Adaptive cruise control with stop&amp;go function using the state-dependent nonlinear model predictive control approach

Shakouri, Payman; Ordys, Andrzej; Askari, Mohamad R.

doi:10.1016/j.isatra.2012.05.001

Cited by 59 publications

(28 citation statements)

References 11 publications

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“…The switching rules ( [3], [5]) for transition between CC and ACC modes are illustrated in Table 1.. If the ultrasonic sensor of the follower detects a slower moving robot in front, the controller adjusts the velocity to maintain the clearance inter-distance (desired distance).…”

Section: Control Designmentioning

confidence: 99%

“…This output vector y is decoupled into three separate outputs y 1 , y 2 and y 3 . For each, the state-space equations of the system can be extended by taking the integral action into consideration to obtain the incremental model as follows [5], [9]: …”

Section: Model Predictive Controlmentioning

confidence: 99%

“…Three F i=1,2,3 and φ i=1,2,3 need to be calculated to design the MPC. F and φ are general matrices in predictive control and they can be found in literature [5], [9].…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…A comprehensive study on the ACC was carried out by [2]. Several model-based control approaches and architectures have been suggested and developed for designing ACC systems from the classical to the nonlinear advanced control methods, such as PID control and linear quadratic control (LQC) taking the gain scheduling approach [3][4], model predictive control (MPC) and nonlinear MPC (NMPC) [5][6], and the sliding mode controller [7] among others. The practical implementation of the ACC system using the three control methodologies, MPC, fuzzy PID, and conventional PID control, in the application to the real-time single board computer based robotic vehicle is presented in this paper.…”

mentioning

confidence: 99%

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Robotic Implementation of the Adaptive Cruise Control-Comparison of Three Control Methods

2014

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Abstract. This paper explores the practical implementation of the Adaptive Cruise Control (ACC) system on a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit). The ACC algorithm based on three control methodologies, the fuzzy PID control, model predictive control (MPC) and conventional PID control, is deployed on a field programmable gate array (FPGA), included in the robot's architecture. The results are compared both in the simulation and using the real robot. The comparison of the performance demonstrates a good correlation between theory and real implementation, whilst highlighting problems introduced by a real system. IntroductionAdaptive cruise control (ACC) system, cruise control (CC) system and emergency stop are the more common longitudinal applications, which have been studied in the field of the robotics and intelligent transportation system (ITS) [1]. The ACC system is an extension of the cruise control system, which not only controls the velocity of the vehicle but also is it capable of controlling the distance between the leader and follower vehicles to retain a safe distance. A comprehensive study on the ACC was carried out by [2]. Several model-based control approaches and architectures have been suggested and developed for designing ACC systems from the classical to the nonlinear advanced control methods, such as PID control and linear quadratic control (LQC) taking the gain scheduling approach [3][4], model predictive control (MPC) and nonlinear MPC (NMPC) [5][6], and the sliding mode controller [7] among others. The practical implementation of the ACC system using the three control methodologies, MPC, fuzzy PID, and conventional PID control, in the application to the real-time single board computer based robotic vehicle is presented in this paper. Furthermore, the results of those controllers are compared both in the simulation and using the real robot. The paper is organized as follows: Section 2 describes the hardware and the associated system transfer function. Section 3 describes the ACC

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Section: Control Designmentioning

confidence: 99%

Section: Model Predictive Controlmentioning

confidence: 99%

“…Three F i=1,2,3 and φ i=1,2,3 need to be calculated to design the MPC. F and φ are general matrices in predictive control and they can be found in literature [5], [9].…”

Section: Model Predictive Controlmentioning

confidence: 99%

mentioning

confidence: 99%

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Robotic Implementation of the Adaptive Cruise Control-Comparison of Three Control Methods

2014

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“…Many researchers calculated it by multiplying the speed of vehicle and time-constant [1]. Some researchers suggested to add anti-collision constant to the equation [2]- [4]. Deng and Yingping defined the second degree equation of the safety distance according to the speed using fitting function [5].…”

Section: Introductionmentioning

confidence: 99%

Advanced Smart Cruise Control with Safety Distance Considered Road Friction Coefficient

Hong¹,

Park²,

Yoo³

et al. 2016

IJCTE

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Abstract-This research suggests the velocity controller with advanced smart cruise control (ASCC) with stop & go control considering the surface friction coefficient and applying a safety distance. A safety distance affects quality of cruise control. If a safety distance is too short, the probability to cause a collision gets greater whereas if it is too long, it can cause traffic congestion. Thus, calculating the optimal safety distance is very important. In this study, we used fitting functions to obtain a basic safety distance and then added the μ-safety distance, considering friction coefficient and relative velocity, to calculate final safety distance. We invented ASCC and stop & go control by considering velocity of vehicle to maintain the safety distance and relative velocity at the same time. Carsim was used for simulation and we found that while a vehicle with the velocity controller, calculating the velocity and location difference between preceding and following vehicles with four friction coefficients, it keeps a safety distance.

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Survey on Control Schemes for Automated Driving on Highways

Rupp

Stolz

2016

Automated Driving

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Adaptive cruise control with stop&go function using the state-dependent nonlinear model predictive control approach

Cited by 59 publications

References 11 publications

Robotic Implementation of the Adaptive Cruise Control-Comparison of Three Control Methods

Robotic Implementation of the Adaptive Cruise Control-Comparison of Three Control Methods

Advanced Smart Cruise Control with Safety Distance Considered Road Friction Coefficient

Survey on Control Schemes for Automated Driving on Highways

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