Iakovos Michailidis

A "plug and play" computationally efficient approach for control design of large-scale nonlinear systems using cosimulation: a combination of two "ingredients"

This article describes a computationally efficient simulation-based control design approach that has the capability of handling optimization problems arising from large-scale nonlinear systems, with fast convergence properties and low computational requirements. The purpose of this article is to describe the main features of the PCAO algorithm, analyze its convergence and stability properties, and demonstrate its efficiency using simulations of two large-scale, real-life systems (a traffic network and an energy-efficient building) for which conventional optimization techniques fail to provide an efficient simulation-based control design.

A “plug-n-play” computationally efficient approach for control design of large-scale nonlinear systems using co-simulation

Recently, there has been a growing interest towards simulation-based control design (co-simulation), where the controller utilizes an optimizer to minimize or maximize an objective function (system performance) whose evaluation involves simulation of the system to be controlled. However, existing simulation-based approaches are not able to handle in a computationally efficient way large-scale optimization problems involving hundreds or thousands of states and parameters. In this paper, we propose and analyze a new simulation-based control design approach, employing an adaptive optimization algorithm capable of efficiently handle large-scale control problems. The convergence properties of the proposed algorithm are established. Simulation results exhibit efficiency of the proposed approach when applied to large-scale problems. The simulation results employ two large-scale real-life systems for which conventional popular optimization techniques totally fail to provide an efficient simulation-based control design.

Convex Design Control for Practical Nonlinear Systems

This paper describes a new control scheme for approximately optimal control (AOC) of nonlinear systems, convex control design (ConvCD). The key idea of ConvCD is to transform the approximate optimal control problem into a convex semi-definite programming (SDP) problem. Contrary to the majority of existing AOC designs where the problem that is addressed is to provide a control design which approximates the performance of the optimal controller by increasing the “controller complexity,” the proposed approach addresses a different problem: given a controller of “fixed complexity” it provides a control design that renders the controller as close to the optimal as possible and, moreover, the resulted closed-loop system stable. Two numerical examples are used to show the effectiveness of the method.

Simulation-based synthesis for approximately optimal urban traffic light management

Suitable control measures and strategies must be taken to counteract the reduced throughput and the degradation of the network infrastructure caused by traffic congestion in urban networks. This paper studies and analyzes the performance of an adaptive traffic-responsive strategy that manages the traffic light parameters (the cycle time and the split time) in an urban network to reduce traffic congestion. The proposed traffic-responsive strategy adopts a nearly-optimal control formulation: first, an (approximate) solution of the HJB is parametrized via an appropriate Lyapunov positive definite matrix; then, the solution is updated via a procedure that generates candidate control strategies and selects at each iteration the best one based on the estimation of close-to-optimality and the information coming from the simulation model of the network (simulation-based design). Simulation results obtained using an AIMSUN model of the traffic network of Chania, Greece, an urban traffic network containing many varieties of junction staging, demonstrate the efficiency of the proposed approach.

Simulation-based implementation and evaluation of a system of systems optimization algorithm in a building control system

The objective of this research is to evaluate the performance of a system of systems optimization algorithm, namely, L4G-PCAO, in building energy systems. Since the test bed of this research is an office building with more than two hundred occupiers, the heating and cooling demands of the building must always be fully satisfied. Consequently, changes in the currently-installed control system cannot be made forthrightly. Therefore, fresh ideas like implementation of new control strategies or optimization algorithms should be firstly put to the test via dynamic simulation, which makes engineers capable of examining new control and optimization strategies. The performance should then be analyzed and evaluated before implementing in the use case. This paper presents a strategy for simulation-based implementation of L4G-PCAO in a building energy system and also evaluates its performance. The results show that it is not only possible to conserve energy by applying this newly-developed optimization algorithm to existing control systems, but also it can shift the usage of energy sources in a more environment-friendly direction.

Adaptive Optimal Control for Large-Scale Nonlinear Systems

In this paper, we present an adaptive optimal control approach applicable to a wide class of large-scale nonlinear systems. The proposed approach avoids the so-called loss-of-stabilizability problem and the problem of poor transient performance that are typically associated with adaptive control designs. Moreover, it does not require the system model to be in a certain parameterized form, and most importantly, it is able to efficiently handle systems of large dimensions. Theoretical analysis establishes that the proposed methodology guarantees stability and exponential convergence to state trajectories that can be made as close as desired to the optimal ones. A numerical example demonstrates the capability of the proposed approach to overcome loss-of-stabilizability problems. Moreover, simulation experiments for energy-efficient climate control performed on a ten-office building demonstrate the effectiveness of the proposed approach in large-scale nonlinear applications.

