Robust distributed model predictive control of linear systems with structured time-varying uncertainties

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min–max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.     

The evolution of web-based optimisation: From ASP to e-Services

In many application domains, optimisation and other analytic models are often embedded as a decision engine within the respective business processes. In this paper, we study recent trends in the provision of optimisation tools and optimisation-based decision support systems (DSS) as web-enabled distributed applications. We analyse the evolution from the Application Service Provision (ASP) model to the e-Services model, and we illustrate the importance of distributed optimisation components in the effective deployment of embedded “business analytics”. We finally provide an overview of the OSP and the WEBOPT projects, which deliver optimisation-based applications and optimisation components in a distributed environment.     

Design of distributed PID-type dynamic matrix controller for fractional-order systems

With the continuous requirements for product quality and safety operation in industrial production, it is difficult to describe the complex large-scale processes with integer-order differential equations. However, the fractional differential equations may precisely represent the intrinsic characteristics of such systems. In this paper, a distributed PID-type dynamic matrix control method based on fractional-order systems is proposed. First, the high-order approximate model of integer order is obtained by utilising the Oustaloup method. Then, the step response model vectors of the plant is obtained on the basis of the high-order model, and the online optimisation for multivariable processes is transformed into the optimisation of each small-scale subsystem that is regarded as a sub-plant controlled in the distributed framework. Furthermore, the PID operator is introduced into the performance index of each subsystem and the fractional-order PID-type dynamic matrix controller is designed based on Nash optimisation strategy. The information exchange among the subsystems is realised through the distributed control structure so as to complete the optimisation task of the whole large-scale system. Finally, the control performance of the designed controller in this paper is verified by an example.     

Distributed optimisation problem with communication delay and external disturbance

This paper investigates the distributed optimisation problem for the multi-agent systems (MASs) with the simultaneous presence of external disturbance and the communication delay. To solve this problem, a two-step design scheme is introduced. In the first step, based on the internal model principle, the internal model term is constructed to compensate the disturbance asymptotically. In the second step, a distributed optimisation algorithm is designed to solve the distributed optimisation problem based on the MASs with the simultaneous presence of disturbance and communication delay. Moreover, in the proposed algorithm, each agent interacts with its neighbours through the connected topology and the delay occurs during the information exchange. By utilising Lyapunov–Krasovskii functional, the delay-dependent conditions are derived for both slowly and fast time-varying delay, respectively, to ensure the convergence of the algorithm to the optimal solution of the optimisation problem. Several numerical simulation examples are provided to illustrate the effectiveness of the theoretical results.     

On the game-theoretic analysis of distributed generative adversarial networks

In this paper, a distributed method is proposed for training multiple generative adversarial networks (GANs) with private data sets via a game-theoretic approach. To facilitate the requirement of privacy protection, distributed training algorithms offer a promising solution to learn global models without sample exchanges. Existing studies have mainly concentrated on training neural networks using pure cooperation strategies, which are not suitable for GANs. This paper develops a new framework for distributed GANs, where two groups of discriminators and generators are involved in a zero-sum game. Under connected graphs, such a framework is reformulated as a constrained minmax optimisation problem. Then, a fully distributed training algorithm is proposed without exchanging any private data samples. The convergence of the proposed algorithm is established via advanced consensus and optimisation techniques. Simulation studies are presented to validate the effectiveness of the proposed framework and algorithm.     

Distributed MPC of polytopic uncertain systems: handling quantised communication and packet dropouts

In this paper, we study the distributed model predictive control (MPC) of polytopic uncertain systems with quantised communication and packet dropouts. The model of the whole plant is divided into a certain number of incomplete subsystems. Due to the nature of the distributed control structure, there is generally a lack of information about the state of the overall system. Each subsystem shares its information with neighbour subsystems via reliable connection. Distributed MPC controllers are designed for each subsystem by solving the linear matrix inequalities optimisation problem. The distributed state feedback laws are quantised and transmitted via communication network. An iterative algorithm is presented to make coordination among distributed state feedback laws. The communication is assumed to be affected by random packet dropouts in a representation of Bernoulli distributed white sequences with known conditional probabilities. A case study is carried out to demonstrate the effectiveness of the proposed distributed MPC technique.     

