Real-time weighted multi-objective model predictive controller for adaptive cruise control systems

Zhao, Rongchen; Wong, Pak Kin; Xie, Zhengchao; Zhao, Jing

doi:10.1007/s12239-017-0028-2

Cited by 71 publications

(40 citation statements)

References 24 publications

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“…However, the traditional model predictive control algorithm with a constant weight matrix will probably not be able to improve the overall performance in transient scenarios [13]. Zhao et al [13] pointed out that the traditional model predictive control algorithm with a constant weight matrix cannot meet the improvement requirement in fuel economy and ride comfort, and accordingly proposed a real-time weight tuning strategy to solve time-varying multi-objective control problems, where the weight matrix can be adjusted with respect to different operating conditions.…”

Section: Modeling Of Integrated Car-following Systemmentioning

confidence: 99%

“…Zhao et al [13] pointed out that the traditional model predictive control algorithm with a constant weight matrix cannot meet the improvement requirement in fuel economy and ride comfort, and accordingly proposed a real-time weight tuning strategy to solve time-varying multi-objective control problems, where the weight matrix can be adjusted with respect to different operating conditions. In addition, when the weight coefficient for each sub-objective is constant, it is easy to cause the distribution of solutions to be more concentrated, and accordingly it will be difficult to achieve the global optimum [14].…”

Section: Introductionmentioning

confidence: 99%

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Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy

Zhang

Chen

2018

Applied Sciences

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This paper presents a novel multi-objective coordinated adaptive cruise control (ACC) algorithm based on a model predictive control (MPC) framework which can comprehensively address issues regarding longitudinal car-following performance, lateral stability, as well as vehicle safety. During the car-following, vehicle dynamics, illustrating the forces acting on the tire contact patches, are established. To simplify the tightly coupled dynamics system, a state-feedback based disturbance decoupling method is employed, by which longitudinal and lateral dynamics can be completely decoupled. Furthermore, the traditional MPC control with a constant weight matrix will probably not be able to solve time-varying multi-objective coordinated optimization issues, especially in transient scenarios. A weight coefficient self-tuning strategy is therefore suggested by which the weight coefficient for each sub-objective can be adjusted automatically with the change of traffic scenarios, accordingly improving the overall car-following performance. The simulations show that the control algorithm utilizing the suggested self-tuning strategy reaps significant benefits in terms of longitudinal car-following performance, while at the same time maintaining a small lateral stability error range.

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Section: Modeling Of Integrated Car-following Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy

Zhang

Chen

2018

Applied Sciences

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“…Most of these control problems are formulated in terms of a single objective function and one common method to form a single objective is to weight the aforesaid objectives . In general, the weights need to be tuned and even to be adjusted at each sampling time for desired responses . In the context of autonomous vehicles, multiobjective predictive cruise control (MOPCC) is required to satisfy varying traffic scenarios on challenging road conditions, that is, the weights should change according to the active scenario.…”

Section: Introductionmentioning

confidence: 99%

“…25 In general, the weights need to be tuned and even to be adjusted at each sampling time for desired responses. 26,27 In the context of autonomous vehicles, multiobjective predictive cruise control (MOPCC) is required to satisfy varying traffic scenarios on challenging road conditions, that is, the weights should change according to the active scenario. To overcome the difficulties in weight selection, He et al 28 proposed a Utopia tracking scheme that minimizes the distance of a set of stage cost functions to its Utopia point.…”

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confidence: 99%

Multiobjective predictive cruise control for connected vehicle systems on urban conditions with InPA‐SQP

Shi

et al. 2019

Optim Control Appl Methods

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Summary The connected vehicle (CV) system is one of the most effective core technologies in intelligent transportation systems. In order to solve the optimal velocity prediction problem for a CV system on urban roads, a multiobjective predictive cruise controller (MOPCC) for vehicles in the CV system is proposed to coordinate multiple performances including safety, tracking ability, ride comfort, and fuel economy. Firstly, with the ad hoc wireless communication technology, the signal phase and timing information is obtained to calculate the feasible velocity range for improving mobility. Then, the optimal target velocity of vehicles is computed by minimizing the fuel economic polynomial models of the vehicle system. Secondly, in order to systematically cope with those multiple performances, the Utopia point method is applied to change the multiobjective optimization problem into Utopia tracking problem. Furthermore, the MOPCC problem is formulated and solved by a fast numerical algorithm, ie, integrated perturbation analysis and sequential quadratic programming. Finally, simulations are presented to demonstrate the effectiveness of the proposed method in terms of improved the multiple performances.

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“…Currently, a number of features are added due to the revolution of recent technologies e.g., all the control operations such as the speed control, the attainment of last run speed, deactivate the speed after brakes etc. by pressing the buttons [1]. Moreover these features are common in automotive vehicles.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of PID Controller for an Automobile Cruise Control System using Ant Lion Optimizer

Pradhan¹,

Majhi²,

Pradhan³

et al. 2017

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Abstract. This paper considers the design and performance evaluation of PID controller for an automobile cruise control system (ACCS). A linearized model of the cruise control system has been studied as per the dominant characteristics in closed loop system. The design problem is recast into an optimization problem which is solved using Ant Lion Optimization (ALO). The transient performance of proposed ACCS i.e., settling time, rise time, maximum overshot, peak time and steady state error are investigated by step input response and root locus analysis. To show the efficacy of the proposed algorithm over a state space method, classical PID, fuzzy logic, genetic algorithm, a comparison study is presented by using MATLAB/SIMULINK. Furthermore, the robustness of the system is evaluated by using bode analysis, sensitivity, complimentary sensitivity and controller sensitivity. The results indicate that the designed ALO based PID controller for ACCS achieves better performance than other recent methods reported in the literature.

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Real-time weighted multi-objective model predictive controller for adaptive cruise control systems

Cited by 71 publications

References 24 publications

Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy

Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy

Multiobjective predictive cruise control for connected vehicle systems on urban conditions with InPA‐SQP

Performance Evaluation of PID Controller for an Automobile Cruise Control System using Ant Lion Optimizer

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