Multi-objective control strategy for energy management of grid-connected heterogeneous microgrids

The demand-side energy management of microgrids comprising of buildings of heterogeneous nature (residential, commercial, industrial, etc.) and thus exhibiting heterogeneous occupancy pattern, requires the development of appropriate energy management systems (EMSs) that can integrate the maximum exploitation of the distributed energy resources like photovoltaic panels with the thermal comfort of the occupants. This paper presents a simulation-based optimization approach for the design of an EMS in grid-connected photovoltaic-equipped microgrids with heterogeneous buildings and occupancy schedules. The EMS optimizes a multi-objective criterion that takes into account both the energy cost and the thermal comfort of the aggregate microgrid. A three-building microgrid test case is used to demonstrate the effectiveness of the proposed approach: comparisons with alternative rule-based and optimization-based EMSs show that the proposed EMS strategy exploits the occupancy information to automatically change the energy demand of each building, resulting in improved energy cost and thermal comfort.

A supervisory approach to microgrid demand response and climate control

Microgrids equipped with small-scale renewable-energy generation systems and energy storage units offer challenging opportunity from a control point of view. In fact, in order to improve resilience and enable islanded mode, micro-grid energy management systems must dynamically manage controllable loads by considering not only matching energy generation and consumption, but also thermal comfort of the occupants. Thermal comfort, which is often neglected or oversimplified, plays a major role in dynamic demand response, especially in front of intermittent behavior of the renewable energy sources. This paper presents a novel control algorithm for joint demand response management and thermal comfort optimization in a microgrid composed of a block of buildings, a photovoltaic array, a wind turbine, and an energy storage unit. In order to address the large-scale nature of the problem, the proposed control strategy adopt a two-level supervisory strategy: at the lower level, each building employs a local controller that processes only local measurements; at the upper level, a centralized unit supervises and updates the three controllers with the aim of minimizing the aggregate energy cost and thermal discomfort of the microgrid. Comparisons with alternative strategies reveal that the proposed supervisory strategy efficiently manages the demand response so as to sensibly improve independence of the microgrid with respect to the main grid, and guarantees at the same time thermal comfort of the occupants.

Local4Global Adaptive Optimization and control for System-of-Systems

Over the recent past years research effort has been dedicated towards addressing a generic solution in System of Systems (SoS) control problems. Two are the main obstacles that have to be bypassed in such problem cases, especially in real life applications, rendering the optimization problem into a complicated/challenging one: (i) modelling/simulation tools are usually used in order to construct an as-close-as-possible to reality accurate model, whose construction though requires a considerable amount of time and effort and (ii) furthermore, standard control system designs when applied to SoS exhibit poor performance as they are required to handle very high-dimensional problems. In this paper, we present a first attempt towards addressing these issues. More precisely, a new adaptive optimal control methodology is presented and evaluated. The main attributes of the proposed control methodology is its local nature with minimum requirements for coordination between the constituent system of the SoS and its model-free nature.

Automatically fine-tuned speed control system for fuel and travel-time efficiency: A microscopic simulation case study

Within the current document a model independent, cognitive and adaptive optimization mechanism, namely CAO, is adopted for providing efficient speed/torque control actions. However since real-life tests were not feasible, a simulation model of a 1.4lt displacement gasoline car, playing the role of the actual car, was adopted while the potential maximum cruising speed levels are chosen so as to emulate a usual suburban route and the vehicle speed control is replicated by a common PID scheme. The control decisions are applied directly to the car throttle/torque pedal itself. The goal of the optimization application was to minimize the vehicle velocity error, while minimizing the total fuel consumption for a certain 10km trip with varying road angles/slopes. It should be noted that CAO module could be applied directly to a car system in a straightforward manner without any preparatory investigations. Initially PID gain values were selected arbitrary while a PID gain — tuning module from Matlab/Simulink was used in order to fine-tune them under certain road conditions. Finally the tuned values were used as initial ones for all CAOs application cases (A, B, C and D). CAO presented substantial improvements in the specified performance index, with respect to the base case speed strategy, as well as to the PID tuning in all simulation scenarios considered.