Weight Changing Model Predictive Controller for Adaptive Cruise Control with Multiple Objectives

2018 ◽  
Author(s):  
Usman Munir ◽  
Zhang Junzhi
Keyword(s):  
Adaptive Cruise Control ◽  
Multiple Objectives ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Predictive Controller

Multi-objective adaptive cruise controller design using nonlinear predictive controller with the objective function with variable weights determined by fuzzy logic controller

2021 ◽  
pp. 095440702110147
Author(s):  
Behzad Samani ◽  
Amir H. Shamekhi
Keyword(s):  
Objective Function ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Hierarchical Control ◽  
Control Input ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Control Step ◽  
Predictive Controller

In this paper, an adaptive cruise control system with a hierarchical control structure is designed. The upper-level controller is a model predictive controller (MPC) that by minimizing an objective function in the presence of the constraints, calculates the desired acceleration as control input and sends it to the lower-level controller. So the lower-level controller, which is a fuzzy controller, determines the amount of throttle valve opening or brake pressure to get the car to this desired acceleration. The model predictive controller performs optimization at each control step to minimize the objective function and achieve the reference values. Usually, the objective function has predetermined and constant weights to meet objectives such as maintain the driver’s desired speed and increase safety and in some cases increase comfort and reduce fuel consumption. In this paper, it is suggested that instead of using constant weights in the objective function, these weights should be determined by a fuzzy controller, depending on the different conditions in which the car is placed. The simulation results show that the variability of the weights of the objective function achieves control objectives much better than the optimization of the objective function with constant weights.

Online Hybrid Model Predictive Controller Design for Cruise Control of Automobiles

2017 ◽  
Author(s):  
Kaveh Merat ◽  
Jafar Abbaszadeh Chekan ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Hybrid Model ◽  
Optimization Problem ◽  
Controller Design ◽  
Research Work ◽  
Mixed Integer ◽  
Control Procedure ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Gear Shift ◽  
Predictive Controller

In the proposed study, a Hybrid Model Predictive Controller is introduced for cruise control of an automobile model. The presented model consists of the engine, the gearbox, and the transmission dynamics, where the aerodynamics force and elastic friction between the tires and road are taken into account. Through Piecewise Linearization of nonlinearities in the system; (torque)-(throttle)-(angular velocity) of engine and (aerodynamic drag force)-(automobile velocity), a comprehensive piecewise linear model for the system is obtained. Then combined with the switch and shift between engaged gears in gearbox, the Piecewise Affine (PWA) model for the vehicle dynamics is acquired. As far as the control design is concerned, the cruise control problem for tracking a desired speed fashion is addressed by a MPC-based controller design. The proposed control approach is based on the online model predictive control, applied on the obtained PWA dynamics. The highlighted novelties of the presented research work are summarized as: first a more complete model is examined due to the consideration of a realistic model for engine. This improvement makes the polyhedron regions of the PWA system dependent to both state variable (i.e., velocity) and input signals (i.e., throttle and engaged gear) which brings the complexity to the design of control procedure. Second, due to the switch in the dynamics and dependence of our PWA model to discrete input (gear shift), the desperate need to solve the optimization problem through mixed integer programming, which needs high computation effort specially for our system, seems inevitable. We triumph over this challenge through introducing “possible gear shift scenario” sets. Hence, by constraining the optimization problem to the introduced logical sets, the problem still remains convex optimization type and the computation volume is reduced. In addition, we hired branch and bound method which allowed us to have large problems to be solved in a tractable amount of time and computation resources. At last, some simulations are presented to exhibit the performance of the proposed method.

scholarly journals A New Hybrid Model Predictive Controller Design for Adaptive Cruise of Autonomous Electric Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Yuanhang Chen ◽  
Guodong Feng ◽  
Shaofang Wu ◽  
Xiaojun Tan
Keyword(s):  
Hybrid Model ◽  
Controller Design ◽  
Autonomous Driving ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Energy Feedback ◽  
Lateral Dynamics ◽  
Computation Efficiency ◽  
Predictive Controller ◽  
Autonomous Electric Vehicles

