Modelling and system identification of active magnetic bearing systems

Active magnetic bearing (AMB) systems have recently attracted much attention in the rotating machinery industry due to their advantages over traditional bearings such as fluid film and rolling element bearings. The AMB control system must provide robust performance over a wide range of machine operating conditions and over the machine lifetime in order to make this technology commercially viable. An accurate plant model for AMB systems is essential for the aggressive design of control systems. In this paper, we propose two approaches to obtain accurate AMB plant models for the purpose of control design: physical modelling and system identification. The former derives a model based upon the underlying physical principles. The latter uses input – output data without explicitly resorting to physical principles. For each problem, a brief summary of the theoretical derivation and assumptions is given. Experimental results based on data collected from an AMB test facility at the United Technologies Research Center provide a vehicle for a comparison of the two approaches.     

Modelling and evaluation of time aspects in business processes

There is a need for modelling and performance evaluation techniques and tools for a fast and reliable design of workflow systems. This paper introduces a modelling methodology based on coloured stochastic Petri nets. It allows the integration of control flow, organizational, information related and timing aspects in one modelling framework. The processing delays include stochastic distributions in addition to deterministic times. Several workflows and the effects of constrained shared resources needed for different tasks can easily be described and analysed together. Control flow and organizational aspects are modelled separately in resource and workflow models. These models are automatically compiled into one model, which can then be used for qualitative analysis or performance evaluation. The proposed modelling and evaluation method is supported by the software tool TimeNET. An application example shows its use.     

Feedback Control Methodologies for Nonlinear Systems

A number of computational methods have been proposed in the literature to design and synthesize feedback controls when the plant is modeled by nonlinear dynamics. However, it is not immediately clear which is the best method for a given problem; this may depend on the nature of the nonlinearities, size of the system, whether the amount of control used or time needed for the method is a concern, and other factors. In this paper, a comprehensive comparison study of five methods for the synthesis of nonlinear control systems is carried out. The performance of the methods on several test problems are studied, and some recommendations are made as to which feedback control method is best to use under various conditions.     

Job scheduling with forbidden setups and two objectives using genetic algorithms and penalties

One of the most important tasks in service and manufacturing systems is how to schedule arriving jobs such that some criteria will be satisfied. Up to now there have been defined a great variety of scheduling problems as well as corresponding models and solution approaches. Most models suffer from such more or less restrictive assumptions like single machine, unique processing times, zero set-up times or a single criterion. On the other hand some classical approaches like linear or dynamic programming are practicable for small-size problems only. Therefore over the past years we can observe an increasing application of heuristic search methods. But scheduling problems with multiple machines, forbidden setups and multiple objectives are scarcely considered. In our paper we apply a Genetic Algorithm to such a problem which was found at a continuous casting plant. Because of the forbidden setups the probability for a random generated schedule to be feasible is nearly zero. To resolve this problem we use three kinds of penalties, a global, a local and a combined approach. For performance investigations of these penalty types we applied our approaches to a real world test instance with 96 jobs, three machines and two objectives. We tested five different penalty levels with 51 independent runs to evaluate the impact of the penalties.     

Different Zhang functions leading to different Zhang-dynamics models illustrated via time-varying reciprocal solving

Along with neural dynamics (based on analog solvers) widely arising in scientific computation and optimization fields in recent decades which attracts extensive interest and investigation of researchers, a novel type of neural dynamics, called Zhang dynamics (ZD), has been formally proposed by Zhang et al. for the online solution of time-varying problems. By following Zhang et al.’s neural-dynamics design method, the ZD model, which is based on an indefinite Zhang function (ZF), can guarantee the exponential convergence performance for the online time-varying problems solving. In this paper, different indefinite Zhang functions, which can lead to different ZD models, are proposed and developed as the error-monitoring functions for the time-varying reciprocal problem solving. Additionally, for the goal of developing the floating-point processors or coprocessors for the future generation of computers, the MATLAB Simulink modeling and simulative verifications of such different ZD models are further presented for online time-varying reciprocal solving. The modeling results substantiate the efficacy of such different ZD models for time-varying reciprocal solving.     

Modeling,analysis and performance evaluation for fault diagnosis and Fault Tolerant Control in bottle-filling plant modeled using Hybrid Petri nets

In this paper, an approach to achieve fault diagnosis and Fault Tolerant Control in a typical bottle-filling plant using event based techniques is discussed. For this purpose, the plant is modeled using Hybrid Petri nets which enable study and analysis with regard to the working of the plant. Once effective modeling is done based on two different case studies considered, new algorithms are proposed to achieve fault diagnosis and Fault Tolerant Control on the models developed. Finally, performance measures with regard to the models proposed are evaluated to check the correctness of the models developed. Both analytical and numerical results are obtained which are highly useful to understand plant behavior.     

