Sensor-network-based robust distributed control and estimation

This paper proposes a novel distributed estimation and control method for uncertain plants. It is of application in the case of large-scale systems, where each control unit is assumed to have access only to a subset of the plant outputs, and possibly controls a restricted subset of input channels. A constrained communication topology between nodes is considered so the units can benefit from estimates of neighboring nodes to build their own estimates. The paper proposes a methodology to design a distributed control structure so that the system is asymptotically driven to equilibrium with L₂-gain disturbance rejection capabilities. A difficulty that arises is that the separation principle does not hold, as every single unit ignores the control action that other units might be applying. To overcome this, a two-stage design is proposed: firstly, the distributed controllers are obtained to robustly stabilize the plant despite the observation errors in the controlled output. At the second stage, the distributed observers are designed aiming to minimize the effects of the communication noise in the observation error. Both stages are formulated in terms of linear matrix inequalities. The performance is shown on a level-control real plant.     

From irreversible thermodynamics to a robust control theory for distributed process systems

In this paper we combine recent results that link passivity, as it is understood in system's theory, with concepts from irreversible thermodynamics to develop a robust control design methodology for distributed process systems. In this context, we show that passivity and stabilization of systems where non-dissipative phenomena are taking place is possible under very simple, finite dimensional control configurations. These include, boundary and high gain controllers, which combined with robust identification schemes should be able to provide efficient plant operation.     

Common randomness and distributed control: A counterexample

When agents collaborate to perform a control task, it is of interest to characterize the set of joint probability distributions they can achieve on their joint action space when they are passively provided with external common randomness. We give a simple counterexample to a natural conjecture about this class of joint distributions.     

Realization issues,tuning, and testing of a distributed predictive control algorithm

A non-iterative, non-cooperative distributed state-feedback control algorithm based on neighbor-to-neighbor communication, named distributed predictive control (DPC), has been recently proposed in the literature for constrained linear discrete-time systems, see  [15], [14], [2], [4]. The theoretical properties of DPC, such as convergence and stability, its extensions to the output feedback and tracking problems, and applications to simulated plants have been investigated in these papers. However, for a practical use of DPC some realization issues are still open, such as the automatic selection of some tuning parameters, the initialization of the algorithm, or its response to unexpected disturbances which could lead to the lack of the recursive feasibility, a fundamental property for any model predictive control (MPC) technique.This paper presents novel solutions to all these issues, with the goal to make DPC attractive for industrial and practical applications. Three realistic simulation examples are also discussed to evaluate the proposed numerical algorithms and to compare the performances of DPC to those of a standard centralized MPC algorithm.     

Performance evaluation of bag of gangs scheduling in a heterogeneous distributed system

Distributed systems deliver a cost-effective and scalable solution to the increasing performance intensive applications by utilizing several shared resources. Gang scheduling is considered to be an efficient time-space sharing scheduling algorithm for parallel and distributed systems. In this paper we examine the performance of scheduling strategies of jobs which are bags of independent gangs in a heterogeneous system. A simulation model is used to evaluate the performance of bag of gangs scheduling in the presence of high priority jobs implementing migrations. The simulation results reveal the significant role of the implemented migration scheme as a load balancing factor in a heterogeneous environment. Another significant aspect of implementing migrations presented in this paper is the reduction of the fragmentation caused in the schedule by gang scheduled jobs and the alleviation of the performance impact of the high priority jobs.     

Mixed integer programming for minimizing the period of a cyclic railway timetable for a single track with two train types

In this paper we extend the work of Bergmann (1975) to investigate the capacity of a single track, unidirectional rail line that adheres to a cyclic timetable. A set of intermediate stations lies between an origin and destination with one siding at each station. Two types of trains—express and local—are dispatched from the origin in alternating fashion. The local stops at every intermediate station and the express stops at no intermediate stations. A mixed integer linear program is developed in order to minimize the length of the dispatching cycle and minimize the total stopping (dwell) time of the local train at all stations combined. Constraints include a minimum dwell time for the local train at each station, a maximum total dwell time for the local train, and headway considerations on the main line and in stations. Hundreds of randomly generated problem instances with up to 70 stations are considered and solved to optimality in a reasonable amount of time using IBM ILOG CPLEX.     

Energy-based control of a distributed solar collector field

Model-based control of the outlet temperature of a distributed solar collector field is studied. An energy-based controller is derived using internal energy as a controlled variable. The controller relies on a distributed parameter nonlinear plant model and includes feedforward from the solar irradiation and inlet temperature. Convergence of the closed loop is proved, and the method is experimentally verified to perform well on a pilot-scale solar power plant.     

