Dissertations / Theses on the topic 'Automatic Identification System'

A model is developed for the Quanser torsion system available at Control Systems Research Laboratory at Chulalongkorn University. The torsion system is a laboratory equipment that is designed for the study of position control. It consists of a DC motor that drives three inertial loads that are coupled in series with the motor, and where all components are coupled to each other through torsional springs.

Several nonlinearities are observed and the most significant one is an offset in the input signal, which is compensated for. Experiments are carried out under feedback as the system is marginally stable. Different input signals are tested and used for system identification. Linear black-box state-space models are then identified using PEM, N4SID and a subspace method made for closed-loop identification, where the last two are the most successful ones. PEM is used in a second step and successfully enhances the parameter estimates from the other algorithms.

In the system identification community a popular framework for the problem of estimating a parametrized model structure given a sequence of input and output pairs is given by the prediction-error method. This method tries to find the parameters which maximize the prediction capability of the corresponding model via the minimization of some chosen cost function that depends on the prediction error. This optimization problem is often quite complex with several local minima and is commonly solved using a local search algorithm. Thus, it is important to find a good initial estimate for the local search algorithm. This is the main topic of this thesis.

The first problem considered is the regressor selection problem for estimating the order of dynamical systems. The general problem formulation is difficult to solve and the worst case complexity equals the complexity of the exhaustive search of all possible combinations of regressors. To circumvent this complexity, we propose a relaxation of the general formulation as an extension of the nonnegative garrote regularization method. The proposed method provides means to order the regressors via their time lag and a novel algorithmic approach for the \textsc{arx} and \textsc{lpv-arx} case is given.

Thereafter, the initialization of linear time-invariant polynomial models is considered. Usually, this problem is solved via some multi-step instrumental variables method. For the estimation of state-space models, which are closely related to the polynomial models via canonical forms, the state of the art estimation method is given by the subspace identification method. It turns out that this method can be easily extended to handle the estimation of polynomial models. The modifications are minor and only involve some intermediate calculations where already available tools can be used. Furthermore, with the proposed method other a priori information about the structure can be readily handled, including a certain class of linear gray-box structures. The proposed extension is not restricted to the discrete-time case and can be used to estimate continuous-time models.

The final topic in this thesis is the initialization of discrete-time systems containing polynomial nonlinearities. In the continuous-time case, the tools of differential algebra, especially Ritt's algorithm, have been used to prove that such a model structure is globally identifiable if and only if it can be written as a linear regression model. In particular, this implies that once Ritt's algorithm has been used to rewrite the nonlinear model structure into a linear regression model, the parameter estimation problem becomes trivial. Motivated by the above and the fact that most system identification problems involve sampled data, a version of Ritt's algorithm for the discrete-time case is provided. This algorithm is closely related to the continuous-time version and enables the handling of noise signals without differentiations.

There are many aspects to consider when designing system identification experiments in control applications. Input design is one important issue. This thesis considers input design both for identification of linear time-invariant models and for stability validation.

Models obtained from system identification experiments are uncertain due to noise present in measurements. The input spectrum can be used to shape the model quality. A key tool in input design is to introduce a linear parametrization of the spectrum. With this parametrization a number of optimal input design problems can be formulated as convex optimization programs. An Achilles' heel in input design is that the solution depends on the system itself, and this problem can be handled by iterative procedures where the input design is based on a model of the system. Benefits of optimal input design are quantified for typical industrial applications. The result shows that the experiment time can be substantially shortened and that the input power can be reduced.

Another contribution of the thesis is a procedure where input design is connected to robust control. For a certain system structure with uncertain parameters, it is shown that the existence of a feedback controller that guarantees a given performance specification can be formulated as a convex optimization program. Furthermore, a method for input design for multivariable systems is proposed. The constraint on the model quality is transformed to a linear matrix inequality using a separation of graphs theorem. The result indicates that in order to obtain a model suitable for control design, it is important to increase the power of the input in the low-gain direction of the system relative to the power in the high-gain direction.

A critical issue when validating closed-loop stability is to obtain an accurate estimate of the maximum gain of the system. This problem boils down to finding the input signal that maximizes the gain. Procedures for gain estimation of nonlinear systems are proposed and compared. One approach uses a model of the system to design the optimal input. In other approaches, no model is required, and the system itself determines the optimal input sequence in repeated experiments.

The aim of this thesis is to develop the knowledge about nonlinear and/or adaptive solid fuel boiler control at Vattenfall Utveckling AB. The aim is also to make a study of implemented and published control strategies.

