Set Of Local Controllers Research Articles

DisEHPPC: Enabling Heterogeneous Privacy-Preserving Consensus-Based Scheme for Economic Dispatch in Smart Grids.

These days, the increasing incremental cost consensus-based algorithms are designed to tackle the economic dispatch (ED) problem in smart grids (SGs). However, one principal obstruction lies in privacy disclosure for generators and consumers in electricity activities between supply and demand sides, which may bring great losses to them. Hence, it is extraordinarily essential to design effective privacy-preserving approaches for ED problems. In this article, we propose a two-phase distributed and effective heterogeneous privacy-preserving consensus-based (DisEHPPC) ED scheme, where a demand response (DR)-based framework is constructed, including a DR server, data manager, and a set of local controllers. The first phase is that Kullback-Leibler (KL) privacy is guaranteed for the privacy of consumers' demand by the differential privacy method. The second phase is that (ε,δ) -privacy is, respectively, achieved for the generation energy of generators and the sensitivity of electricity consumption to electricity price by designing the privacy-preserving incremental cost consensus-based (PPICC) algorithm. Meanwhile, the proposed PPICC algorithm tackles the formulated ED problem. Subsequently, we further carry out the detailed theoretical analysis on its convergence, optimality of final solution, and privacy degree. It is found that the optimal solution for the ED problem and the privacy preservation of both supply and demand sides can be guaranteed simultaneously. By evaluation of a numerical experiment, the correctness and effectiveness of the DisEHPPC scheme are confirmed.

On coparanormality in distributed supervisory control of discrete‐event systems

AbstractDecomposition and localization of a supervisor are both reduction methods in distributed supervisory control of discrete‐event systems. Decomposition is employed to reduce the number of events and localization is used to reduce the number of states of local controllers. In decomposition of a supervisor both observation and control scopes are restricted, whereas in localization only control authority is restricted to the corresponding local controller. In this paper, we propose a decomposition method by defining a coparanormality property, and by using the relative observability property of a monolithic supervisor. Coparanormality is a coobservation property defined based on a paranormality property for a set of natural projections. It is shown that each supervisor can be coparanormal, provided a set of appropriate natural projections exist. Moreover, it is proved that relative observability is a sufficient condition to decompose a supervisor. Furthermore, the supervisor localization procedure is generalized to find a set of local controllers for any partition of the controllable events set. The implementation of such local controllers may become easier in industrial systems.

A Game-Theoretic Decentralized Model Predictive Control of Thermal Appliances in Discrete-Event Systems Framework

This paper presents a decentralized model predictive control (MPC) scheme for thermal appliances coordination control in smart buildings. The general system structure consists of a set of local MPC controllers and a game-theoretic supervisory control constructed in the framework of discrete-event systems (DES). In this hierarchical control scheme, a set of local controllers work independently to maintain the thermal comfort level in different zones, and a centralized supervisory control is used to coordinate the local controllers according to the power capacity and the current performance. Global optimality is ensured by satisfying the Nash equilibrium at the coordination layer. The validity of the proposed method is assessed by a simulation experiment including two case studies. The results show that the developed control scheme can achieve a significant reduction of the peak power consumption while providing an adequate temperature regulation performance if the system is $\mathcal{P}$ -observable.

Fixed-point based hierarchical MPC control design for a cryogenic refrigerator

In this paper, a simple, general and scalable hierarchical control framework is proposed and validated through the interconnection of the Joule–Thomson and the Brayton cycle stages of a cryogenic refrigerator. The proposed framework enables to handle the case of destabilizing interconnections through state and/or control signals (which is the case of the cryogenic refrigerator example). Moreover, it offers the possibility to simply change the behavior of the overall system (depending on the context) by only changing the coordinator problem's parameters without changing the set of local controllers used by subsystems which is a common industrial requirement regarding industrial control architectures. Finally, the proposed scheme enables a smooth operator handover on a specific subsystem and/or actuator.

Constrained tracking control for nonlinear systems

This paper proposes a tracking control strategy for nonlinear systems without needing a prior knowledge of the reference trajectory. The proposed method consists of a set of local controllers with appropriate overlaps in their stability regions and an on-line switching strategy which implements these controllers and uses some augmented intermediate controllers to ensure steering the system states to the desired set points without needing to redesign the controller for each value of set point changes. The proposed approach provides smooth transient responses despite switching among the local controllers. It should be mentioned that the stability regions of the proposed controllers could be estimated off-line for a range of set-point changes. The efficiencies of the proposed algorithm are illustrated via two example simulations.

