Optimal eco-driving scheme for reducing energy consumption and carbon emissions on curved roads.

Energy consumption and emissions of a vehicle are highly influenced by road contexts and driving behavior. Especially, driving on horizontal curves often necessitates a driver to brake and accelerate, which causes additional fuel consumption and emissions. This paper proposes a novel optimal ecological (eco) driving scheme (EDS) using nonlinear model predictive control (MPC) considering various road contexts, i.e., curvatures and surface conditions. Firstly, a nonlinear optimization problem is formulated considering a suitable prediction horizon and an objective function based on factors affecting fuel consumption, emissions, and driving safety. Secondly, the EDS dynamically computes the optimal velocity trajectory for the host vehicle considering its dynamics model, the state of the preceding vehicle, and information of road contexts that reduces fuel consumption and carbon emissions. Finally, we analyze the effect of different penetration rates of the EDS on overall traffic performance. The effectiveness of the proposed scheme is demonstrated using microscopic traffic simulations under dense and mixed traffic environment, and it is found that the proposed EDS substantially reduces the fuel consumption and carbon emissions of the host vehicle compared to the traditional (human-based) driving system (TDS), while ensuring driving safety. The proposed scheme can be employed as an advanced driver assistance system (ADAS) for semi-autonomous vehicles.

Bi-Level Coordinated Merging of Connected and Automated Vehicles at Roundabouts.

Traditional uncoordinated traffic flows in a roundabout can lead to severe traffic congestion, travel delay, and the increased fuel consumption of vehicles. An interesting way to mitigate this would be through cooperative control of connected and automated vehicles (CAVs). In this paper, we propose a novel solution, which is a roundabout control system (RCS), for CAVs to attain smooth and safe traffic flows. The RCS is essentially a bi-level framework, consisting of higher and lower levels of control, where in the higher level, vehicles in the entry lane approaching the roundabout will be made to form clusters based on traffic flow volume, and in the lower level, the vehicles' optimal sequences and roundabout merging times are calculated by solving a combinatorial optimization problem using a receding horizon control (RHC) approach. The proposed RCS aims to minimize the total time taken for all approaching vehicles to enter the roundabout, whilst minimally affecting the movement of circulating vehicles. Our developed strategy ensures fast optimization, and can be implemented in real-time. Using microscopic simulations, we demonstrate the effectiveness of the RCS, and compare it to the current traditional roundabout system (TRS) for various traffic flow scenarios. From the results, we can conclude that the proposed RCS produces significant improvement in traffic flow performance, in particular for the average velocity, average fuel consumption, and average travel time in the roundabout.

Chaotic Boltzmann machines.

The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, efficient hardware implementation is expected. Moreover, the ferromagnetic phase transition of the Ising model is shown to be characterised by the largest Lyapunov exponent of the proposed system. In general, a method to relate probabilistic models to nonlinear dynamics by derandomising Gibbs sampling is presented.

Chaotic Ising-like dynamics in traffic signals.

The green and red lights of a traffic signal can be viewed as the up and down states of an Ising spin. Moreover, traffic signals in a city interact with each other, if they are controlled in a decentralised way. In this paper, a simple model of such interacting signals on a finite-size two-dimensional lattice is shown to have Ising-like dynamics that undergoes a ferromagnetic phase transition. Probabilistic behaviour of the model is realised by chaotic billiard dynamics that arises from coupled non-chaotic elements. This purely deterministic model is expected to serve as a starting point for considering statistical mechanics of traffic signals.

Polynomial-time algorithm for controllability test of a class of boolean biological networks.

In recent years, Boolean-network-model-based approaches to dynamical analysis of complex biological networks such as gene regulatory networks have been extensively studied. One of the fundamental problems in control theory of such networks is the problem of determining whether a given substance quantity can be arbitrarily controlled by operating the other substance quantities, which we call the controllability problem. This paper proposes a polynomial-time algorithm for solving this problem. Although the algorithm is based on a sufficient condition for controllability, it is easily computable for a wider class of large-scale biological networks compared with the existing approaches. A key to this success in our approach is to give up computing Boolean operations in a rigorous way and to exploit an adjacency matrix of a directed graph induced by a Boolean network. By applying the proposed approach to a neurotransmitter signaling pathway, it is shown that it is effective.

Piecewise affine systems approach to control of biological networks.

In terms of a piecewise affine system representation, which is a kind of hybrid system model, this article discusses a series of approaches to modelling, analysing and synthesizing a biological network such as a gene-regulatory network. First, the input assignment problem, the controllable state set problem (CSP) and the input trajectory generation problem are emphasized as control problems to be addressed for biological networks. Subsequently, after the modelling issue on biological networks developed in the systems and control community is briefly explained, the CSP is described in detail with reference to control of the quorum-sensing system in the pathogen Pseudomonas aeruginosa. Finally, an optimal control design method to the quorum-sensing system is proposed as a solution to the input trajectory generation problem.