A solid fuel boiler is a large-scale heat (and power) generating plant. The Idbäcken boiler studied in this work, is a one hundred MW furnace mainly fired with wood chips. The control system consists of several linear PID controllers working together, and the furnace is a nonlinear system. That, and the fact that the fuel-flow is not monitored, are the main reasons for the control problems. The system fluctuates periodically and the CO outlets sometimes rise high above the permitted level.

There is little work done in the area of advanced boiler control, but some interesting approaches are described in scientific articles. MPC (Model Predictive Control), nonlinear system identification using ANN (Artificial Neural Network), fuzzy logic, Hµ loop shaping and MIMO (Multiple Input Multiple Output) PID tuning methods have been tested with good results.

Both linear and nonlinear system identification is performed in the thesis. The linear models are able to explain about forty percent of the system behavior and the nonlinear models explain about sixty to eighty percent. The main result is that nonlinear models improve the performance and that there are considerable disturbances complicating the identification. Another identification issue was the feedback during the data collection.

The trend today is to use many inexpensive sensors instead of a few expensive ones, since the same accuracy can generally be obtained by fusing several dependent measurements. It also follows that the robustness against failing sensors is improved. As a result, the need for high-dimensional regression techniques is increasing.

As measurements are dependent, the regressors will be constrained to some manifold. There is then a representation of the regressors, of the same dimension as the manifold, containing all predictive information. Since the manifold is commonly unknown, this representation has to be estimated using data. For this, manifold learning can be utilized. Having found a representation of the manifold constrained regressors, this low-dimensional representation can be used in an ordinary regression algorithm to find a prediction of the output. This has further been developed in the Weight Determination by Manifold Regularization (WDMR) approach.

In most regression problems, prior information can improve prediction results. This is also true for high-dimensional regression problems. Research to include physical prior knowledge in high-dimensional regression i.e., gray-box high-dimensional regression, has been rather limited, however. We explore the possibilities to include prior knowledge in high-dimensional manifold constrained regression by the means of regularization. The result will be called gray-box WDMR. In gray-box WDMR we have the possibility to restrict ourselves to predictions which are physically plausible. This is done by incorporating dynamical models for how the regressors evolve on the manifold.

This report presents work done in the field of linear and nonlinear system identification on robots. The subject of study has been a new parallel manipulator called Gantry-Tau. The work shall be seen as one of the first steps in the dynamical analysis of the robot. All practical work presented in the report was conducted on a prototype situated at University of Queensland.

The actuators have been analysed and modelled with the aim to gain knowledge of weaknesses and dynamical behaviour. The analysis resulted in a study of nonlinear grey-box identification of hysteresis in the drive train of the actuators. A very compact nonlinear hysteresis model was used together with a three-step identification procedure. The results show that a model of the nonlinear system can be successfully identified from measurement data.

Finally a method for estimation of parameters in the model for the inverse dynamics of the leg structure has been investigated. It turns out that the investigated method is not able to give accurate estimates. This is thought to be a result of unmodelled behaviour in the system and noisy data.

Inom processindustrin finns en etablerad reglerhierarki där basreglering sker med PID-regulatorer och där avancerad, multivariabel styrning sköts av MPC-programvara. Steget mellan dessa två nivåer kan upplevas som stort. För mindre och snabba multivariabla processer undvikes helst en multivariabel ansats, med försämrad reglering som följd. På Preem AB har detta upplevts som ett problem. Syftet med examensarbetet har varit att utveckla en alternativ, multivariabel styrstrategi för en process med ett mindre antal reglerstorheter som interagerar. Detta har gjorts genom en utveckling av en LQG-regulator i styrsystemet DeltaV.

För att implementera en regulator i ett styrsystem måste hänsyn tas till en rad faktorer, såsom hantering av olika körlägen, bortfall av signaler, integratoruppvridning, kommunikation med slavregulatorer och inte minst operatörernas gränssnitt för hantering av regulatorn. Att sedan utveckla en regulator för en process kräver bland annat stegförsök, analys och anpassning av stegtestdata, modellidentifiering, framtagning av trimningskonstanter, testning av styrstrategi i simulerad miljö och idrifttagning. Den typen av frågeställningar addresseras i rapporten.

Examensarbetet visar att det finns en plats för LQG-regulatorn i processindustrin för en viss typ av problem. Den utvecklade regulatorn har implementerats på en avsvavlingsprocess på Preems oljeraffenaderi i Lysekil med lyckat resultat. Oscillationer i processen, som tidvis påverkat produktionen av propen, har kunnat reduceras.

