Design of gain schedule fractional PID control for nonlinear thrust vector control missile with uncertainty

The purpose of this paper is to control the trajectory of the nonlinear missile model in the pitch channel by using Fractional PID controller (FPID) and Gain Schedule Fractional PID controller (GSFPID). FPID and GSFPID with nonlinear missile model are designed where their parameters are tuned by Simulink design optimization in the Matlab toolbox. This optimization method gives the optimal parameters that achieve the best tracking with step unit reference signal. The GSFPID controller compensates the restrictions that represent physical limits of actuators in the pitch channel. The GSFPID with nonlinear missile model is designed in two phases. The first phase is the boost phase where the thrust force is maximized and the second phase is sustain phase where the thrust force is minimized. The equations of motion for nonlinear missile model with FPID and GSFPID are modelled mathematically in the Matlab-Simulink environment. The results of FPID and GSFPID controllers with the nonlinear missile model are presented and compared. The wind effect and the dynamic uncertainties effects are researched and the results are compared. The closed-loop nonlinear system is linearized by the Simulink linear analysis tool at critical operating point t = 5.8 sec and the stability is studied

Model based fault detection for two-dimensional systems

Fault detection and isolation (FDI) are essential in ensuring safe and reliable operations in industrial systems. Extensive research has been carried out on FDI for one dimensional (1-D) systems, where variables vary only with time. The existing FDI strategies are mainly focussed on 1-D systems and can generally be classified as model based and process history data based methods. In many industrial systems, the state variables change with space and time (e.g., sheet forming, fixed bed reactors, and furnaces). These systems are termed as distributed parameter systems (DPS) or two dimensional (2-D) systems. 2-D systems have been commonly represented by the Roesser Model and the F-M model. Fault detection and isolation for 2-D systems represent a great challenge in both theoretical development and applications and only limited research results are available. In this thesis, model based fault detection strategies for 2-D systems have been investigated based on the F-M and the Roesser models. A dead-beat observer based fault detection has been available for the F-M model. In this work, an observer based fault detection strategy is investigated for systems modelled by the Roesser model. Using the 2-D polynomial matrix technique, a dead-beat observer is developed and the state estimate from the observer is then input to a residual generator to monitor occurrence of faults. An enhanced realization technique is combined to achieve efficient fault detection with reduced computations. Simulation results indicate that the proposed method is effective in detecting faults for systems without disturbances as well as those affected by unknown disturbances.The dead-beat observer based fault detection has been shown to be effective for 2-D systems but strict conditions are required in order for an observer and a residual generator to exist. These strict conditions may not be satisfied for some systems. The effect of process noises are also not considered in the observer based fault detection approaches for 2-D systems. To overcome the disadvantages, 2-D Kalman filter based fault detection algorithms are proposed in the thesis. A recursive 2-D Kalman filter is applied to obtain state estimate minimizing the estimation error variances. Based on the state estimate from the Kalman filter, a residual is generated reflecting fault information. A model is formulated for the relation of the residual with faults over a moving evaluation window. Simulations are performed on two F-M models and results indicate that faults can be detected effectively and efficiently using the Kalman filter based fault detection. In the observer based and Kalman filter based fault detection approaches, the residual signals are used to determine whether a fault occurs. For systems with complicated fault information and/or noises, it is necessary to evaluate the residual signals using statistical techniques. Fault detection of 2-D systems is proposed with the residuals evaluated using dynamic principal component analysis (DPCA). Based on historical data, the reference residuals are first generated using either the observer or the Kalman filter based approach. Based on the residual time-lagged data matrices for the reference data, the principal components are calculated and the threshold value obtained. In online applications, the T2 value of the residual signals are compared with the threshold value to determine fault occurrence. Simulation results show that applying DPCA to evaluation of 2-D residuals is effective.Doctoral These

Measurements, Models, Systems and Design

531 s.

Sliding Mode Control

The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Modeling, Estimation, and Pattern Analysis of Random Texture on 3-D Surfaces

To recover 3-D structure from a shaded and textural surface image involving textures, neither the Shape-from-shading nor the Shape-from-texture analysis is enough, because both radiance and texture information coexist within the scene surface. A new 3-D texture model is developed by considering the scene image as the superposition of a smooth shaded image and a random texture image. To describe the random part, the orthographical projection is adapted to take care of the non-isotropic distribution function of the intensity due to the slant and tilt of a 3-D textures surface, and the Fractional Differencing Periodic (FDP) model is chosen to describe the random texture, because this model is able to simultaneously represent the coarseness and the pattern of the 3-D texture surface, and enough flexible to synthesize both long-term and short-term correlation structures of random texture. Since the object is described by the model involving several free parameters and the values of these parameters are determined directly from its projected image, it is possible to extract 3-D information and texture pattern directly from the image without any preprocessing. Thus, the cumulative error obtained from each pre-processing can be minimized. For estimating the parameters, a hybrid method which uses both the least square and the maximum likelihood estimates is applied and the estimation of parameters and the synthesis are done in frequency domain. Among the texture pattern features which can be obtained from a single surface image, Fractal scaling parameter plays a major role for classifying and/or segmenting the different texture patterns tilted and slanted due to the 3-dimensional rotation, because of its rotational and scaling invariant properties. Also, since the Fractal scaling factor represents the coarseness of the surface, each texture pattern has its own Fractal scale value, and particularly at the boundary between the different textures, it has relatively higher value to the one within a same texture. Based on these facts, a new classification method and a segmentation scheme for the 3-D rotated texture patterns are develope

Voltage-based droop control of converter-interfaced distributed generation units in microgrids

Sinds de laatste jaren is er in het elektrisch energienet een enorme toevloed aan kleine decentrale generatoren, vaak op basis van hernieuwbare energiebronnen. De distributienetten werden echter niet gebouwd om injectie van energie toe te laten. Hierdoor komen steeds meer problemen in de distributienetten voor, zoals bijvoorbeeld overspanningen tijdens zonnige periodes. Dit bemoeilijkt de verdere integratie van hernieuwbare energiebronnen. In deze context werd het microgrid concept voorgesteld om een gecoordineerde koppeling van decentrale generatoren in het net mogelijk te maken. Microgrids zijn kleine subnetten die lokaal hun elementen, zoals de generatoren en de lasten regelen om bepaalde doeleinden te bereiken. Ze kunnen bijvoorbeeld de spanningsregeling in hun net verzorgen of als een geheel meespelen in de energiemarkten. Een karakteristiek van microgrids is dat ze onafhankelijk van het net kunnen werken, in het zogenaamde eilandbedrijf. In eilandbedrijf moeten het verbruik en de opwekking op ieder tijdstip op elkaar afgesteld zijn. Aangezien microgrids erg verschillende eigenschappen hebben van het gewone elektrisch net, zijn hier specifieke regelstrategieen voor vereist. In deze doctoraatsverhandeling wordt een dergelijke regelstrategie uitgewerkt, de zogenaamde spanningsgebaseerde droop (proportionele) regeling. Het spanningsniveau wordt als de niet-conventionele parameter gebruikt om het microgrid te regelen