Modelling of fluid properties in hydraulic positive displacement machines

This paper presents a numerical model for the simulation of a swash-plate axial piston pump, focusing on the characterization of fluid properties.As it is well known, the reduction of flow oscillations (which generates pressure ripples and produces vibration and noise in the entire circuit) and the avoidance of cavitation are the major problems in the design of these pumps. Developing a simulation code can be very useful for component optimisation in order to predict and reduce the undesired phenomena. The paper first gives a quick overview on a previously developed pump model; afterwards four different models of the fluid are presented: they take into account cavitation in different ways. Their aim at characterizing as well as possible the unsteady and erratic cavitation features in a simplified manner, in order to apply the models to the simulation of hydraulic components.In the second part of the paper an application is shown of these models to an axial piston pump: a few results are presented and compared with available test data. The effects of the fluid models on the predicted pump performance are shown and commented.     

Distributed model predictive control with hierarchical architecture for communication: application in automated irrigation channels

This paper is concerned with a distributed model predictive control (DMPC) method that is based on a distributed optimisation method with two-level architecture for communication. Feasibility (constraints satisfaction by the approximated solution), convergence and optimality of this distributed optimisation method are mathematically proved. For an automated irrigation channel, the satisfactory performance of the proposed DMPC method in attenuation of the undesired upstream transient error propagation and amplification phenomenon is illustrated and compared with the performance of another DMPC method that exploits a single-level architecture for communication. It is illustrated that the DMPC that exploits a two-level architecture for communication has a better performance by better managing communication overhead.     

Decentralised decision making in heterogeneous teams using anonymous optimisation

This paper considers the scenario where multiple autonomous agents must cooperate in making decisions to minimise a continuous and differentiable team cost function. A distributed and asynchronous optimisation algorithm is presented which allows each agent to incrementally refine their decisions while intermittently communicating with the rest of the team. A convergence analysis provides quantitative requirements on the frequency agents must communicate that is prescribed by the structure of the decision problem. In general the solution method will require every agent to communicate to and have a model of every other agent in the team. To overcome this, a specific subset of systems, called Partially Separable, is defined. These systems only require each agent to have a combined summary of the rest of the team and allows each agent to communicate locally over an acyclic communication network, greatly increasing the scalability of the system.     

On an algorithm for combined system optimisation and parameter estimation

The paper is concerned with the determination of optimum steady-state operation of industrial plant where the optimisation is performed using a mathematical model with parameters whose values are estimated by comparing model and real plant measurements. The two associated problems of system optimisation and model parameter estimation are discussed and an algorithm is examined whose purpose is to accomplish the correct steady-state optimum operating condition on the real plant in spite of inaccuracies in the structure of the mathematical model. The aim of the paper is to investigate the performance of the algorithm which is accomplished through a theoretical analysis of its application to a linear process, where the optimisation is performed using a quadratic performance index and a mathematical model of incorrect structure. Particular emphasis is given to the stability and convergence properties of the algorithm and to the effect of real process measurement errors. Simulation results are also presented illustrating the effectiveness of the technique when applied to nonlinear optimisation problems including a study concerned with determining optimum controller set points to maximise the net rate of return from a chemical reactor plant.     

Verifying Automata Specification of Distributed Probabilistic Real-Time Systems

In this paper,a qualitative model checking algorithm for verification of distributed probabilistic real-time systems(DPRS)is presented.The model of DPRS,called real-time proba bilistic process model(RPPM),is over continuous time domain.The properties of DPRS are described by using deterministic timed automata(DTA).The key part in the algorithm is to map continuous time to finite time intervals with flag variables.Compared with the existing algorithms,this algorithm uses more general delay time equivalence classes instead of the unit delay time equivalence classes restricted by event sequence,and avoids generating the equivalence classes of states only due to the passage of time.The result shows that this algorithm is cheaper.     

Evaluation of the concrete electromagnetics properties by using radar measurements in a context of building sustainability

In this paper, an inverse method to evaluate the dielectric values of concrete sample by using radar measurements in the context of the building security is presented. A numerical model of the experimental device is proposed and compared with the data measured. By using this numerical model, a convergent optimisation strategy using a Gauss-Newton method is given. This optimisation process is applied on simulated data and on several configurations using measurements.     

Co-evolutionary immuno-particle swarm optimization with penetrated hyper-mutation for distributed inventory replenishment

This article is betrothed to serve as a continuation of the emerging swarm techniques to solve supply chain problems. Our aim is to map some of the pressing research challenges contributed by the artificial intelligence community and to develop an improved algorithm: Co-evolutionary immuno-particle swarm optimisation with penetrated hyper-mutation (COIPSO-PHM). In this paper, we proposed a new algorithm which uses clonal selection approach in particle swarm optimisation by embedding co-evolutionary theory to solve the problem of inventory replenishment in distributed plant–warehouse–retailer system. Constraint handling is explicitly taken care by implanting augmented lagrangian concept. To demonstrate the efficiency of the algorithm, its performance are evaluated and compared on 10 benchmarked problems (made constrained problem via random initialisation in the infeasible zone) including functions with uni-modalities as well as multi-modalities. The result follows shows superior performance of the algorithm in every respect.     