Process control in process industry is done in a hierarchy in which PID controllers are used for basic control and MPC software is used for advanced, multivariable process control. The implementation of multivariable control using MPC software is a major undertaking and development of such controllers for small and fast multivariable processes is therefore avoided. To achieve better control for such processes, a simpler approach to multivariable control is often sought. The purpose of this masters thesis is to develop an alternative, multivariable control strategy for processes with a smaller number of interacting control variables. This is achieved by developing an LQG-controller in the DCS DeltaV at Preem AB.

Implementation of such a controller in a DCS requires that consideration is given to a number of factors, including handling of different modes, loss of signals, reset windup, communication with slave controllers and construction of operator interface. To develop a controller for a specific process also requires step testing, model identification, tuning of the controller parameters, simulation of the control strategy and commissioning. Solutions to such issues are addressed in this report.

The thesis shows that  LQG-controllers can be useful in process industry for some niche applications. The LQG-controller has successfully been applied to a desulphurisation process at Preem's oil refinery in Lysekil, where oscillations affecting the production of propylene have been reduced.

The last years a TC-sensing module called HSS-440 has been developed at AppliedSensor. The sensor is used in hydrogen powered cars to detect H2-leakages. TC-sensing is a technique that uses small changes in thermal conductivity when H2 is present to determine concentrations. Today these small changes are estimated with a prediction model that uses several hundreds of parameters.

A sensor substrate from a new manufacturer is now introduced. This means an opportunity to look over the current solution. The task for this thesis is to investigate system properties and new solutions regarding a prediction model with minimal need for calibration.

System properties are investigated and relations for heat flow and influence of H2 are established. In the process an earlier not known nonlinearity are proved to exist. From this, a new open loop nonlinear greybox model is estimated and the nonlinearity are concluded to improve the model. The model is then closed with an earlier implemented PI-regulator and concluded to be useful for H2-predictions. The new model also utilizes 11 parameters instead of hundreds which is a big improvement.

Sista åren har en sensor, med beteckningen HSS-440, mätandes Termisk konduktivitet utvecklats på AppliedSensor. Sensorn används för att upptäcka läckage av H2-gas i vätgasdrivna bilar. Vid Termisk Konduktivitets mätning används små förändringar av den termiska konduktiviteten, då H2 är närvarande i det omgivande mediumet, som ett mått på koncentrationen. Idag änvänder prediktionsmodellen flera hundra parametrar för att skatta denna koncentration.

Nu introduceras ett sensorsubstrat från en ny tillverkare, vilket innebär ett bra tillfälle att se över den gamla lösningen. Syftet med examensarbetet är därför att undersöka nya systemegenskaper i och med introduktionen av det nya sensorsubstratet samt nya lösningar på en prediktionsmodel med ett minimalt behov av kalibrering.

Systemegenskaperna undersöks och samband för värmeflöden och H2's påverkan på systemet fastställs. Vid denna undersökning upptäcks en tidigare okänd olinjäritet. Utifrån detta bestämms en ny olinjär greybox modell där den nyfunna olinjäriteten bevisas förbättra modellen. Modellen sluts med en tidigare implementerade PI-regulator och bevisas vara användbar vid H2-prediktion. Den nya modellen använder även bara 11 parametrar istället för flera hundra vilket är en stor förbättring.

Robotic manipulators have been introduced in industry as a form of increasing productivity. Today, there exist an interest to enlarge the application of these manipulators to outdoor environments. Forestry cranes used in the forestry industry are a clear example. A long term goal in this industry is the development of autonomous systems to increase the logging efficiency. In this thesis, we consider how to control the rotator of these cranes, which is an electro-hydraulically actuated motor, and is used to control the end ffector tool. Control system design for the rotator is a challenging task since the sensing is not available to full extent. The main reason is the harsh environment that these machines are exposed to and sensors such as encoders are very fragile and cannot be used. In this thesis we use alternative sensing devices, such as a magnetic sensor and a stereo camera. In the case of the camera we face a problem with big delay. A prediction method has been used to compute desired values. Due to various reasons certain measuring devices can not be used in the industry. We consider four cases for control system design where dfferent combinations of available sensors have been used. Initially angular position of the rotator is controlled using only the magnetic sensor. A cascade control setup is used where pressure and position are measurable, first using the magnetic sensor and later using the camera. When only pressure measurements are available identified models have replaced sensors for position feedback. All tests and experiments have been done using a scaled version of a real forestry crane. The available crane has similar configuration and dynamics as the real one and is therefor useful for experimental purposes.

