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In setpoint control, an equilibrium of the system to be controlled is typically employed as the setpoint. Thus, it is important to know the “possible equilibria” of the system, i.e. the value at which the state continues to stay by a suitable control input. Here, a possible equilibrium is called a controllable equilibrium. In this paper, we study two problems on controllable equilibria. First, we consider the problem of determining the set of controllable equilibria associated with constant inputs of magnitude one or less. Second, we address the problem of finding a constant input minimizing the distance between the resulting equilibrium and the desired state value. For each problem, we provide solutions in model-based and data-driven manners.

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.

Human driving behavior significantly affects vehicle fuel economy and emissions on hilly roads. This paper presents an ecological (eco) driving scheme (EDS) on hilly roads using nonlinear model predictive control (NMPC) in a mixed traffic environment. A nonlinear optimization problem with a relevant prediction horizon and a cost function is formulated using variables impacting the fuel economy of vehicles. The EDS minimizes vehicle fuel usage and emissions by generating the optimum velocity trajectory considering the longitudinal motion dynamics, the preceding vehicle’s state, and slope information from the digital road map. Furthermore, the immediate vehicle velocity and angle of the road slope are used to tune the cost function’s weight utilizing fuzzy inference methods for smooth maneuvering on slopes. Microscopic traffic simulations are used to show the effectiveness of the proposed EDS for different penetration rates on a real hilly road in Fukuoka City, Japan, in a mixed traffic environment with the conventional (human-based) driving scheme (CDS). The results show that the fuel consumption and emissions of vehicles are significantly reduced by the proposed NMPC-based EDS compared to the CDS for varying penetration rates. Additionally, the proposed EDS significantly increases the average speed of vehicles on the hilly road. The proposed scheme can be deployed as an advanced driver assistance system (ADAS).

There is a critical transition before a high-dimensional network system completely deteriorates. The Dynamical Network Marker (DNM) theory has been developed for the early prediction of such critical transitions by only using High-Dimension Small-Sample-Size (HDSSS) data. This article presents a model-free dominant pole placement approach for restabilizing the high-dimensional network systems towards avoidance of critical transitions by early treatment. Instead of traditional model-based pole placement, we present a model-free exact dominant pole placement method with dominant eigenvectors of the system matrix, which can be estimated from HDSSS data of system states. We further introduce two approximations of exact dominant pole placement to reduce the complexity of implementing restabilization. The first one is to approximate the right dominant eigenvector-based pole placement by reducing the number of intervened nodes. The second one is to intervene only in the diagonal part of the system matrix. We conduct theoretical analysis and numerical simulations to investigate the performance of the proposed dominant pole placement method.

In real-world social infrastructures such as power systems, proven controllers are already implemented and operated stably. To further improve the control performance reliably, bi-layered control with inheriting the existing controller is proposed: the existing controller generates the baseline control signal in the upper layer, while multiple sub-controllers individually coordinate the signal to actuate the infrastructure system in the lower layer. The sub-controllers are characterized by few parameters that represent the degree of coordination. Then, the update strategy of the parameter with guaranteeing the safety of the overall system is proposed. The effectiveness of the bi-layered control is shown via a numerical experiment with a power system model.

We formulate and analyze a profit maximization problem for one participant (aggregator) in a multiperiod electricity market with the consideration of profit fluctuations caused by uncertain photovoltaics (PV) output. We first introduce an aggregator's prosumption model, whereby an energy demand/supply profile can be achieved by adjusting the dispatchable devices, e.g., energy storage management systems and fossil fuel generators. Then, the uncertainty issue stemming from the PV profiles is handled by a classical stochastic programming framework and a robust optimization framework. A dispatchable tuning cost function is discussed based on the two frameworks above, and the distribution of the particular cost is analyzed. The simulation results show that although both of the two frameworks are able to overcome the PV uncertainty, an average strategy gives a higher expected value of profits with a higher risk (a larger variance) while a worst-case strategy gives a lower expected value of profits with a smaller risk.

This paper presents Adaptive Cruise Control (ACC) with look-ahead anticipation, based on the model of ACC used in recent commercial vehicles, to take early decisions in driving a vehicle on the freeway. The existing ACC found in the high-end cars has limited operating range as it often fails to respond smoothly in advance behind a decelerating vehicle. Although advanced techniques, such as model predictive control (MPC), can significantly improve a vehicle’s driving performance, they are associated with high computational complexity and have limited scopes for practical implementation. The proposed look-ahead anticipatory scheme of ACC predicts the relative states of the preceding vehicle using a conditional persistence prediction technique in an adaptive short horizon. With negligible computation cost, it determines the control input using parametric functions prudently for improving driving performance. The proposed scheme is evaluated on multiple vehicles in typical traffic scenarios to examine individual driving behavior and the stability of a vehicle string. Finally, we investigate the influences of a small part of vehicles with the proposed ACC on overall traffic using the AIMSUN traffic simulator and compare performances of overall traffic.

In this paper, we propose a bounding method for the optimal demand-dispatch schedules in the form of time-series intervals for battery-aided electrical grids with solar photovoltaic systems (PV) when the confidence intervals of both PV output and its temporal change are known. The result corresponds to the minimal regulating capacity for generators and batteries to meet most economically power demand induced by any PV output scenario. We model a set of predicted demand scenarios as a class of convex polytope, and formulate a day-ahead economic dispatch problem as parametric quadratic programming (pQP). Then, our problem reduces to finding the interval hull of minimizers of the pQP over the convex polytope. To solve it, we prove that a minimizer of the pQP for any feasible active set of constraints can be maximized/minimized at a fixed vertex on the convex polytope, from which it can be shown that the optimal generation at any time slot reaches its maximum/minimum if power demand is maximum/minimum at any time slot, and that the optimal discharging power at each time slot does so if power demand is maximum/minimum at the same time slot and as low/high as possible at the other time slots. This means the time-series intervals of dispatch solutions can be obtained by solving the day-ahead economic dispatch problem only for a finite number of such power demand scenarios. Numerical simulations demonstrate the effectiveness of the proposed method.

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.