Robust wavelet network control for a class of autonomous vehicles to track environmental contour line

We address the problem of environmental contour line tracking for a class of autonomous vehicles. A reference velocity is designed for the autonomous vehicles to do contour line tracking. Based on Lashall invariance principle, an ideal controller is designed for the vehicle with ideal model and ideal information about the environmental concentration function to track the desired contour line. For the vehicle with possibly modeling uncertainty, we combine a neural controller containing a wavelet neural network (WNN) identifier with a robust control to construct a robust adaptive WNN control for the vehicle to track the desired environmental contour line. Then we give theoretical proof of the efficiency of the designed robust adaptive WNN control. Simulation results and conclusion are presented and discussed.     

Application of a continuous supervisory fuzzy control on a discrete scheduling of manufacturing systems

This paper considers the design and the practical implementation of a stable multiple objective real-time scheduling problem for a complex production system. In this paper, a complex production system is viewed as a kind of systems producing a variety of products (multiple-part-type) under constraints and multiple production objectives often conflicting. Previously, fuzzy control theory and fuzzy intervals arithmetic have been used to develop a distributed and supervised continuous-flow control architecture. In this framework, the objective of the distributed control structure is to balance the production process by adjusting the continuous production rates of the machines on the basis of the average local behavior. The supervisory control methodology aims at maintaining the overall performances within acceptable limits. In the new proposed approach, the problem of a stable real-time scheduling of jobs is considered at the shop-floor level. In this context, as the stability of the control structure is ensured, the actual dispatching times are determined from the continuous production rates through a discretization procedure. To deal with conflicts between jobs at a shared machine, a decision is made. It concerns the actual part to be processed and uses some criterions representing a measure of the job's priority. The simulation results show the validity of the proposed approach in terms of production cost, robustness and system stability.     

Reduced complexity adaptive nonlinear control of a distributed collector solar field

An approach to the control of a distributed collector solar field relying on feedback linearization, Lyapunov based adaptation and a simplified plant model is presented. The control objective consists of manipulating the oil flow so that the outlet oil temperature is regulated around a given setpoint. For dealing with plant nonlinearities and external disturbances, a nonlinear transformation is performed on the accessible variables such that the transformed system behaves as an integrator, to which linear control techniques are then applied. Since the transformation depends on an unknown parameter, an adaptation law is designed so as to minimize a Lyapunov function for the whole system's state. For the sake of control synthesis a simplified plant model which retains the bilinear nonlinearity is employed. The resulting control law has the same control structure of the one yielding exact input-output linearization but assumes a different placement of a temperature sensor. In order to justify this procedure, plant internal dynamics is studied. Experimental results obtained in the actual field are presented.     

Further results on sampled-data design for robust control of a single qubit

This paper presents further results on the robust control method for qubit systems in Dong et al. (2013) Dong, D., Petersen, I.R., &; Rabitz, H. (2013). Sampled-data design for robust control of a single qubit. IEEE Transactions on Automatic Control, 58, 2654–2659.[Crossref], [Web of Science ®] , [Google Scholar]. Based on the properties of an antisymmetric system, an alternative method is presented to analyse and exclude singularity intervals in the proof of partial original results. For the case of amplitude damping decoherence, a larger sampling period is presented when the upper bound of the probability of failure is small enough. For the case of phase damping decoherence, a larger sampling period is given when the lower bound of the target coherence is large enough. Furthermore, we provide improved sampling periods for amplitude damping decoherence and phase damping decoherence without the above prior constraints.     

Distributed artificial intelligence for FMS scheduling, control and design support

The aim of this paper is to point out some of the abilities of Distributed Artificial Intelligence in the domain of scheduling, control and design support of Flexible Manufacturing Systems. A distributed management system is proposed, based on Distributed Problem Solving, sub-field of Distributed Artificial Intelligence. The basic concepts are the concept of Resource Management Entity to ensure local optimization of the management of resources and the concept of cooperation to provide ability for global and local consistency. The management of resources is associated to activities such as scheduling, control or simulation. It is shown that this system computes not only practicable schedulings, but also presents, on the one hand, some abilities in supporting the design and the robust optimization of Flexible Manufacturing Systems, and, on the other hand, some abilities in supporting real-time control of such systems. This enables, in future works, to design a Distributed Decision Support System for integrated scheduling, control and design support of production systems.     

On optimal policies for control and estimation over a Gaussian relay channel

The problem of causal transmission of a memoryless Gaussian source over a two-hop memoryless Gaussian relay channel is considered. The source and the relay encoders have average transmit power constraints, and the performance criterion is mean-squared distortion. The main contribution of this paper is to show that unlike the case of a point-to-point scalar Gaussian channel, linear encoding schemes are not optimal over a two-hop relay channel in general, extending the sub-optimality results which are known for more than three hops. In some cases, simple three-level quantization policies employed at the source and at the relay can outperform the best linear policies. Further a lower bound on the distortion is derived and it is shown that the distortion bounds derived using cut-set arguments are not tight in general for sensor networks.     

