Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

Abstract

This work is concerned with Model Predictive Control (MPC) algorithm for vehicle obstacle avoidance. The second objective of the algorithm is on-line minimization of fuel utilization. At first, the rudimentary nonlinear MPC optimization problem is formulated. Next, the constraints related to the predicted process state variables are formulated as soft ones to guarantee computational safety. Furthermore, in order to obtain a computationally simple procedure, the process dynamics and the fuel utilization model are linearized on-line and used for prediction in MPC. It leads to a quadratic programming MPC task, the necessity of nonlinear optimization performed in real-time is eliminated. In order to stress advantages of the discussed computationally uncomplicated MPC method it is compared with the basic scheme with on-line nonlinear optimization in terms of control quality and computational time. Additionally, effectiveness of the MPC algorithm is discussed in presence of modeling errors and measurement noise. Finally, additional constraints imposed on the rate of change of the manipulated variables are considered.

Obstacle Avoidance during Teleoperation by Model Predictive Control with Time-varying Delay

“…The problem setup is for the vehicles to move from the initial state to the final state without colliding with each other, while simultaneously avoiding other stationary and moving obstacles. A method to avoid obstacles by giving constraints to the MPC has also been proposed (12)∼ (14) . However, these methods incorporate the prohibited area into the constraint, and if the plant moves into the prohibited area, it may become unstable.…”

“…u ob is the optimized control input required to avoid the detected obstacle. The reference trajectory X re f and U re f is defined as ( 13), (14).…”

A Learning-and-Tube-Based Robust Model Predictive Control Strategy for Plug-In Hybrid Electric Vehicle

“…Additionally, as the length of the predictive horizon extends, the accumulative error in state observation is amplified, potentially leading to suboptimal control effects or even detrimental control performance. Aiming at simplifying the construction and solving of state functions, quadratic programming (QP) [36,37], commonly used in MPC solvers, requires linearization through the Taylor expansion method [38], which consequently degrades the accuracy of state observation further. To essentially enhance the state observing ability for the built-in state predictive model, combination-oriented methods integrating offline state observation model training and online data prediction, in which data-driven MPCs [39,40] are one of the most preferred solutions.…”

Battery Energy Consumption Analysis of Automated Vehicles Based on MPC Trajectory Tracking Control

“…It can predict the future output of the system according to the historical information of the controlled object and future input. Incorporating MPC in vehicle obstacle avoidance and steering control can improve obstacle avoidance efficiency [25,26]. The vehicle is driven safely at high speed by building a coupled nonlinear tire model [27] and adding sigmoid safety constraints to the MPC controller [28].…”