An optimal controller for time-varying stochastic systems with multiple time delays

A flexible controller for optimal control of linear time-varying stochastic systems with multiple time delays is developed. The plants to be controlled are represented using a multi-input multi-output controlled autoregressive moving average model. The delays are described using a diagonal matrix. Input and output filters in the form of linear time-varying moving average operators are introduced into a generalized minimum variance control cost functional in order to meet the needs of various applications. The controller is applicable to a large class of linear time-varying systems

Multisensor Out Of Sequence Data Fusion for Estimating the State of Discrete Control Systems

The fusion center of a complex control system estimates its state with the information provided by different sensors. Physically distributed sensors, communication networks, pre-processing algorithms, multitasking, etc, introduce non-systematic delays in the arrival of information to the fusion center, making the information available out-of-sequence (OOS). For real-time control systems, the state has to be efficiently estimated with all the information received so far. So, several solutions of the OOS problem for dynamic multiple-input multiple-output (MIMO) discrete control systems traditionally solved by the Kalman filter (KF) have been proposed recently. This paper presents two new streamlined algorithms for the linear and non-linear case. IFAsyn, the linear algorithm, is equivalent to other optimal solutions but more general, efficient and easy to implement. EIFAsyn, the nonlinear one, is a new solution of the OOS problem in the extended KF (EKF) framework

Development and evaluation of methods for control and modelling of multiple-input multiple-output systems

In control, a common type of system is the multiple-input multiple-output (MIMO) system, where the same input may affect multiple outputs, or conversely, the same output is affected by multiple inputs. In this thesis two methods for controlling MIMO systems are examined, namely linear quadratic Gaussian (LQG) control and decentralized control, and some of the difficulties associated with them.One difficulty when implementing decentralized control is to decide which inputs should control which outputs, also called the input-output pairing problem. There are multiple ways to solve this problem, among them using gramian based measures, which include the Hankel interaction index array, the participation matrix and the Σ2 method.\ua0 These methods take into account system dynamics as opposed to many other methods which only consider the steady-state system. However, the gramian based methods have issues with input and output scaling. Generally, this is handled by scaling all inputs and outputs to have equal range. However, in this thesis it is demonstrated how this can cause an incorrect pairing. Furthermore, this thesis examines other methods of scaling the gramian based measures, using either row or column sums, or by utilizing the Sinkhorn-Knopp algorithm. It is shown that there are considerable benefits to be gained from the alternative scaling of the gramian based measures, especially when using the Sinkhorn-Knopp algorithm. The use of this method also has the advantage that the results are completely independent of the original scaling of the inputs and outputs.An expansion to the decentralized control structure is the sparse control, in which a decentralized controller is expanded to include feed-forward or MIMO blocks. In this thesis we explore how to best use the gramian based measures to find sparse control structures, and propose a method which demonstrates considerable improvement compared to existing methods of sparse control structure design.A prerequisite to implementing control configuration methods is an understanding of the processes in question. In this thesis we examine the pulp refining process and design both static and dynamic models for pulp and paper properties such as shives width, fiber length and tensile index, and various available inputs. We demonstrate that utilizing internal variables (primarily consistencies) estimated from temperature measurements yields improved results compared to using solely measured variables. The measurement data from the refiners is noisy, sometimes sparse and generally irregularly sampled. This thesis discusses the challenges posed by these constraints and how they can be resolved.\ua0\ua0 An alternative way to control a MIMO system is to implement an LQG controller, which yields a single control structure for the entire system using a state based controller. It has been proposed that LQG control can be an effective control scheme to be used on networked control systems with wireless channels. These channels have a tendency to be unreliable with packet delays and packet losses. This thesis examines how to implement an LQG controller over such unreliable communication channels, and derives the optimal controller minimizing the cost function expressed in actuated controls.When new methods of control system design and analysis are introduced in the control engineering field, it is important to compare the new results with existing methods. Often this requires application of the methods on examples, and for this purpose benchmark processes are introduced. However, in many areas of control engineering research the number of examples are relatively few, in particular when MIMO systems are considered. For a thorough assessment of a method, however, as large number of relevant models as possible should be used. As a remedy, a framework has been developed for generating linear MIMO models based on predefined system properties, such as model type, size, stability, time constants, delays etc. This MIMO generator, which is presented in this thesis, is demonstrated by using it to evaluate the previously described scaling methods for the gramian based pairing methods