A survey on clustering methods for distributed and networked control systems

Clustering strategies are becoming increasingly relevant to boost the scalability of distributed control methods by focusing the cooperation efforts on highly coupled agents. They are also relevant in systems where failing communication links and plug-and-play events are considered, which demand increased flexibility and modularity. This article reviews commonalities and differences of those distributed strategies that exploit the degree of interaction between control agents to boost the mentioned properties, frequently leading to control structures where the communication network becomes a decision variable that may evolve dynamically. Taxonomies based on the control law employed, the criterion for selecting the network topology, its static/dynamical nature, the control architecture, and the provided theoretical properties, are given. Additionally, a review of applications in power networks, water systems, vehicle and traffic systems, renewable energy plants, and chemical processes is provided.     

Comparison of a centralised and distributed approach for a generic scheduling system

PEGS (Production and Environmental Generic Scheduler) is a generic production scheduler that produces good schedules over a wide range of problems. It is centralised, using search strategies with the Shifting Bottleneck algorithm. We have also developed an alternative distributed approach using software agents. In some cases this reduces run times by a factor of 10 or more. In most cases, the agent-based program also produces good solutions for published benchmark data, and the short run times make our program useful for a large range of problems. Test results show that the agents can produce schedules comparable to the best found so far for some benchmark datasets and actually better schedules than PEGS on our own random datasets. The flexibility that agents can provide for today’s dynamic scheduling is also appealing. We suggest that in this sort of generic or commercial system, the agent-based approach is a good alternative.     

Load shedding and distributed resource control of stream processing networks

Recent advances in networking and information technology boost the development of new and advanced services offered over communication systems that integrate a widely heterogeneous mix of applications and computer devices. Without careful traffic control and resource management, the implied dramatic increase in the demand for networking resources and remote application services may lead to substantial degradation of the Quality of Service as experienced by the end users. In this paper, we consider the problem of joint admission control and dynamic resource allocation in a stream processing network so as to optimize the overall system utility. With a primal-dual-based optimization approach, we show that the resource allocation problem and the admission control problem can be decomposed. We then present a distributed algorithm which incorporates a push-and-pull-based admission control mechanism, and a pressure-based cμ rule for resource allocation. We show that the algorithm guarantees the stability of the network and converges to the optimal solution. Various numerical experiments are then presented to demonstrate the quality of the solution and the speed of convergence.     

Parallelizing with BDSC,a resource-constrained scheduling algorithm for shared and distributed memory systems

We introduce a new parallelization framework for scientific computing based on BDSC, an efficient automatic scheduling algorithm for parallel programs in the presence of resource constraints on the number of processors and their local memory size. BDSC extends Yang and Gerasoulis’s Dominant Sequence Clustering (DSC) algorithm; it uses sophisticated cost models and addresses both shared and distributed parallel memory architectures. We describe BDSC, its integration within the PIPS compiler infrastructure and its application to the parallelization of four well-known scientific applications: Harris, ABF, equake and IS. Our experiments suggest that BDSC’s focus on efficient resource management leads to significant parallelization speedups on both shared and distributed memory systems, improving upon DSC results, as shown by the comparison of the sequential and parallelized versions of these four applications running on both OpenMP and MPI frameworks.     

Fair and distributed peer-to-peer allocation of a common, refillable resource

We consider the general problem of distributed and fair peer-to-peer (P2P) allocation of a common, refillable resource. This problem recurs in a number of scenarios, for example grid computing, content distribution, Internet Service Provider service sharing, and distributed file storage over asymmetric channels. We present several distributed schemes for this allocation problem and show that these schemes guarantee two key properties: (i) asymptotic fairness, in that (even maliciously colluding) users are proportionally assigned resources corresponding to what they contribute; (ii) natural incentive to join and cooperate fairly in the system. We demonstrate the practicability of our approaches on a prototype P2P file storage system designed for typical residential Internet connections, in which download capacities often significantly exceed upload capacities. Our implementation shares file data when communications are idle using random linear codes so that, when needed, an end-user can download a file from several sources at a higher data rate than his home computer’s upload capacity. We present experimental results that support our analytical guarantees.     

不确定性机械系统的最优鲁棒控制设计: 模糊法

针对不确定性机械系统,提出了一种新的最优鲁棒控制方法.本文用模糊法去描述机械系统中的不确定性.机械系统的性能要求是确定的(保证最低要求),同时也是模糊的(成本控制里用到).所提出的控制方法是确定的,而不是基于假设的规则.经过严格的理论证明,控制系统最终可达到理想的性能指标.基于模糊信息,本文设计了一个性能指标(综合成本,包括系统的平均模糊性能和控制成本).通过最小化此性能指标,可解决控制的最优设计问题.这种最优设计方法可得到唯一的解析形式的最优解.总的来说,这种最优鲁棒控制方法较为系统,能够保证确定的系统性能得以实现,同时控制成本最小.最后,本文选了一个机械系统作为例子.