Supervisor localization of discrete-event systems under partial observation

Recently we developed supervisor localization, a top-down approach to distributed control of discrete-event systems. Its essence is the allocation of monolithic (global) control action among the local control strategies of individual agents. In this paper, we extend supervisor localization by considering partial observation; namely not all events are observable. Specifically, we employ the recently proposed concept of relative observability to compute a partial-observation monolithic supervisor, and then design a suitable localization procedure to decompose the supervisor into a set of local controllers. In the resulting local controllers, only observable events can cause state change. We finally illustrate our result by a Transfer Line example.

Hierarchical distributed stabilization of power networks

Large fluctuation of electric power due to high penetration of renewable energy sources such as photovoltaic and wind power generation increases the risk to make the whole power network system unstable. The conventional frequency control called load frequency control is based on PID (proportional-integral-derivative) control or more advanced centralized and decentralized/distributed control. If we could more effectively use information on the state of the other neighbor generators, we can expect to make the whole system more robust against the large frequency fluctuation. This paper proposes a fundamental framework towards the design of hierarchical distributed stabilizing controllers for a network of power generators and loads. This novel type of distributed controller, composed of a global controller and a set of local controllers, takes into account the effect of the interaction among the generators and loads to improve robustness for the variation of locally stabilizing controllers.

Damping of Power System Oscillations by using ANFIS based UPFC supplementary controller

The main purpose of this paper is to investigate an Adaptive Neuron- Fuzzy Inference System (ANFIS) based supplementary Unified Power Flow Controller (UPFC) in superimposing a damping function on the control signal of UPFC who’s incorporated in multi-machines power system. By using a hybrid learning procedure, the proposed ANFIS construct an input –output mapping based on stipulated input-output data pairs.This control scheme designed to produce supplementary signal for damping oscillations in the interconnected power systems. Oscillations in power systems have to be taken a serious note when the fault takes place in any part of the system; else this might lead to the instability mode and shutting down of the power system. The UPFC device is modelled using dynamic phasor method and interfaced with an interconnected power system. The improved damping performance was studied and it is validated on a benchmark power system example taken from literature. The systems model is evaluated via simulations during unbalanced faults. The control actions are formulated as a set of local controllers, namely the generator excitation controller, power system stabilizer (PSS) and unified power flow controller, which are coordinated to maintain transient angle stability, transient voltage stability and damping of inter-area and local oscillations following a disturbance.

Comparison of Linear Quadratic Controllers with Stability Analysis for DC‐DC Boost Converters Under Large Load Range

AbstractThis paper presents four different control design strategies for linear quadratic regulators applied to DC‐DC boost converters operating under large load range, pointing out advantages and drawbacks by means of a performance comparison. Two of the strategies are based on single controllers and the other two use a set of local controllers to cope with the entire load range. The robust stability of the closed‐loop linearized system with the four control strategies is investigated via the existence of a common Lyapunov function, obtained in a fast and efficient way by the solution of linear matrix inequality conditions.

Synthesizing scheduled robust model predictive control with target tracking

Systems with large operating regions and non-zero state target tracking have limited the industrial application of robust model predictive control (RMPC) with synthetic action. To overcome the problem, this paper presents a novel formulation of synthesizing scheduled RMPC for linear time varying (LTV) systems. Off-line, we compute the matrix that transforms target output into steady state first. Then a set of stabilizing state feedback laws which are corresponding to a set of estimated regions of stability covering the desired operating region are provided. On-line, these control laws are implemented as a single scheduled state feedback model predictive control (MPC) which switches between the set of local controllers and achieve the desired target at last. Finally, the algorithm is illustrated with an example.

A multi-model approach to gain-scheduling control for agile missile autopilot design

AbstractThis paper presents a multi-model approach to the design of a robust gain-scheduled missile autopilot subject to rapid change in the dynamic stability during boost-phase. In this approach, a set of local controllers is designed for a family of multi-models to represent uncertainty bounds over a flight envelope. The application of linear matrix inequalities (LMIs) and ν-gap metric combined into the multi-model approach permits a robust gain-scheduling scheme to be systematically accomplished. As a result, the design of the overall gain-scheduled autopilot is formulated as a set of linear matrix inequalities given by desired performance requirements. Simulation results are conducted for a heading reversal maneuver over rapid center of gravity variation to show stability and performance robustness of the proposed approach.