Enhancement of power system stability using wide area measurement system based damping controller

Contemporary power networks are gradually expanding incorporating new sources of electrical energy and power electronic based devices. The major stability issue in large interconnected power systems is the lightly damped interarea oscillations. In the light of growth of their incidents there are increased concerns about the effectiveness of current control devices and control systems in maintaining power system stability. This thesis presents a Wide Area Measurement System (WAMS) based control scheme to enhance power system stability. The control scheme has a hierarchical (two-level) structure comprising a Supplementary Wide-Area Controller (SWAC) built on top of existing Power System Stabilisers (PSSs). The SWAC's focus is on stabilising the critical interarea oscillations in the system while leaving local modes to be controlled entirely by local PSSs. Both control systems in the two levels work together to maintain system stability. The scheme relies on synchronised measurements supplied by Phasor Measurement Units (PMUs) through the WAMS and the only cost requirement is for the communication infrastructure which is already available, or it will be in the near future. A novel linear quadratic Gaussian (LQG) control design approach which targets the interarea modes directly is introduced in this thesis. Its features are demonstrated through a comparison with the conventional method commonly used in power system damping applications. The modal LQG approach offers simplicity and flexibility when targeting multiple interarea modes without affecting local modes and local controllers, thus making it highly suitable to hierarchical WAMS based control schemes. Applicability of the approach to large power systems is demonstrated using different scenarios of model order reduction. The design approach incorporates time delays experienced in the transmission of the SWAC's input/output signals. Issues regarding values of time delays and required level of detail in modelling time delays are thoroughly discussed. Three methods for selection of input/output signals for WAMS based damping controllers are presented and reviewed. The first method uses modal observability/controllability factors. The second method is based on the Sequential Orthogonalisation (SO) algorithm, a tool for the optimal placement of measurement devices. Its application is extended and generalised in this thesis to handle the problem of input/output signal selection. The third method combines clustering techniques and modal factor analysis. The clustering method uses advanced Principal Component Analysis (PCA) where its draw backs and limitations, in the context of power system dynamics' applications, are overcome. The methods for signal selection are compared using both small signal and transient stability analysis to determine the best optimal set of signals. Enhancement of power system stability is demonstrated by applying the proposed WAMS based control scheme on the New England test system. The multi-input multi-output (MIMO) WAMS based damping controller uses a reduced set of input/output signals and is designed using the modal LQG approach. Effectiveness of the control scheme is comprehensively assessed using both small signal and transient stability analysis for different case studies including small and large disturbances, changes in network topology and operating condition, variations in time delays, and failure of communication links.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Enhancement of power system stability using wide area measurement system based damping controller

