• International Journal of Technology (IJTech)
  • Vol 11, No 7 (2020)

Neural Network Predictive Control Approach Design for Adaptive Cruise Control

Neural Network Predictive Control Approach Design for Adaptive Cruise Control

Title: Neural Network Predictive Control Approach Design for Adaptive Cruise Control
Pratama Mahadika, Aries Subiantoro, Benyamin Kusumoputro

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Cite this article as:
Mahadika, P., Subiantoro, A., Kusumoputro, B., 2020. Neural Network Predictive Control Approach Design for Adaptive Cruise Control. International Journal of Technology. Volume 11(7), pp. 1451-1462

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Pratama Mahadika Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Aries Subiantoro Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Benyamin Kusumoputro Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
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Abstract
Neural Network Predictive Control Approach Design for Adaptive Cruise Control

As one part of the advanced driver assistance systems (ADAS), adaptive cruise control (ACC) is introduced to reduce the possibility of traffic accidents by controlling the throttle and the pressure on the brakes to maintain a safe distance from the vehicle in front. Generally, linearized model-based controllers are used in the ACC. In this paper, a new approach to ACC’s inner loop is developed by designing the controller using neural network predictive control (NNPC) which integrates the capability of artificial neural networks (ANN) to imitate vehicle characteristics and model predictive control (MPC) to obtain the minimized quadratic error between future reference trajectories and predicted outputs. Two separate control loops will be used: an outer loop based on a decision algorithm, and the PI controller, which will give the inner loop a speed reference to maintain the safe distance from the vehicle in front. NNPC is used in the inner loop to manipulate throttle and brake pressure on the brakes in order to control the speed of the following vehicle. Simulations will be carried out using software-in-the-loop (SIL) between CarSim and Simulink. The ANN model is identified and verified to mimic the nonlinearity behavior of the vehicle model using the mean square error (MSE) parameter. The results of this study are that the ANN model is able to imitate the vehicle dynamic with MSE equal to 0.0095, and the controller can maintain a safe distance while having a smooth response.

Adaptive cruise control; Artificial neural network; Dynamic vehicle model; Neural network predictive control

Introduction

In recent years, trends in improving driving safety have become an important concern for the automotive industry because traffic accidents are major concerns faced by drivers. These problems can be avoided by introducing some forms of driver assistance to prevent accidents. In fact, 50% of accidents that occur are rear-end collisions. That’s why advanced driver assistance systems (ADAS) are developed by automobile manufacturers to make driving safer. The National Transportation Safety Board said that active safety systems are 50% more effective in reducing death rates in accidents compared to passive safety systems such as airbags (ACEA, 2018). As part of the ADAS system, active cruise control (ACC) was developed in early 1990. The ACC system is capable of adjusting vehicle speed while maintaining a safe distance from the vehicle in front. This system modulates the throttle valve  and  brake  pressure to  reduce  or  accelerate the  vehicle to   the desired   speed and distance. Radar, laser, and other sensory devices are used to measure the distance from vehicles in front, so the ACC system can choose a proper driving mode.

Several methods for the ACC system have been developed in four-wheeled vehicles. Classical methods such as PID and fuzzy have been developed since a few decades ago. Rout and his colleagues utilized a PID controller that was optimized with genetic algorithm (GA) to produce optimal PID tuning (Rout et al., 2016). Shakouri developed the ACC system with the gain-scheduling method using PI and LQ controllers to manipulate throttle valve and brake pressure (Shakouri et al., 2011), and Pananurak and his colleagues used fuzzy logic algorithms for ACC systems (Pananurak et al., 2009). Some predictive methods began to be developed because they resulted in better control of vehicle dynamics. Shakouri also developed the two-loop ACC system by utilizing the model predictive control (MPC) for throttle and brake control as inner loop control and PI as a speed controller for outer-loop control (Shakouri and Ordys, 2014). After that, Naus and his colleagues utilized implicit MPC and multi-parametric quadratic programs for online identification of ACC systems (Naus et al., 2010). Miftakhudin and colleagues developed a multistage MPC system with constraints for the ACC controller to achieve a smooth response (Miftakhudin et al., 2019). On problem shared by all the research mentioned above, is that the controller uses the linearization method in modeling the longitudinal motion of four-wheeled vehicles. These methods limit the controller’s ability to work only in a specified range and are difficult to obtain for a large working range.

From many control methods that have been developed, artificial neural network (ANN) has not been widely applied in automotive controllers, especially in ACC systems, even though ANN is widely known for its ability to capture nonlinear phenomena. For that reason, the main contribution of this work is to make a controller that integrates the ability of ANN to capture nonlinear dynamics of moving vehicles and the predictive ability of MPC to control ACC systems. This method, called neural network predictive control (NNPC), began development 1996 when Soloway started working on neural generalized predictive control that combines ANN for model identification and generalized predictive control (GPC) for the controller (Soloway and Haley, 1996). These methods created a new problem for minimization of the cost function in GPC. Newton-Raphson was used to compute optimization problems numerically to obtain optimal control sequences for the controller. This paper used a slightly modified method, quasi-Newton, to compute the control sequence for the controller and Broyden Fletcher Goldfarb Shanno (BFGS) algorithm to solve inverse Hessian matrices that appear in Newton-based optimization. In this research, simulation will be carried out in software-in-the-loop (SIL) between MATLAB and CarSim.

Conclusion

Based on the data and simulation implemented in CarSim and MATLAB, the ACC system with NNPC can track the leader vehicle’s speed and keep the safe distance desired with a relatively small error in distance. This research has limitations in acceleration of the leader vehicle. A rapid change in speed will need new training data sets for the ANN model. This method also has a drawback in the working range because the ANN model can only work accurately if there is sufficient data training, and is hard to implement in a wide working range. It would need vast data and take huge computational effort to train. For future work, another ANN model can be implemented to switch between a vehicle’s speed range and acceleration to be able to work in different scenarios.

Acknowledgement

    This research is funded by a Research Grant from Publikasi Terindeks Internasonal (PUTI) 2020 Universitas Indonesia.

References

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