Linear time varying model based model predictive control for lateral path tracking (original) (raw)
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Model Predictive Control - Theory and Applications [Working Title]
In this chapter, we present a study on autonomous vehicle path tracking utilizing both Linear, Linear Time Varying, Nonlinear Model Predictive controllers and evaluate their performance on a given path. The vehicle is both a linear and non-linear bicycle model with front steering . The performance metric in the evaluation of the controller/path tracker is the root mean square error between the defined path and the resulting vehicle path. The path tracking algorithm utilized are Serret-Frenet Equations and a modified version of Pure Pursuit. The robustness of the controllers are examined by varying vehicle mass, tire-road coefficient of friction, and tire cornering stiffness.
Steering Angle Control of Car for Dubins Path-tracking Using Model Predictive Control
Journal of Physics: Conference Series, 2018
Car as one of transportation is inseparable from technological developments. About ten years, there are a lot of research and development on lane keeping system(LKS) which is a system that automaticaly controls the steering to keep the vehicle especially car always on track. This system can be developed for unmanned cars. Unmanned system car requires navigation, guidance and control which is able to direct the vehicle to move toward the desired path. The guidance system is represented by using Dubins-Path that will be controlled by using Model Predictive Control. The control objective is to keep the car's movement that represented by dinamic lateral motion model so car can move according to the path appropriately. The simulation control on the four types of trajectories that generate the value for steering angle and steering angle changes are at the specified interval.
Nonlinear Model Predictive Path-Following Control
2009
In the frame of this work, the problem of following parametrized reference paths via nonlinear model predictive control is considered. It is shown how the use of parametrized paths introduces new degrees of freedom into the controller design. Sufficient stability conditions for the proposed model predictive path-following control are presented. The method proposed is evaluated via simulations of an autonomous mobil robot.
Path Tracking Control Based on an Adaptive MPC to Changing Vehicle Dynamics
International Journal of Mechanical Engineering and Robotics Research
In this paper, an adaptive Model Predictive Controller (MPC) is proposed as a solution for path tracking control problem for autonomous vehicles. The effect of feeding the MPC with a continuously changing vehicle's mathematical model is studied, so that the controller becomes more adaptable to changing parameter values accompanied with instantaneous states. The proposed MPC is compared with both Stanley controller and a similar MPC that uses a fixed vehicle model. The performance is measured by the ability to minimize both lateral position and heading angle errors. A dynamic bicycle model for the vehicle is deployed in the MPC and the controllers are simulated in CarSim-MATLAB/Simulink co-simulation environment using three common maneuvers: S-Road, double lane change and curved road. Results show that the proposed controller gives better tracking performance than the two others with minimal instantaneous and root mean square RMS errors.
Nonlinear Model Predictive Control using Lyapunov Functions for Vehicle Lateral Dynamics
IFAC-PapersOnLine, 2016
The purpose of this paper is the development of a nonlinear model based predictive control strategy for the autonomous control of the steering system for ground vehicles. The control system is aimed to automatically steer the vehicle along a desired trajectory. The developed strategy uses control Lyapunov functions to guarantee the stability of the closed-loop control system. In order to obtain reliable results, a nonlinear vehicle dynamic model is used in the design phase of the controller. The model incorporates the tire-ground contact nonlinearities and describes with higher accuracy the real vehicle dynamics. The proposed approach is validated using simulation results and it is shown that this approach could provide good performances in practical use.