Iterative identification and control design using finite-signal-to-noise models

In this paper, a model is said to be validated for control design if using the model-based controller, the closed loop performance of the real plant satisfies a specified performance bound. To improve the model for control design, only closed loop response data is available to deduce a new model of the plant. Hence the procedure described herein involves three steps in each iteration: (i) closed loop identification; (ii) plant model extraction from the closed loop model; (iii) controller design. Thus our criteria for model validation involve both the control design procedure by which the closed loop system performance is evaluated, and the identification procedure by which a new model of the plant is deduced from the closed loop response data. This paper proposes new methods for both parts, and also proposes an iterative algorithm to connect the two parts. To facilitate both the identification and control tasks, the new finite-signal-to-noise (FSN) model of linear systems is utilized. The FSN model allows errors in variables whose noise covariances are proportional to signal covariances. Allowing the signal to noise ratios to be bounded but uncertain, a control theory to guarantee a variance upper bound is developed for the discrete version of this new FSN model. The identification of the closed loop system is accomplished by a new type of q-Markov Cover, adjusted to accommodate the assumed FSN structure of the model. The model of the plant is extracted from the closed loop identification model. This model is then used for control design and the process is repeated until the closed loop performance validates the model. If the iterations produce no such a controller, we say that this specific procedure cannot produce a model valid for control design and the level of the required performance must be reduced.     

Positivity embedding for noncolocated and nonsquare flexible systems

Positivity based control design for flexible structures provides closed-loop stability regardless of parameter variations and unmodeled dynamics. The present framework requires the plant to be square and the actuators colocated with rate sensors. These constraints severely limit achievable performance in control systems using the positivity approach. In this paper, a dynamic embedding is derived to render a nonsquare plant with noncolocated actuators and sensors positive real. The dynamic embedding is parametrized for general flexible structures. A numerical algorithm is developed to compute the embedding parameters. The Draper tetrahedral truss structure is used to demonstrate the design of dynamic embeddings. Relaxing the colocation constraint in a positivity based design significantly improves the closed-loop performance.     

A stochastic model for drilling optimization

Drilling optimization problems in oilfields are usually formulated and solved by using deterministic mathematical models, in which uncertain (indeterminate) factors or random issues are not taken into consideration. However, it has been widely experienced that random factors (such as those from soil layers, drill bits, and surface equipment) greatly affect the drilling performance. This paper introduces a new stochastic model for describing such random effects. This model, when used to optimization design, is more practical and provides a better characterization for real oilfield situations as compared with other deterministic models, and has been demonstrated to be more efficient in solving real design problems of drilling optimizations.     

An entire strategy for precise system tracking control of a revolving thin flexural link,part I: Finite element modeling and direct tuning controller design

An entire control strategy including a design based model, controller design, and system output modification for a distributed parameter system is illuminated by application to feedback control of a revolving thin flexural link. In Part I, a very realizable actuator and a sensor, which uses a motor and a tachometer, are applied to design the control system. The finite element modeling and the state space representation are obtained for the purpose of control system analysis and computer simulation. Instead of relying on parameter identification subroutines, a controller design based on directly tuning the parameter of the gain makes the closed-loop absolutely stable and good for system tracking control. This control system design scheme is robust, insensitive to system parameter changes, and this algorithm cannot depend on traditionally priori knowledge such as the system dimension, exact model, or observer design. The performance included in the presence of all the high frequency dynamics can be effectively shown through the computer simulation, and one is led to speculate that this design scheme may perform quite well in the real world implementation.     

Numerical modelling and analysis of external gear pumps by applying generalized control volumes

Components in gear pumps usually involve complex geometrical arrangements in order to achieve the desired performance. The use of lumped parametric models is considered the most accurate and effective method for investigation of the associated design issues. In this study, the numerical modelling approach based on the lumped parameters and control volume concepts is reviewed, especially for gear teeth within the meshing zone. To apply the approach to the entire gear pump, control volume concepts are generalized to all gear pockets and flow orifices with some reasonable assumptions. The assumptions include instantaneous angular positions, orifice transitions and imagined control volumes with internal flows. The fluid dynamics and pump performance, which even have the measurement difficulties, can be estimated to investigate and optimize the design parameters of gears by the model. A simulation example and its experimental results of a gear machine are presented to validate the proposed approach in this article.     

Non‐linear Control of a Hydraulic Piston Actuator without Velocity Information

The mathematical models of hydraulic actuators are known to be non‐linear. Therefore, in order to increase the dynamic performance of the closed‐loop system, we have to take into account the significant non‐linearities of the hydraulic plant in the controller design. In this contribution, we deal with so‐called valve‐controlled translational piston actuators. In general, they have the pleasing property to be exact input‐to‐state linearizable in the sense of the differential geometric control synthesis approach. However, in practical applications it often turns out that those controllers, which have to rely on the knowledge of the piston velocity, have problems in the case of noisy measurements. This is why, we propose an approach where the non‐linear controller is designed in such a way that the control law is independent of the piston velocity. It can be even proven that the closed‐loop system is globally asymptotically stable.     

