Danil V. Prokhorov

2008 Special Issue: Toyota Prius HEV neurocontrol and diagnostics

A neural network controller for improved fuel efficiency of the Toyota Prius hybrid electric vehicle is proposed. A new method to detect and mitigate a battery fault is also presented. The approach is based on recurrent neural networks and includes the extended Kalman filter. The proposed approach is quite general and applicable to other control systems.

Training Recurrent Neurocontrollers for Real-Time Applications

In this paper, we introduce a new approach to train recurrent neurocontrollers for real-time applications. We begin with training a recurrent neurocontroller for robustness on high-fidelity models of physical systems. For training, we use a recently developed derivative-free Kalman filter method which we enhance for controller training. After training, we fix weights of our recurrent neurocontroller and deploy it in an embedded environment. Then, we carry out additional training of the neurocontroller by adapting in real time its internal state (short-term memory), rather than its weights (long-term memory). Such real-time training is done with a new combination of simultaneous perturbation stochastic approximation (SPSA) and adaptive critic. Our critic is also a recurrent neural network (RNN), and it is trained by stochastic meta-descent (SMD) for increased efficiency. Our approach is applied to two important practical problems, electronic throttle control and hybrid electric vehicle control, with apparent performance improvement.

Neural Networks in Automotive Applications

Neural networks are making their ways into various commercial products across many industries. As in aerospace, in automotive industry they are not the main technology. Automotive engineers and researchers are certainly familiar with the buzzword, and some have even tried neural networks for their specific applications as models, virtual sensors, or controllers (see, e.g., [1] for a collection of relevant papers). In fact, a quick search reveals scores of recent papers on automotive applications of NN, fuzzy, evolutionary and other technologies of computational intelligence (CI); see, e.g., [2–4]. However, such technologies are mostly at the stage of research and not in the mainstream of product development yet. One of the reasons is “black-box” nature of neural networks. Other, perhaps more compelling reasons are business conservatism and existing/legacy applications (trying something new costs money and might be too risky) [5, 6]. NN technology which complements, rather than replace, the existing non-CI technology in applications will have better chances of wide acceptance (see, e.g., [8]). For example, NN is usually better at learning from data, while systems based on first principles may be better at modeling underlying physics. NN technology can also have greater chances of acceptance if it either has no alternative solution, or any other alternative is much worse in terms of the cost-benefit analysis. A successful experience with CI technologies at the Dow Chemical Company described in [7] is noteworthy. Ford Motor Company is one of the pioneers in automotive NN research and development [9, 10]. Relevant Ford papers are referenced below and throughout this volume. Growing emphasis on model based development is expected to help pushing mature elements of the NN technology into the mainstream. For example, a very accurate hardware-in-the-loop (HIL) system is developed by Toyota to facilitate development of advanced control algorithms for its HEV platforms [11]. As discussed in this chapter, some NN architectures and their training methods make possible an effective development process on high fidelity simulators for subsequent on-board (in-vehicle) deployment. While NN can be used both on-board and outside the vehicle, e.g., in a vehicle manufacturing process, only on-board applications usually impose stringent constraints on the NN system, especially in terms of available computational resources. Here we provide a brief overview of NN technology suitable for automotive applications and discuss a selection of NN training methods. Other surveys are also available, targeting broader application base and other non-NN methods in general; see, e.g., [12]. Three main roles of neural network in automotive applications are distinguished and discussed: models (Sect. 1), virtual sensors (Sect. 2) and controllers (Sect. 3). Training of NN is discussed in Sect. 4, followed by a simple example illustrating importance of recurrent NN (Sect. 5). The issue of verification and validation is then briefly discussed in Sect. 6, concluding this chapter.