A framework for design of scheduled output feedback model predictive control

We present a stabilizing scheduled output feedback Model Predictive Control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local output feedback predictive controllers with their estimated regions of stability covering the desired operating region, and implement them as a single scheduled output feedback MPC which on-line switches between the set of local controllers and achieves nonlinear transitions with guaranteed stability. This algorithm provides a general framework for scheduled output feedback MPC design.

Global power system control using generator excitation, PSS, FACTS devices and capacitor switching

A novel power system control scheme is developed in this paper using a framework for coordinating dissimilar controllers at different locations. The control actions are formulated in the so-called global controller as a set of local controllers, namely the generator excitation controller, power system stabilizer (PSS), unified power flow controller (UPFC) and capacitor switching, which are coordinated to maintain transient angle stability, transient voltage stability and damping of long-term inter-area oscillations following a disturbance. The proposed coordinated control scheme is tested on a nine-bus example system.

Efficient Scheduled Stabilizing Output Feedback Model Predictive Control for Constrained Nonlinear Systems

In this note, we present a computationally efficient scheduled output feedback model predictive control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local output feedback predictive controllers with their estimated regions of stability covering the desired operating region, and implement them as a single scheduled output feedback MPC which on-line switches between the set of local controllers and achieves nonlinear transitions with guaranteed stability. The algorithm is illustrated with a highly nonlinear continuous stirred tank reactor process.

A Framework for Design of Scheduled Output Feedback Model Predictive Control

Abstract We present a stabilizing scheduled output feedback Model Predictive Control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local output feedback predictive controllers with their estimated regions of stability covering the desired operating region, and implement them as a single scheduled output feedback MPC which on-line switches between the set of local controllers and achieves nonliilear transitions with guaranteed stability. This algorithm provides a general framework for scheduled output feedback MPC design.

Efficient scheduled stabilizing model predictive control for constrained nonlinear systems

AbstractWe present a computationally efficient scheduled model predictive control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local predictive controllers with estimates of their regions of stability covering the desired operating region, and implement them as a single scheduled MPC which on‐line switches between the set of local controllers and achieves nonlinear transitions with guaranteed stability. This algorithm is computationally efficient and provides a general framework for the scheduled MPC design. The algorithm is illustrated with two examples. Copyright © 2003 John Wiley & Sons, Ltd.

Decentralized adaptive predictive control of multireach irrigation canal

This paper presents the design of a predictive controller for decentralized control of an irrigation canal. The overall canal is decomposed into N subsystems (reaches) separated by a control structure (gate). The subsystems are interdependent because flow through the structures depends on both up- and downstream water levels. Here, predictive control theory is used to formulate a set of local controllers for the automatic operation of each reach gate. The control scheme presented explicitly takes into account the interactions among subsystems. The objective of each controller is to maintain the downstream end water level of each reach at a constant target value under external perturbation. To evaluate the controller performance in realistic conditions, it is tested on an open-canal flow non-linear model. The case study considered deals with eight canal reaches.

A Comparative Analysis and Evaluation of Decentralized Control Structures

The research on the control problem of large scale systems of different kinds, mainly in the field of manufacturing, transportation and economics, has suggested a deeper investigation on the implementable decision structures. Present paper deals with the definition of a Generalized Hierarchical Structure (GHS), which allows to arrange in itself all the structures till now presented. The proposed GHS consists of “modules”, each one composed by a modelled part of the system together with its controller.Based on said structure, new results on coordinability and consistency of a set of local controllers are presented, and a comparative analysis of existing structures with multiple control units is performed.

Large-scale discrete systems : a two-level optimization scheme†

Design of controllers for large-scale systems often requires methods based on decomposition and decentralization to overcome the twin problems of high dimensionality and constraints on the information structure. In this paper a two-level control scheme that effectively utilizes these methods for the optimization of large-scale discrete systems is presented. Decentralized controllers that optimize individual decoupled subsystems while ignoring the interactions constitute a set of local controllers at the first level, while a global controller at a higher hierarchical level is used to minimize the suboptimality of performance resulting due to the presence of the interactions. Such a control scheme is robust, simple to implement and guarantees global asymptotic stability of the overall system.