Bart De Schutter

Optimal coordination of variable speed limits to suppress shock waves

When freeway traffic is dense, shock waves may appear. These shock waves result in longer travel times and in sudden large variations in the speeds of the vehicles, which could lead to unsafe situations. Dynamic speed limits can be used to eliminate or at least to reduce the effects of shock waves. However, coordination of the variable speed limits is necessary in order to prevent the occurrence of new shock waves and/or a negative impact on the traffic flows in other locations. In this paper, we present a model predictive control approach to optimally coordinate variable speed limits for freeway traffic with the aim of suppressing shock waves. First, we optimize continuous valued speed limits, such that the total travel time is minimal. Next, we include a safety constraint that prevents drivers from encountering speed limit drops larger than, e.g., 10 km/h. Furthermore, to get a better correspondence between the computed and applied control signals, we also consider discrete speed limits. We illustrate our approach with a benchmark problem.

Demand Response With Micro-CHP Systems

With the increasing application of distributed energy resources and novel information technologies in the electricity infrastructure, innovative possibilities to incorporate the demand side more actively in power system operation are enabled. A promising, controllable, residential distributed generation technology is a microcombined heat and power system (micro-CHP). Micro-CHP is an energy-efficient technology that simultaneously provides heat and electricity to households. In this paper, we investigate to what extent domestic energy costs could be reduced with intelligent, price-based control concepts (demand response). Hereby, first the performance of a standard, so-called heat-led micro-CHP system is analyzed. Then, a model-predictive control (MPC) strategy aimed at demand response is proposed for more intelligent control of micro-CHP systems. Simulation studies illustrate the added value of the proposed intelligent control approach over the standard approach in terms of reduced variable energy costs. Demand response with micro-CHP lowers variable costs for households by about 1%-14%. The cost reductions are highest with the most strongly fluctuating real-time pricing scheme.

Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations

This paper presents a new method for the design and validation of advanced driver assistance systems (ADASs). With vehicle hardware-in-the-loop (VEHIL) simulations, the development process, and more specifically the validation phase, of intelligent vehicles is carried out safer, cheaper, and is more manageable. In the VEHIL laboratory, a full-scale ADAS-equipped vehicle is set up in a hardware-in-the-loop simulation environment, where a chassis dynamometer is used to emulate the road interaction and robot vehicles to represent other traffic. In this controlled environment, the performance and dependability of an ADAS is tested to great accuracy and reliability. The working principle and the added value of VEHIL are demonstrated with test results of an adaptive cruise control and a forward collision warning system. On the basis of the ‘V’ diagram, the position of VEHIL in the development process of ADASs is illustrated.

The extended linear complementarity problem

In this paper we define the Extended Linear Complementarity Problem (ELCP), an extension of the well-known Linear Complementarity Problem (LCP). We show that the ELCP can be viewed as a kind of unifying framework for the LCP and its various generalizations. We study the general solution set of an ELCP and we develop an algorithm to find all its solutions. We also show that the general ELCP is an NP-hard problem.

Stability Analysis and Nonlinear Observer Design using Takagi-Sugeno Fuzzy Models

Many problems in decision making, monitoring, fault detection, and control require the knowledge of state variables and time-varying parameters that are not directly measured by sensors. In such situations, observers, or estimators, can be employed that use the measured input and output signals along with a dynamic model of the system in order to estimate the unknown states or parameters. An essential requirement in designing an observer is to guarantee the convergence of the estimates to the true values or at least to a small neighborhood around the true values. However, for nonlinear, large-scale, or time-varying systems, the design and tuning of an observer is generally complicated and involves large computational costs. This book provides a range of methods and tools to design observers for nonlinear systems represented by a special type of a dynamic nonlinear model -- the Takagi--Sugeno (TS) fuzzy model. The TS model is a convex combination of affine linear models, which facilitates its stability analysis and observer design by using effective algorithms based on Lyapunov functions and linear matrix inequalities. Takagi--Sugeno models are known to be universal approximators and, in addition, a broad class of nonlinear systems can be exactly represented as a TS system. Three particular structures of large-scale TS models are considered: cascaded systems, distributed systems, and systems affected by unknown disturbances. The reader will find in-depth theoretic analysis accompanied by illustrative examples and simulations of real-world systems. Stability analysis of TS fuzzy systems is addressed in detail. The intended audience are graduate students and researchers both from academia and industry. For newcomers to the field, the book provides a concise introduction dynamic TS fuzzy models along with two methods to construct TS models for a given nonlinear system