Direkt viktoptimering (Direct Weight Optimization, DWO) är en ickeparamterisk systemidentifieringsmetod. DWO bygger på att man skattar ett funktionsvärde i en viss punkt genom en viktad summa av mätvärden, där vikterna optimeras fram. Det faktum att DWO har en inparameter som man måste veta i förväg leder till att man på något sätt vill skatta denna inparameter. Det finns många sätt man kan göra denna skattning på men det centrala i denna uppsats är att skatta inparametern lokalt. Fördelen med detta är att metoden anpassar sig om till exempel systemet ändrar beteende från att variera långsamt till att variera snabbare. Denna typ av metoder brukar kallas adaptiva metoder.Det finns flera metoder för att skatta en inparameter lokalt och anpassningen till DWO är redan klar för ett fåtal som lämpar sig bra. Det är dock inte undersökt vilken av dessa metoder som ger det bästa resultatet för just DWO. Syftet med denna uppsats är alltså att ta reda på hur man lokalt kan skatta en inparameter till DWO på bästa sätt och om DWO är en bra grund att basera en adaptiv metod på.Det har visat sig att DWO kanske är för känslig för en lokalt vald inparameter för att vara en bra grund att basera en adaptiv metod på. Däremot utmärker sig en av metoderna för att skatta inparametern genom att vara mycket bättre än de andra metoderna när den kanske inte borde vara det. Varför den är så bra kan vara ett bra ämne för vidare forskning.

Direct Weight Optimization (DWO) is a nonparametric system identification meth\-od. In DWO the value of a function in a certain point is estimated by a weighted sum of measured values. The weights are obtained as a solution to a convex optimization problem. DWO has a design parameter which has to be chosen or estimated a priori. There are many ways to estimate this parameter. The main focus of this thesis is to estimate this parameter locally. The advantage of estimating the parameter locally is that the estimate will adapt if the system changes behavior from slowly varying to rapidly varying. Estimation methods of this type are usually called adaptive estimation methods.There are a number of adaptive estimation methods and the adaptation of some of these methods to DWO has already been done. There are however no evaluation studies done. The goal with this thesis is therefore to find out how to estimate the parameter in DWO in the best way and to find out whether DWO is a good base for an adaptive method.It turned out that DWO might be too sensitive to local changes in the design parameter to be a good base for an adaptive method. However, one of the adaptive estimation methods stands out from the rest because it is much better than the other methods when it, perhaps, should not. Why this method is good might be a good subject for further research.

PolyTech AB in Malmköping manufactures gyro stabilized camera systems or helicopter applications. In this Master´s Thesis a shaker test rig for vibration testing of these systems is designed, implemented and evaluated. The shaker is required to have an adjustable frequency and displacement and different shakers that meet these requirements are treated in a literature study.

The shaker chosen in the test rig is based on a mechanical solution that is described in detail. Additionally all components used in the test rig are described and modelled. The test rig is identified and evaluated from different experiments carried out at PolyTech, where the major part of the identification is based on data collected from accelerometers.

The test rig model is used to develop a controller that controls the frequency and the displacement of the shaker. A three-phase motor is used to control the frequency of the shaker and a linear actuator with a servo is used to control the displacement. The servo controller is designed using observer and state feedback techniques.

Additionally, the mount in which the camera system is hanging is modelled and identified, where the identification method is based on nonlinear least squares (NLS) curve fitting technique.

Predictive control relies on predictions of the future behaviour of the system to be controlled. These predictions are calculated from a model of this system, thus making the model the cornerstone of the predictive controller. Furthermore predictive control is the only advanced control methodology that has managed to become widely used in the industry. The necessity of good models in the predictive control context can thus be motivated both from the very nature of predictive control and from its widespread use in industry.

This thesis is concerned with examining the use of multiple models in the predictive controller. In order to do this the standard predictive control formulation has been extended to incorporate the use of multiple models. The most general case of this new formulation allows the use of an individual model for each prediction horizon.

The models are estimated using measurements of the input and output sequences from the true system. When using this data to find a good model of the system it is important to remember the intended purpose of the model. In this case the model is going to be used in a predictive controller and the most important feature of the models is to deliver good k-step ahead predictions. The identification algorithms used to estimate the models thus strives for estimating models good at calculating these predictions.

Finally this thesis presents some complete simulations of these ideas showing the potential of using multiple models in the predictive control framework.

Information about dynamic tire properties has always been important for drivers of wheel driven vehicles. With the increasing amount of systems in modern vehicles designed to measure and control the behavior of the vehicle information regarding dynamic tire properties has grown even more important.

In this thesis a number of methods for modeling and estimating dynamic tire properties have been implemented and evaluated. The more general issue of estimating model parameters in linear and non-linear vehicle models is also addressed.

We conclude that the slope of the tire slip curve seems to dependent on the stiffness of the road surface and introduce the term combined stiffness. We also show that it is possible to estimate both longitudinal and lateral combined stiffness using only standard vehicle sensors.