Vehicle chassis coordinated control strategy based on model predictive control method

Modular Multilevel Converter (MMC) has the characteristics of high voltage level and low switching frequency. The traditional modular multilevel converter circulating current control strategy mostly adopts the PI control principle, and the parameter setting is complicated and the accuracy is not high, and the control process is more difficult. Model predictive control strategy is the optimal control method based on the model in the existing time domain. This paper proposes a Model Predictive Control (MPC) method based on carrier phase-shifted pulse width modulation (PSC-PWM) to suppress the circulating current, and output the optimal modulation wave through model prediction. Compared with the traditional control strategy, this strategy is simple to implement, does not require complex tuning calculations, and combines with the traditional capacitor voltage equalization strategy to obtain the output modulation wave. A 7-level MMC simulation control system is built in MATLAB / SIMLINK to verify the theory, comparing with existing control methods, it can be concluded that the proposed method has high calculation efficiency, good control accuracy and strong robustness.

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Model Predictive Control-based Coordinated Control Strategy for VSC-MTDC Systems

2019 22nd International Conference on Electrical Machines and Systems (ICEMS) ◽

10.1109/icems.2019.8921884 ◽

2019 ◽

Author(s):

Guanyu Zhou ◽

Wenzhong Ma

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Control Strategy ◽

Coordinated Control

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Extended Kalman filter–based and model predictive control–based dynamic coordinated control strategy for power-split hybrid electric bus

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407019891750 ◽

2019 ◽

Vol 234 (6) ◽

pp. 1623-1633

Author(s):

Xiaohua Zeng ◽

Tong Liu ◽

Dafeng Song ◽

Nannan Yang ◽

Haoyong Cui

Keyword(s):

Kalman Filter ◽

Model Predictive Control ◽

Extended Kalman Filter ◽

Predictive Control ◽

Control Strategy ◽

Hybrid Electric Vehicle ◽

Coordinated Control ◽

Power Split ◽

Hybrid Electric ◽

Electric Mode

The power-split hybrid electric vehicle achieves excellent fuel economy because both the engine speed and the torque of this system are decoupled from the road load. However, for a power-split hybrid electric vehicle with multiple power sources, the inconsistency of the response characteristic of each power source seriously affects the stability control of the power system and riding comfort, so the coordinated control of the power system is particularly important. This article proposed a dynamic coordinated control strategy. First, extended Kalman filter is applied to realize robust online estimation of the engine dynamics. Then, an extended Kalman filter–based and model predictive control–based dynamic coordinated control strategy is designed to achieve accurate reference tracking in hybrid electric mode. Considering the real-time performance for the online application of the dynamic coordinated control strategy, a fast model predictive control solver is formed based on a reasonable assumption. Offline simulation results show that accurate reference tracking is achieved in hybrid electric mode. Hardware-in-the-loop simulation is also conducted to validate the real-time performance of the proposed dynamic coordinated control strategy. This study is expected to improve the performance and robustness of the dynamic coordinated control strategy in hybrid electric mode while reducing the calibration load.

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Overtaking control strategy based on model predictive control with varying horizon for unmanned ground vehicle

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407020947515 ◽

2020 ◽

Vol 235 (1) ◽

pp. 78-92

Author(s):

Chaofang Hu ◽

Lingxue Zhao

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Control Strategy ◽

Control Method ◽

Pseudospectral Method ◽

Unmanned Ground Vehicle ◽

Tracking Accuracy ◽

Ground Vehicle ◽

Smooth Path ◽

The Difference

In this paper, a synthesized novel strategy of varying predictive horizon-based model predictive control is proposed for the overtaking control of unmanned ground vehicle. The whole control strategy includes path planning and path tracking. First, the preferred path in presence of diverse constraints of states, inputs, and collision avoidance can be calculated using Gauss pseudospectral method where expected position, velocity, and attitude are provided. Correspondingly, the continuous optimal control problem is converted to discrete nonlinear programming. Second, model predictive control is developed for tracking the optimized path. Considering the effect of the predictive horizon and the Gauss points’ distribution on tracking performance, the varying predictive horizon is introduced to improve the tracking accuracy in non-smooth path. By the varying predictive horizon-based model predictive control method, less computation burden and better control performance are achieved. For the difference between the mathematical expressions and the real unmanned ground vehicle dynamics, genetic algorithm is utilized to identify the parameters of tire model. Simulations in MATLAB and CarSim are both implemented to illustrate the effectiveness of the proposed method.

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