Simple, robust, selftuning controller

This paper proposes a robust method for automatic tuning of parameters of a discrete PID controller. The tuning rules for SISO and MIMO systems are based on automatic determination of critical gain and critical frequency from the estimated model parameters. The plant model can be expressed by a transfer function in continuous and/or discrete form or by differential and/or difference equation. A simple control law using Takahashi discrete form is proposed. Simulations results prove that it is easy to use being able to handle minimum and nonminimum phase plant as well.     

A tutorial on the deterministic Impulse Control Maximum Principle: Necessary and sufficient optimality conditions

This paper considers a class of optimal control problems that allows jumps in the state variable. We present the necessary optimality conditions of the Impulse Control Maximum Principle based on the current value formulation. By reviewing the existing impulse control models in the literature, we point out that meaningful problems do not satisfy the sufficiency conditions. In particular, such problems either have a concave cost function, contain a fixed cost, or have a control-state interaction, which have in common that they each violate the concavity hypotheses used in the sufficiency theorem. The implication is that the corresponding problem in principle has multiple solutions that satisfy the necessary optimality conditions. Moreover, we argue that problems with fixed cost do not satisfy the conditions under which the necessary optimality conditions can be applied. However, we design a transformation, which ensures that the application of the Impulse Control Maximum Principle still provides the optimal solution. Finally, we show for the first time that for some existing models in the literature no optimal solution exists.     

ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics

In this paper, we present the ParadisEO white-box object-oriented framework dedicated to the reusable design of parallel and distributed metaheuristics (PDM). ParadisEO provides a broad range of features including evolutionary algorithms (EA), local searches (LS), the most common parallel and distributed models and hybridization mechanisms, etc. This high content and utility encourages its use at European level. ParadisEO is based on a clear conceptual separation of the solution methods from the problems they are intended to solve. This separation confers to the user a maximum code and design reuse. Furthermore, the fine-grained nature of the classes provided by the framework allow a higher flexibility compared to other frameworks. ParadisEO is of the rare frameworks that provide the most common parallel and distributed models. Their implementation is portable on distributed-memory machines as well as on shared-memory multiprocessors, as it uses standard libraries such as MPI, PVM and PThreads. The models can be exploited in a transparent way, one has just to instantiate their associated provided classes. Their experimentation on the radio network design real-world application demonstrate their efficiency.     

Fuzzy modelling environment for designing fuzzy controllers

Fuzzy control algorithms are developed based on fuzzy models of systems. The control issues are posed as multiobjective optimization problems involving goals and constraints imposed on system's variables. Two basic design modes embrace on- and off-line modes of control development. The first type of design deals with the time and state-dependent objectives and pertains to control determination based upon the current state of the fuzzy model. The second design mode gives rise to an explicit form of a fuzzy controller that is learned based on a given list of state-control associations. Both the fuzzy models as well as fuzzy controllers are realized as logic processors.     

Scheduling and control,performance measures and discrete event simulation

There are a growing number of finite capacity scheduling tools which are in use and available to industry. To be fully effective, a scheduling system needs to be incorporated within a control methodology which enables shop-floor performance to be analysed and, when necessary, corrective actions to be formulated. Manufacturing system performance, however, is not only dependent upon short-term planning decisions, but is also constrained by the capability for which it is designed. Hence, any control methodology should be based on a consistent set of performance measures and well defined procedures which help to integrate decision making at all levels in the order fulfilment process. The paper examines the distinctive roles of process optimisation and process control in the decision hierarchy; it classifies a set of measures of performance and other system variables according to the functions they serve in manufacturing control, and suggests a hierarchical process control methodology. The role of discrete event simulation techniques in linking decisions on manufacturing system design and production planning/control is also discussed.     

On the use of reduced-order models and simulation data in control-system design

This note extends some results on robust control and approximation,to provide a systematic framework for the use of reduced-ordermodels of single-input single-output systems in control systemsdesign where data from a finite number of simulations of theoriginal open-loop system can be used to assess the stabilityand performance of the implemented closed-loop scheme and substantiallyreduce conservatism during the design stage without the needfor a full closed-loop plant simulation.     

Approximate analysis and optimization of batch ordering policies in capacitated supply chains

Devising manufacturing/distribution strategies for supply chains and determining their parameter values have been challenging problems. Linking production management to stock keeping processes improves the planning of the supply chain activities, including material management, culminating in improved customer service levels. In this study, we investigate a multi-echelon supply chain consisting of a supplier, a plant, a distribution center and a retailer. Material flow between stages is driven by reorder point/order quantity inventory control policies. We develop a model to analyze supply chain behavior using some key performance metrics such as the time averages of inventory and backorder levels, as well as customer service levels at each echelon. The model is validated against simulation, yielding good agreement of robust performance metrics. The metrics are then used within an optimization framework to design the supply chain so as to minimize expected total system costs. The outcome of the optimization framework specifies how to move inventory throughout the supply chain and how to set inventory control parameters, i.e., reorder levels and replenishment batch sizes.