_x ) [-β+(l_r ( ) ̇)/v_x ]-( ) ̇
( ) ̇=( ) ̇
( ) ̈=(2l_f C_f)/I_z [δ_f-β-(l_f ( ) ̇)/v_x ]-(2C_r l_r)/I_z [-β+(l_r ( ) ̇)/v_x ]
X ̇=v_x cos⁡()-v_x tan(β)sin⁡()
Y ̇=v_x sin⁡(...

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... directions. Computers and Chemical Engineering, 23(2):187-202, 1998. URL http://www.sciencedirect.com/science/article/pii/S0098135498002609. 17. J. O. Trierweiler and A. R. Secchi. Exploring the potentiality of using multiple model approach in nonlinear model predictive control. In Frank Allgwer and Alex Zheng, editors, Nonlinear Model Predictive Control, volume 26 of Progress in Systems and Control Theory, pp. 191-203. Birkhuser Basel, 2000. URL http://dx.doi.org/10.1007/978-3-0348-8407-5_11.
18. Gomes DA Silva JR. J. M. Kuhne F., Lages W. F. Model predictive control of a mobile robot using linearization. In: IEEE International Conference on Mechatronics and Robotics, Aachen, Germany. Pp. 525 - 530, 2004.
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