Contemporary power networks are gradually expanding incorporating new sources of electrical energy and power electronic based devices. The major stability issue in large interconnected power systems is the lightly damped interarea oscillations. In the light of growth of their incidents there are increased concerns about the effectiveness of current control devices and control systems in maintaining power system stability. This thesis presents a Wide Area Measurement System (WAMS) based control scheme to enhance power system stability. The control scheme has a hierarchical (two-level) structure comprising a Supplementary Wide-Area Controller (SWAC) built on top of existing Power System Stabilisers (PSSs). The SWAC's focus is on stabilising the critical interarea oscillations in the system while leaving local modes to be controlled entirely by local PSSs. Both control systems in the two levels work together to maintain system stability. The scheme relies on synchronised measurements supplied by Phasor Measurement Units (PMUs) through the WAMS and the only cost requirement is for the communication infrastructure which is already available, or it will be in the near future. A novel linear quadratic Gaussian (LQG) control design approach which targets the interarea modes directly is introduced in this thesis. Its features are demonstrated through a comparison with the conventional method commonly used in power system damping applications. The modal LQG approach offers simplicity and flexibility when targeting multiple interarea modes without affecting local modes and local controllers, thus making it highly suitable to hierarchical WAMS based control schemes. Applicability of the approach to large power systems is demonstrated using different scenarios of model order reduction. The design approach incorporates time delays experienced in the transmission of the SWAC's input/output signals. Issues regarding values of time delays and required level of detail in modelling time delays are thoroughly discussed. Three methods for selection of input/output signals for WAMS based damping controllers are presented and reviewed. The first method uses modal observability/controllability factors. The second method is based on the Sequential Orthogonalisation (SO) algorithm, a tool for the optimal placement of measurement devices. Its application is extended and generalised in this thesis to handle the problem of input/output signal selection. The third method combines clustering techniques and modal factor analysis. The clustering method uses advanced Principal Component Analysis (PCA) where its draw backs and limitations, in the context of power system dynamics' applications, are overcome. The methods for signal selection are compared using both small signal and transient stability analysis to determine the best optimal set of signals. Enhancement of power system stability is demonstrated by applying the proposed WAMS based control scheme on the New England test system. The multi-input multi-output (MIMO) WAMS based damping controller uses a reduced set of input/output signals and is designed using the modal LQG approach. Effectiveness of the control scheme is comprehensively assessed using both small signal and transient stability analysis for different case studies including small and large disturbances, changes in network topology and operating condition, variations in time delays, and failure of communication links.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development and evaluation of methods for control of multiple-input multiple output systems

In control, the most common type of system is the multiple-input multiple-output(MIMO) system, where the same input may affect multiple outputs, orconversely, the same output is affected by multiple inputs. In thisthesis two methods for controlling MIMO systems are examined, namelylinear quadratic Gaussian (LQG) control and decentralized control,and some of the difficulties associated with them. One difficulty when implementing decentralized control is to decidewhich inputs should control which outputs, that is the input-outputpairing problem. There are multiple ways to solve this problem, amongthem using gramian based measures, which include the Hankel interactionindex array, the participation matrix and the Sigma2 method.These methods take into account system dynamics as opposed to manyother methods which only consider the steady-state system. However,the gramian based methods have issues with input and output scaling.Generally, this is resolved by scaling all inputs and outputs to haveequal range. However, in this thesis it is demonstrated how this canresult in an incorrect pairing. Furthermore this thesis examines othermethods of scaling the gramian based measures, using either row orcolumn sums, or by utilizing the Sinkhorn-Knopp algorithm. This thesisshows that there are considerable benefits to be gained from the alternativescaling of the gramian based measures, especially when using the Sinkhorn-Knoppalgorithm. The use of this method also has the advantage that theresults are completely independent of the original scaling of theinputs and outputs.An alternative way to control a MIMO system is to implement an LQGcontroller, which yields a single control structure for the entiresystem using a state based controller. It has been proposed that LQGcontrol can be an effective control scheme to be used on networkedcontrol systems with wireless channels. These channels have a tendencyto be unreliable with package delays and package losses. This licentiatethesis examines how to implement an LQG controller over such unreliablecommunication channels, and proposes an optimal controller which minimizesthe cost function.When new methods of control system design and analysis are introducedin the control engineering field, it is important to compare the newresults with existing methods. Often this requires application ofthe methods on examples, and for this purpose benchmark processesare introduced. However, in many areas of control engineering researchthe number of examples are relatively few, in particular when MIMOsystems are considered. For a thorough assessment of a method, however,as large number of relevant models as possible should be used. Asa remedy, a framework has been developed for generating linear MIMOmodels based on predefined system properties, such as model type,size, stability, time constants, delays etc. This MIMO generator,which is presented in this thesis, is demonstrated by using it toevaluate the previously described scaling methods for the gramianbased pairing methods

Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany