Felipe Valencia

Feasible-Cooperation Distributed Model Predictive Control Scheme Based on Game Theory

Abstract This work deals with the formulation of a distributed model predictive control scheme as a decision problem in which the decisions of each subsystem affect the decisions of the other subsystems and the performance of the whole system. This decision problem is formulated as a bargaining game. This formulation allows each subsystem to decide whether to cooperate or not depending on the benefits that the subsystem can gain from the cooperation. A solution based on game theory is proposed. The convexity and feasibility properties of the proposed solution are also presented. The proposed control scheme is tested on a case study with a chain of two continuous stirred tank reactors followed by a nonadiabatic flash separator.

A multiobjective-based switching topology for hierarchical model predictive control applied to a hydro-power valley

In a Hierarchical Model Predictive Control (H-MPC) framework, this paper explores suitable time-variant structures for the hierarchies of different local MPC controllers. The idea is to adapt to different operational conditions by changing the importance of the local controllers. This is done by defining the level of the hierarchy they belong to, and solving within each level the local MPC problem using the information provided by the higher levels at the current time step and the predicted information from the lower levels obtained in the previous time step. As selecting a hierarchy results in a combinatorial optimization problem, it is explicitly solved for a limited number of relevant topologies only and then the switching between topologies is defined with a multiobjective optimizer, so as to decide the best H-MPC scheme according to the expected performance. A comparison with fixed-topology H-MPC controllers is done, showing the effectiveness of the proposed approach for the power control of a hydro-power valley.

Coherent and Incoherent Structural Dynamics in Laser-Excited Antimony

We investigate the excitation of phonons in photoexcited antimony and demonstrate that the entire electron-lattice interactions, in particular coherent and incoherent electron-phonon coupling, can be probed simultaneously. Using femtosecond electron diffraction (FED) with high temporal resolution, we observe the coherent excitation of the fully symmetric \Ag\ optical phonon mode via the shift of the minimum of the atomic potential energy surface. Ab initio molecular dynamics simulations on laser excited potential energy surfaces are performed to quantify the change in lattice potential and the associated real-space amplitude of the coherent atomic oscillations. Good agreement is obtained between the parameter-free calculations and the experiment. In addition, our experimental configuration allows observing the energy transfer from electrons to phonons via incoherent electron-lattice scattering events. The electron-phonon coupling is determined as a function of electronic temperature from our DFT calculations and the data by applying different models for the energy-transfer.

Non-Linear Model Predictive Control Based on Game Theory for Traffic Control on Highways

Abstract This paper presents a methodology for traffic control using a distributed model predictive control scheme based on game theory (GT-DMPC). Using the traffic model METANET as prediction model, the control objective is to minimize the total time spent by vehicles in the traffic network. The proposed control methodology is compared with a centralized model predictive control approach and a non-controlled-case. The simulations show that the GT-DMPC controller efficiently distributes the vehicles entering the highway, and presents a similar performance in comparison with centralized MPC.

Bargaining Game Based Distributed MPC

Despite of the efforts dedicated to design methods for distributed model predictive control (DMPC), the cooperation among subsystems still remains as an open research problem. In order to overcome this issue, game theory arises as an alternative to formulate and characterize the DMPC problem. Game theory is a branch of applied mathematics used to capture the behavior of the players (agents or subsystems) involved in strategic situations where the outcome of a player is function not only of his choices but also depends on the choices of others. In this chapter a bargaining game based DMPC scheme is proposed; roughly speaking, a bargaining game is a situation where several players jointly decide which strategy is best with respect to their mutual benefit. This allows to deal with the cooperation issues of the DMPC problem. Additionally, the bargaining game framework allows to formulate solutions where the subsystems do not have to solve more than one optimization at each time step. This also reduces the computational burden of the local optimization problems.

Moving horizon estimator for measurement delay compensation in model predictive control schemes

This paper deals with the problem of the loss of performance of the pair observer-controller, when measurements have a delay due to communication over networks. Here we consider the case where the estimation of the states is carried out using a moving horizon estimator (MHE), the control actions are computed by using a centralized model predictive controller (MPC), and the delay varies randomly and is n times the sampling time (n ∈ ℕ). In order to tackle the loss of performance associated with the pair MHE-MPC, an MHE with variable structure is proposed. The resulting pair MHE-MPC was tested using the four tank process as a test bed showing an improvement on the performance.

Evaluation And Comparison Of Kalman-Filter-Based Distributed State Estimators For Large-Scale Systems

Abstract This paper presents a performance comparison of Kaiman-filter-based distributed state estimations techniques for large-scale systems. Three distributed Kaiman filter schemes are selected to be implemented in simulation, namely, distributed and decentralized Kaiman filter (DDKF), distributed Kaiman filter with state consensus (DKF-SC) and distributed Kaiman filter with diffusion strategy (DKF-DS), using the heat rod dynamical system as benchmark. The heat conduction and convection in a solid rod dynamic is used as test bed. The comparison is performed by evaluating prediction error and convergence of each scheme, considering the partition of the system into two, three and four subsystems.

Game Theory Based Distributed Model Predictive Control for a Hydro-Power Valley Control

Abstract Hydro-power valleys are large scale systems used to power energy production. The stored water is also used for navigation and agriculture purposes. Recently the control of hydro-power valleys has been formulated as a centralized optimal control problem. However, the scale of the systems make unfeasible real time implementations for centralized controllers. In this work we propose the use of the game theory to formulate a distributed model predictive control scheme to control an hydro-power valley. The proposed control scheme is tested by using a power reference tracking scenario as a test-bed.

A performance comparison for wind power integration into the grid system

The integration level of wind power into the grid system over the world has been growing at a very fast rate in the last few years and is still keeping this pace. In the future, wind power is expected to be at least comparable to the conventional power generation systems. Large-scale wind farms will need to be integrated into traditional power grid systems, creating the need to establish technical standards to make this integration feasible. This paper covers the performance comparison of a power system with and without the inclusion of a large-scale wind farm. The global system model is presented along to a case of study and the simulation results.

Optimal Control of Urban and Highway Traffic

The problems associated with vehicular traffic are becoming more noticeable in large cities and highways, mainly because it is becoming less feasible to build new roads or expand the existing ones. This issue is beginning to be treated as a control systems optimization problem, where the objective is to maximize the use of the existing infrastructure. However, the size of the traffic networks and the large number of variables make the implementation of optimization-based centralized control schemes not feasible. This article presents the development of distributed and decentralized predictive controllers for traffic control purposes. The proposed schemes are tested in simulation environment using a highway with two access ramps and an urban traffic network composed by three intersections.