Design of a speed tracking controller for heavy-duty vehicles with an all-speed governor based on a model predictive control strategy

Jiang, Danna; Huang, Ying; Li, Gang; Hao, Donghao; Zuo, Zhe

doi:10.1177/1468087416688804

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…In Figures 11-13 the legends of fuel cons and nox cons denote the weight factors of u = 0 and u = 1, which are the black solid line and red dotted line, respectively. Logic is the logicbased control scheme in equations (20) and (21), which corresponds to the green solid line. Figure 11 mainly shows the vehicle velocity, fuel consumption and LNT tailpipe NO x emission versus time.…”

Section: Simulation Discussionmentioning

confidence: 99%

“…For performance comparison, a logic-based control scheme is also designed. In particular, the logic rules of the gear number and additional AFR are provided in equations (20) and (21), respectively. Gear number i g depends on the vehicle speed, which is set based on the empirical driver behavior to make the engine work in the efficient region for fuel economy.…”

Section: Simulation Setupmentioning

confidence: 99%

“…For example, the model predictive control (MPC) algorithm has the superiority of solving the optimal control problem to drive an online feedback solution if the optimization problem is continuous. 19,20,21 The implementation of a scheduling map is an alternative method. This method can achieve optimal performance and be downloaded into an electronic control unit (ECU) as maps and applied online.…”

Section: Introductionmentioning

confidence: 99%

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Route-dependent optimal control of the after-treatment system of diesel engines

Matsunaga

Kato

et al. 2019

International Journal of Engine Research

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In this article, the optimal control problem for nitrogen oxide emission reduction is investigated for diesel engines with a lean nitrogen oxide trap. First, a control-oriented model is developed based on conservation laws. Then, the optimal control problem is formulated as a multistage decision problem and solved using a dynamic programming algorithm under dynamical model constraints. A trade-off between fuel economy and nitrogen oxide emission is considered in the cost function of optimization. To demonstrate the obtained optimal control scheme, the parameters of the lean nitrogen oxide trap model are identified with data obtained from a GT-power-based diesel engine simulator. The numerical simulation results for two standard driving cycles and a stochastically generated driving cycle in comparison to a conventional logic-based control scheme are provided using the identified model in the MATLAB/Simulink platform.

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Section: Simulation Discussionmentioning

confidence: 99%

Section: Simulation Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Route-dependent optimal control of the after-treatment system of diesel engines

Matsunaga

Kato

et al. 2019

International Journal of Engine Research

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“…Besides, it should be pointed out that, in practical, the engine speed could only be measured when the teeth rim goes through the installed sensors. Moreover, the mean engine speed calculated by tooth-to-tooth engine speed within a specified segment of CA (2π/3 rad CA for a 4-stroke 6-cylinder engine) is employed as the input for controller, which contributes extra delay in the close-loop control system [40]. This issue has not been considered in previous works concerning the applications of ADRC in diesel engines.…”

Section: Introductionmentioning

confidence: 99%

Variable Sampling Rate based Active Disturbance Control for a Marine Diesel Engine

Wang

Liu

et al. 2019

Electronics

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In this paper, in order to handle the high nonlinearity and the sophisticated disturbance in marine engines, a variable sampling rate based active disturbance rejection controller is developed for engine speed control. In the proposed method, the Active Disturbance Rejection Control (ADRC) is designed with the consideration of the practical application in engine speed control that is known as the Crank-angle (CA) based or event-based sampling and control, which means the sampling interval varies with the engine speed. Such a problem has not been discussed in any previous study regarding the application of ADRC in engine control. To this end, this paper discusses the convergence of the variable sampling rate based Extended State Observer (ESO), as well as its parameters that guarantee stability. To verify the proposed control scheme more properly, a cycle-detailed hybrid nonlinear engine model is employed. Finally, simulations are carried out on the Hardware-in-the-loop (HIL) system to assess the superiority of the proposed strategy. The comparative results with a Fuzzy-Proportional-Integral-Derivative (PID) controller demonstrate that the proposed control scheme has better adaptation to engine speed, load disturbances, and stronger robustness towards model uncertainties, which indicates a promising reduction of time and burden for calibrating the controller. It also proved that the proposed CA based ADRC by variable sampling rate method outperforms the general fixed sampling rate ADRC, which is widely used in previous works. Moreover, the successful application of the proposed algorithm via CA based strategy in a real Engine Control Unit (ECU) indicates its huge potential in practical engine control.

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“…On one hand, the identification or calibration technique, such as the recursive least squares method 3 and the dynamic identification combined with the static function fitting technique, 4 is adopted to obtain the mathematical model of the engine speed system. Based on the model of the certain system, some promising control strategies, such as model predictive control, 5–9 Lyapunov-based feedback control, 10,11 are adopted to guarantee the stability and convergence of speed tracking system. On the other hand, some special control technologies are utilized to cope with the uncertain parameters under certain conditions.…”

Section: Introductionmentioning

confidence: 99%

Cascade active disturbance rejection control–based double closed-loop speed tracking control for automotive engine

Feng

Jiao

Zhong

2019

International Journal of Engine Research

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To improve tracking performance of engine speed in the face of nonlinearity and time-varying uncertainty, this article investigates the double closed-loop cascade active disturbance rejection control strategy for automotive engine control system. In this cascade control arrangement, the outer active disturbance rejection speed controller with the extended state observer for the speed error and its integral, and disturbance from load torque and time-varying uncertainty, drives the set-point of the inner loop to keep the engine speed to its set-point; meanwhile, the inner active disturbance rejection pressure controller with the extended state observer for the pressure error and its integral, and disturbance from the air mass flow rate leaving the intake manifold and the pumping fluctuation of air charge, manages the throttle valve to match the pressure with the set-point requested by the outer active disturbance rejection speed controller. The observer gains and controller gains of active disturbance rejection speed controller and active disturbance rejection pressure controller are determined by the linear matrix inequalities ensuring the stability and disturbance attenuation level of the closed-loop system. The effectiveness is validated by implementing the proposed strategy and a series of related control schemes in the simulator of a real V6 engine.

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Design of a speed tracking controller for heavy-duty vehicles with an all-speed governor based on a model predictive control strategy

Cited by 11 publications

References 14 publications

Route-dependent optimal control of the after-treatment system of diesel engines

Route-dependent optimal control of the after-treatment system of diesel engines

Variable Sampling Rate based Active Disturbance Control for a Marine Diesel Engine

Cascade active disturbance rejection control–based double closed-loop speed tracking control for automotive engine

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