Autonomous driving is an appealing research topic for integrating advanced intelligent algorithms to transform automotive industries and human commuting. This paper focuses on a hybrid model predictive controller (MPC) design for an adaptive cruise. The driving modes are divided into following and cruising, and the MPC algorithm based on simplified dual neural network (SDNN) and proportional-integral-derivative (PID) based on single neuron (SN) are applied to the following mode and the cruising mode, respectively. SDNN is used to accelerate the solution of the quadratic programming (QP) problem of the proposed MPC algorithm to improve the computation efficiency, while PID based on SN performs well in the nonlinear and time-varying conditions in the ACC system. Moreover, lateral dynamics control is integrated into the designed system to fulfill cruise control in the curved road conditions. Furthermore, to improve the energy efficiency of the electric vehicle, an energy feedback strategy is proposed. The simulation results show that the proposed ACC system is effective on both straight roads and curved roads.

scholarly journals Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle Considering Weight Adjustment

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1516
Author(s):  
Sheng Zhang ◽  
Xiangtao Zhuan
Keyword(s):  
Objective Function ◽  
Adaptive Cruise Control ◽  
Multiple Objectives ◽  
Weight Matrix ◽  
Tracking Performance ◽  
Cruise Control ◽  
Constant Weight ◽  
Car Following ◽  
Energy Economy ◽  
Driving Conditions

This paper studies control strategies for adaptive cruise control (ACC) systems in battery electric vehicles (BEVs). A hierarchical control structure is adopted for the ACC system, and the structure contains an upper controller and a lower controller. This paper focuses on the upper controller. In the upper controller, model predictive control (MPC) is applied for optimizing multiple objectives in the car-following process. In addition, multiple objectives, including safety, tracking, comfort, and energy economy, can be transformed into a symmetric objective function with constraints in MPC. In the objective function, the corresponding weight matrix for the optimization of multiple objectives is implemented in symmetric form to reduce the computational complexity. The weights in the weight matrix are usually set to be constant. However, the motion states of the own vehicle and the front vehicle change with respect to time during a car-following process, resulting in variation of the driving conditions. MPCs with constant weights do not adapt well to changes in driving conditions, which limits the performance of the ACC system. Therefore, a strategy for weight adjustment is proposed in order to improve the tracking performance, in which some weights in MPC can be adjusted according to the relative velocity of two vehicles in real time. The simulation experiments are carried out to demonstrate the effectiveness of the strategy for weight adjustment. Based on achieving the other control objectives, the ACC system with the weight adjustment has better tracking performance than the ACC system with the constant weight. While the tracking is improved, the energy economy is also improved.

scholarly journals Model-based Predictive Control implementation for Cooperative Adaptive Cruise Control

2018 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
António Lopes ◽  
Rui Esteves Araújo
Keyword(s):  
Adaptive Cruise Control ◽  
Longitudinal Velocity ◽  
System Stability ◽  
Control Input ◽  
Vehicular Communication ◽  
Cruise Control ◽  
Model Based ◽  
Cooperative Adaptive Cruise Control ◽  
Vehicle Platoon ◽  
Predictive Controller

The automation of road vehicles has become a necessity to improve the efficiency and safety of this system. In a vehicle formation it is important to maintain a safety distance between the vehicles. The control of a vehicle spacing distance and longitudinal velocity can be achieved through the implementation of a model-based predictive controller. This implementation of a cooperative adaptive cruise control allows the access of another vehicle state information through vehicular communication technology and promote state prediction and ultimately system stability. The optimization algorithm performs the computation of the control input in a control horizon window and ensures that the spacing error takes only positive values. The results of the proposed controller are evaluated through the computational tool Simulink in the two-vehicle platoon. The controller is implemented in the precedent vehicle. To assess the performance of the proposed controller different control parameters and constraints were used.

Sign in / Sign up

Export Citation Format

Share Document