International Journal of Robust and Nonlinear Control, 2008
A model predictive control (MPC) approach for controlling an active front steering (AFS) system in an autonomous vehicle is presented. At each time step a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to best follow the desired trajectory on slippery roads at the highest possible entry speed. We start from the results presented in (Int. J. Veh. Auton. Syst. 2005; 3(2–4):265–291; IEEE Trans. Contr. Syst. Technol. 2007; 15(3)) and formulate the MPC problem based on successive online linearization of the nonlinear vehicle model (linear time-varying (LTV) MPC). We present a sufficient stability condition for such LTV MPC scheme. The condition is derived for a general class of nonlinear discrete time systems and results into an additional convex constraint to be included in the LTV MPC design.For the AFS control problem, we compare the proposed LTV MPC scheme with the LTV MPC scheme in (IEEE Trans. Contr. Syst. Technol. 2007; 15(3)) where stability has been enforced with an ad hoc constraint. Simulation and experimental tests up to 17 m/s on icy roads show the effectiveness of the LTV MPC formulation. Copyright © 2007 John Wiley & Sons, Ltd.
Nonlinear Model Predictive Path Following Controller with Obstacle Avoidance
Journal of Intelligent and Robotic Systems, 2021
In the control systems community, path-following refers to the problem of tracking an output reference curve. This work presents a novel model predictive path-following control formulation for nonlinear systems with constraints, extended with an obstacle avoidance strategy. The method proposed in this work simultaneously provides an optimizing solution for both, path-following and obstacle avoidance tasks in a single optimization problem, using Nonlinear Model Predictive Control (NMPC). The main idea consists in extending the existing NMPC controllers by the introduction of an additional auxiliary trajectory that maintains the feasibility of the successive optimization problems even when the reference curve is unfeasible, possibly discontinuous, relaxing assumptions required in previous works. The obstacle avoidance is fulfilled by introducing additional terms in the value functional, rather than imposing state space constraints, with the aim of maintaining the convexity of the state and output spaces. Simulations results considering an autonomous vehicle subject to input and state constraints are carried out to illustrate the performance of the proposed control strategy.
Case Study on a Proven Concept for Lateral Path Following Control
IFAC-PapersOnLine, 2019
A proven lateral path following concept will be introduced in the present study. The main idea is to increase the tracking accuracy by omitting a look ahead based control law and utilizing a simple model which nicely represents the lateral vehicle dynamic. The resulting cascade control structure, as well as the vehicle dynamic model, is discussed in detail. Based on this, model-based controller tuning methods are applied for the parameterization of the individual controllers. Finally, the results are verified with real-world test drive data. Within the scope of this study, it is aimed to propose a robust and easy to tune controller structure in order to regulate the lateral movement of the vehicle in a smooth way. In accordance with that aim, it is more focused on classical control methods than the more sophisticated and computational complex control strategies.
Model Predictive Control for Autonomous Vehicle Tracking
2021
This study develops model predictive control (MPC) schemes for controlling autonomous vehicles tracking on feasible trajectories generated from flatness or polynomial equations. All of the vehicle online moving parameters including coordinate positions, body orientation angle, and steering angle are included into the MPC optimizer for calculating the real-time optimal inputs for the vehicle linear velocity and its steering velocity to minimize the errors between the desired and the actual course of travel. The use of MPC can simplify and eliminate the complexity of controller design since MPC can work itself as a system modelling controller. MPC can also handle on-line the constraints of any variables exceeding their limits. However the high computational demands are the main challenge for this method applying for the real applications.
Model Predictive Control for path tracking and obstacle avoidance of autonomous vehicle
The concept of autonomous vehicles has been widely explored lately by, among others, automotive companies as a way to for example improve fuel efficiency or to gain access to environments which pose a danger to human operators. Model Predictive Control (MPC) has traditionally been used to control systems with slower dynamics but with the emergence of more powerful computers it is now being used in systems with considerably faster dynamics as well. One of the main strengths of MPC is its ability to handle constraints which are present in all physical systems. The aim of this thesis was to develop a single layer linear controller for path tracking and obstacle avoidance of an autonomous car. Its ability to minimize the deviations to the reference path while clearing static obstacles was evaluated. Focus was placed on the tracking problem hence no trajectory planning system was implemented. Instead a predefined path was used. Simulations were developed in MATLAB based on the kinematic ...