Akira Ohata

Smart Driving of a Vehicle Using Model Predictive Control for Improving Traffic Flow

Traffic management on road networks is an emerging research field in control engineering due to the strong demand to alleviate traffic congestion in urban areas. Interaction among vehicles frequently causes congestion as well as bottlenecks in road capacity. In dense traffic, waves of traffic density propagate backward as drivers try to keep safe distances through frequent acceleration and deceleration. This paper presents a vehicle driving system in a model predictive control framework that effectively improves traffic flow. The vehicle driving system regulates safe intervehicle distance under the bounded driving torque condition by predicting the preceding traffic. It also focuses on alleviating the effect of braking on the vehicles that follow, which helps jamming waves attenuate to in the traffic. The proposed vehicle driving system has been evaluated through numerical simulation in dense traffic.

Benchmark problem for automotive engine control

The SICE research committee on advanced powertrain control theory provides a V6 engine model for benchmark of advanced control methodologies. The application problem is to design the control of starting the engine model. Currently, over ten groups are tackling the problem. In this paper, the model, the distinctive features of the problem and their midterm results are briefly summarized. Through this activity, we have the confidence that the activity is effective to enhance the collaboration between the control engineering academic society and the automotive industry.

A Vehicle-Intersection Coordination Scheme for Smooth Flows of Traffic Without Using Traffic Lights

This paper presents a coordination scheme of automated vehicles at an intersection without using any traffic lights. Using a two-way communication network, vehicles approaching the intersection from all sections are globally coordinated, by considering their states all together in a model predictive control framework, in order to achieve smooth traffic flows at the intersection. The optimal trajectories of the vehicles are computed based on avoidance of their cross-collision risks around the intersection under relevant constraints and preferences. The scheme efficiently utilizes the intersection area by preventing each pair of conflicting vehicles from approaching their cross-collision point at the same time, instead of reserving the whole intersection area for the conflicting vehicles one after another. The scheme also enables left- or right-turning movements of vehicles under constrained velocity without using any auxiliary lanes. The proposed vehicle-intersection coordination scheme is evaluated through numerical simulation in a typical test intersection consisting of both multilanes and single-lane approaches with turning movements of vehicles. Observations under different traffic flow conditions reveal that the proposed scheme significantly improves intersection performance compared with the traditional signalized intersection scheme.

An adaptive fuel injection control with internal model in automotive engines

An adaptive fuel-injection control system has been developed for automotive engines. It uses a new air-fuel (A/F) ratio sensor to sense the wide A/F range, and consists of three subsystems based on a fuel behavior model: an inverse system (IS), a model following compensator (MFC), and an estimator of model parameters (EMP). The EMP estimates the parameters of the fuel behavior model during idling and adjusts the IS and MFC. This system allows accurate control of the A/F ratio during acceleration or deceleration, even when the engine coolant temperature conditions and/or fuel compositions vary.<<ETX>>

Introduction to the Benchmark Challenge on SICE Engine Start Control Problem

The speed control during cold start for SI engines is a challenging topic due to the difficulties in handling the dramatic change of the engine dynamics. The SICE Research Committee on Advanced Powertrain Control Theory provides a V6 SI engine model and the benchmark problem of cold start control focusing on the engine speed behavior when the engine model starts. The control design specification is explained in detail and a traditional control is also shown in this paper. Finally, a brief review is given on intermediate results of the challengers.

Coordination of automated vehicles at a traffic-lightless intersection

This paper presents a coordination scheme of automated vehicles at an intersection without using any traffic lights under a connected vehicles environment. Vehicles approaching the intersection from all sections are globally coordinated, by considering their states all together in a model predictive control framework, for their safe and rapid crossing. The optimal trajectories of the vehicles are computed based on avoidance of the cross-collision risks around the intersection. Compared with the other methods, the scheme enhances safety by generating the trajectories of vehicles far from each other with respect to the cross-collision points and enables turning movements under constrained velocity without using any auxiliary lanes. The proposed coordination scheme is evaluated through numerical simulation for a simple test intersection consisting of four single-lane approaches. Observations under different traffic flow conditions reveal that the proposed scheme significantly improves performance compared with the traditional traffic-light-based intersection control.

Development of a New Model Based Air-Fuel Ratio Control System

The second-generation air-fuel ratio control method has been developed to reduce exhaust gas emissions in accordance with the improvements in catalysts. The control system consists of a feedforward control using a fuel behavior model, a feedback control using an universal exhaust gas oxygen (UEGO) sensor and a feedback control utilizing the heated exhaust gas oxygen (HEGO) sensor. Experimental results on actual vehicles show the effectiveness of presented method.

Identification of nonlinear ARX model with input and output dependent coefficients

This paper proposes to identify a nonlinear system in the nonlinear ARX model from input-output data. A local linear ARX model identification is done by selecting the input and output data around the selected level. By integrating the local linear ARX models, a nonlinear ARX model with parameters nonlinearly depending on the input and output is identified. The dependence of parameters on the input and output is identified numerically and can be expressed approximately by the polynomials. The proposed method gives an approach to identify and to represent a nonlinear system which may be used to design a controller

Traffic Signal Control of a Road Network Using MILP in the MPC Framework

This paper investigates the significance of a traffic signal control scheme that simultaneously adjusts all signal parameters, i.e., cycle time, split time and offset, in a road network. A novel framework of model predictive control (MPC) is designed that overcomes the limitations of other MPC based traffic signal control strategies, which are mostly restricted to control only split or green time in a fixed cycle ignoring signal offset. A simple macroscopic model of traffic tailored to MPC is formulated that describes traffic dynamics in the network at a short sampling interval. The proposed framework is demonstrated using a small road network with dynamically changing traffic flows. The parameters of the proposed model are calibrated by using data obtained from detailed microscopic simulation that yields realistic statistics. The model is transformed into a mixed logical dynamical system that is suitable to a finite horizon, and traffic signals are optimized using mixed integer linear programming (MILP) for a given performance index. The framework makes the signals flexibly turn to red and green by adapting quickly to any changes in traffic conditions. Results are also verified by microscopic traffic simulation and compared with other signal control schemes.

An application study of online reference governor to boost pressure control for automotive diesel engines

In this paper, we consider an application of an online reference governor (RG) to boost pressure control of automotive diesel engines. In order to treat the system nonlinearity, the plant is expressed as a piecewise affine model through system idenfication for each fixed operating condition of engine speed and fuelling. The plant model is utilized for the future prediction in the online RG. Then, an online iterative RG algorithm based on the gradient descent method is applied to generate the optimal modified reference so that the constraint on the maximal turbocharger rotation speed is satisfied. We show the effectiveness of the present method through an experiment with a production vehicle.