Optimal Control of a Class of Linear Hybrid Systems with Saturation

We consider a class of first order linear hybrid systems with saturation. A system that belongs to this class can operate in several modes or phases; in each phase each state variable of the system exhibits a linear growth until a specified upper or lower saturation level is reached, and after that the state variable stays at that saturation level until the end of the phase. A typical example of such a system is a traffic signal controlled intersection. We develop methods to determine optimal switching time sequences for first order linear hybrid systems with saturation that minimize criteria such as average queue length, worst case queue length, average waiting time, and so on. First we show how the extended linear complementarity problem (ELCP), which is a mathematical programming problem, can be used to describe the set of system trajectories of a first order linear hybrid system with saturation. Optimization over the solution set of the ELCP then yields an optimal switching time sequence. Although this method yields globally optimal switching time sequences, it is not feasible in practice due to its computational complexity. Therefore, we also present some methods to compute suboptimal switching time sequences. Furthermore, we show that if there is no upper saturation, then for some objective functions the globally optimal switching time sequence can be computed very efficiently. We also discuss some approximations that lead to suboptimal switching time sequences that can be computed very efficiently. Finally, we use these results to design optimal switching time sequences for traffic signal controlled intersections.

Multi-agent Reinforcement Learning: An Overview

Multi-agent systems can be used to address problems in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must instead discover a solution on their own, using learning. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. This chapter reviews a representative selection of multi-agent reinforcement learning algorithms for fully cooperative, fully competitive, and more general (neither cooperative nor competitive) tasks. The benefits and challenges of multi-agent reinforcement learning are described. A central challenge in the field is the formal statement of a multi-agent learning goal; this chapter reviews the learning goals proposed in the literature. The problem domains where multi-agent reinforcement learning techniques have been applied are briefly discussed. Several multi-agent reinforcement learning algorithms are applied to an illustrative example involving the coordinated transportation of an object by two cooperative robots. In an outlook for the multi-agent reinforcement learning field, a set of important open issues are identified, and promising research directions to address these issues are outlined.

Model-based predictive control applied to multi-carrier energy systems

The optimal operation of an integrated electricity and natural gas infrastructure is investigated. The couplings between the electricity system and the gas system are modeled by so-called energy hubs, which represent the interface between the loads on the one hand and the transmission infrastructures on the other. To increase reliability and efficiency, storage devices are present in the multi-carrier energy system. In order to optimally incorporate these storage devices in the operation of the infrastructure, the capacity constraints and dynamics of these have to be taken into account explicitly. Therefore, we propose a model predictive control approach for controlling the system. This controller takes into account the present constraints and dynamics, and in addition adapts to expected changes of loads and/or energy prices. Simulations in which the proposed scheme is applied to a three-hub benchmark system are presented.

Cross-Entropy Optimization of Control Policies With Adaptive Basis Functions

This paper introduces an algorithm for direct search of control policies in continuous-state discrete-action Markov decision processes. The algorithm looks for the best closed-loop policy that can be represented using a given number of basis functions (BFs), where a discrete action is assigned to each BF. The type of the BFs and their number are specified in advance and determine the complexity of the representation. Considerable flexibility is achieved by optimizing the locations and shapes of the BFs, together with the action assignments. The optimization is carried out with the cross-entropy method and evaluates the policies by their empirical return from a representative set of initial states. The return for each representative state is estimated using Monte Carlo simulations. The resulting algorithm for cross-entropy policy search with adaptive BFs is extensively evaluated in problems with two to six state variables, for which it reliably obtains good policies with only a small number of BFs. In these experiments, cross-entropy policy search requires vastly fewer BFs than value-function techniques with equidistant BFs, and outperforms policy search with a competing optimization algorithm called DIRECT.

Optimizing acyclic traffic signal switching sequences through an Extended Linear Complementarity Problem formulation

Abstract In this paper we first show how the Extended Linear Complementarity Problem, which is a mathematical programming problem, can be used to design optimal switching schemes for a class of switched systems with linear dynamics subject to saturation. More specifically, we consider the determination of the optimal switching time instants (the switching sequences are acyclic, but the phase sequence is pre-fixed). Although this method yields globally optimal switching time sequences, it is not feasible in practice due to its computational complexity. Therefore, we also discuss some approximations that lead to suboptimal switching time sequences that can be computed very efficiently and for which the value of the objective function is close to the global optimum. Finally we use these results to design optimal switching time sequences for a traffic signal controlled intersection so as to minimize criteria such as average queue length, worst case queue length, average waiting time, and so on.