Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles

Abstract

This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of control inputs. The objective function of the optimal control problem is to follow the planned path which is represented by a Bézier curve. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better than the traditional circular disk. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the optimization problem. Driving corridors are assumed to be also Bézier curves, which could be obtained from digital maps, and are reformulated to suit the controller algorithm. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.

Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms

“…Unfortunately, the optimization problem (1)-(4) with the prediction (18), based on a nonlinear model, is, in general, a non-convex nonlinear optimization problem; see, e.g., [14][15][16]. Thus, it is a hard-to-solve, time-consuming computational task, and there is no guarantee of finding a global solution, while the time needed to obtain the solution cannot be foreseen in advance.…”

The Model Order Reduction Method as an Effective Way to Implement GPC Controller for Multidimensional Objects

“…max_l defines the maximum number of coefficients for the DV inputs (model order), nDV defines number of DV inputs. The vector of the past values of DVs is given by (9), where dv1 to nDV define the index of DV inputs, delay dv1 to delay nDV defines delay values for each of the DV inputs.…”

“…Furthermore, in MPC, unlike PID control, all necessary constraints may be incorporated systematically in the control law. Additionally, MPC can be applied to nonlinear systems [8][9][10] using the structure of fuzzy models [11][12][13][14][15][16][17] or online linearization of the process model at each MPC algorithm iteration [18,19]. In large-scale industrial applications, MPC algorithms are implemented in Distributed Control Systems (DCS) using Programmable Logic Controllers (PLC) [20] or specialized industrial controllers [21].…”

Use of the Bezier Curves for a Vehicles Driving Cycles' Modeling

“…Analysis of the previously conducted researches, connected with the research and modeling of a driving cycle and concerning the energy consumption by vehicles trajectory [25][26], in the automatized vehicles control systems [27][28], modeling and prediction of the route of vehicle movement and avoidance of collision for pilotless vehicles [29].…”