Distributed optimisation for multi-agent systems with the first-order integrals under Markovian switching topologies

This paper studies the distributed optimisation problem for multi-agent systems with the first-order dynamics over Markovian switching topologies. The interaction topology among agents’ switches following a Markov process and each topology is modelled as a state of the Markov process. The aim is to minimise the global cost functions and make the agents converge to the optimal point through the network communication between the agents, where each agent has a local convex cost function only known by itself. Utilising the knowledge of convex analysis and graph theory, we establish a distributed algorithm for the optimisation problem with randomly switching topologies. A sufficient condition for the existence of such algorithm is obtained by using the Lyapunov method. Besides, the result is also extended to the cases of a Markov process with partially unknown transition rates. Finally, numerical simulations are given to validate the proposed algorithm.     

分布式系统可靠性模型

文章首先给出分布式系统的一般描述;然后采用建立在图论、概率论及布尔代数基础上的网络分析法建立了分布式系统的可靠性模型,用以综合考虑系统拓朴结构、任务集、通讯路径集和处理单元集等对系统可靠性的影响;最后,针对二维TORUS网,给出了建模实例以及模型的有关应用。     

New local diversification techniques for flexible job shop scheduling problem with a multi-agent approach

The Flexible Job Shop problem is among the hardest scheduling problems. It is a generalization of the classical Job Shop problem in that each operation can be processed by a set of resources and has a processing time depending on the resource used. The objective is to assign and to sequence the operations on the resources so that they are processed in the smallest time. In our previous work, we have proposed two Multi-Agent approaches based on the Tabu Search (TS) meta-heuristic. Depending on the location of the optimisation core in the system, we have distinguished between the global optimisation approach where the TS has a global view on the system and the local optimisation approach (FJS MATSLO) where the optimisation is distributed among a collection of agents, each of them has its own local view. In this paper, firstly, we propose new diversification techniques for the second approach in order to get better results and secondly, we propose a new promising approach combining the two latter ones. Experimental results are also presented in this paper in order to evaluate these new techniques.     

Towards price-based predictive control of a small-scale electricity network

ABSTRACT

With the increasing deployment of battery storage devices in residential electricity networks, it is essential that the charging and discharging of these devices be scheduled so as to avoid adverse impacts on the electricity distribution network. In this paper, we propose a non-cooperative, price-based hierarchical distributed optimisation approach that provably recovers the centralised, or cooperative, optimal performance from the point of view of the network operator. The distributed optimisation algorithm provides important insights into the appropriate design of contracts between an energy provider and their associated residential customers, who can themselves act as energy providers as well as consumers (e.g. due to rooftop solar photovoltaics and batteries) depending on the time of the day and on real-time prices. To make the presentation self-contained, and to highlight key properties of the price-based optimisation algorithm, the dual ascent method and its convergence properties are reviewed. The performance of the proposed price-based optimisation algorithm is validated on recent measurement taken from an Australian electricity distribution company, Ausgrid. In addition to analysing the results of the open loop solution, we investigate the effect of real-time prices in the closed loop using a model predictive control framework.     

Non-fragile observer-based output feedback control for polytopic uncertain system under distributed model predictive control approach

In this paper, a non-fragile observer-based output feedback control problem for the polytopic uncertain system under distributed model predictive control (MPC) approach is discussed. By decomposing the global system into some subsystems, the computation complexity is reduced, so it follows that the online designing time can be saved.Moreover, an observer-based output feedback control algorithm is proposed in the framework of distributed MPC to deal with the difficulties in obtaining the states measurements. In this way, the presented observer-based output-feedback MPC strategy is more flexible and applicable in practice than the traditional state-feedback one. What is more, the non-fragility of the controller has been taken into consideration in favour of increasing the robustness of the polytopic uncertain system. After that, a sufficient stability criterion is presented by using Lyapunov-like functional approach, meanwhile, the corresponding control law and the upper bound of the quadratic cost function are derived by solving an optimisation subject to convex constraints. Finally, some simulation examples are employed to show the effectiveness of the method.