This thesis investigates the capabilities of the Collapse Prediction Relay (CPR-D) and also investigates the use of system identification for detection of oscillatory instability. Both the CPR-D and system identification are based on system measurements and do not require modelling of the power system. Measurement based stability monitors can help to avoid instability and blackouts, in cases where the available system model can not predict instability. The CPR-D uses frequency patterns in voltage oscillation to detect system instability. The relay is based on non-linear dynamics Theory. If a collapse is predicted, measures could be taken to prevent a blackout. The relay was tested using the output of simulators and was later installed in a substation. The data from laboratory tests and site installations is analysed enabling a detailed evaluation of the CPR-D.Oscillatory instability can be detected by monitoring the damping ratio of oscillations in the power system. Poor damping indicates a smaller stability margin. Subspace identification is used to estimate damping ratios. The method is tested under different conditions and using several power system models. The results show that using several measurements gives more accurate estimates and requires shorter data windows. A selection method for measurements is proposed in the thesis.

This thesis proposes novel methods for identification and control of multivariable systems including multiple delays and describes their application to control of general anesthesia administration. First, an identification method for multivariable systems whose input and output paths have different time delays is presented. Second, a state predictor for multivariable systems whose input and output paths have different time delays is proposed. Third, the state predictor is used for constructing a state-predictive servo control system for controlled processes whose output paths have different time delays. A robust stability analysis method of the state-predictive servo control system is also examined. Furthermore, based on results of these theoretical studies, control systems for use in general anesthesia administration are developed. First, an identification method for multivariable systems whose input and output paths have different time delays is proposed. This method comprises two steps. In the first step, the delay lengths are estimated from the impulse response matrix identified from input and output (I/O) sequences using a subspace identification algorithm. In the second step, I/O sequences of a delay-free part are constructed from the original sequences and the delay estimates, and the system matrices of the delay-free part are identified. The proposed method is numerically stable and efficient. Moreover, it requires no complex optimization to obtain the delay estimates, nor does it require an assumption about the structure of the system matrices. Second, a state predictor is proposed for multivariable systems whose input and output paths have different time delays. The predictor consists of a full-order observer and a prediction mechanism. The former estimates a vector consisting of past states from the output. The latter predicts the current state from the estimated vector. The prediction error converges to zero at an arbitrary rate, which can be determined using pole assignment method, etc. In the proposed predictor, the interval length of the finite interval integration fed to the observer is shorter than that of an existing delay-compensating observer. Consequently, the proposed predictor is more numerically accurate than the delay-compensating observer. Using the proposed state predictor, a design method of a state-predictive servo controller is described for multivariable systems whose output paths have different time delays. Furthermore, a sufficient stability condition of the state-predictive servo control system against parameter mismatches is derived. Using a characteristic equation of the perturbed closed-loop system, a stability margin can be given on a plane whose axes correspond to the magnitudes of the mismatches on system matrices and on delay lengths. In the remainder of this thesis, development of anesthesia control systems is described to illustrate an application of the theoretical results described above. First, a hypnosis control system is presented. This system administers an intravenous hypnotic drug to regulate an electroencephalogram-derived index reflecting the patient’s hypnosis. The system comprises three functions: i) a model predictive controller that can take into account effects of time delay adequately, ii) an estimation function of individual parameters, and iii) a risk-control function for preventing undesirable states such as drug over-infusion or intra-operative arousal. Results of 79 clinical trials show that the system can reduce the total amount of drug infusion and maintain hypnosis more accurately than an anesthesiologist’s manual adjustment. Second, a simultaneous control system of hypnosis and muscle relaxation is described. For development of this system, a multivariable model of hypnosis and muscle relaxation is identified using the method proposed in this thesis. Then a state-predictive servo control system is designed for controlling hypnosis and muscle relaxation. Finally, the control system’s performance is evaluated through simulation. The resultant simultaneous control system satisfies the performance specifications of settling time, disturbance rejection ability, and a robust stability range. Although this system is not fully developed, the procedure of constructing this control system demonstrates the effectiveness of the proposed methods: the identification method for systems whose input and output paths have different time delays and the design and stability analysis methods of the state-predictive servo control system. / Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第13820号 / 工博第2924号 / 新制||工||1432(附属図書館) / 26036 / UT51-2008-C736 / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 小林 哲生, 教授 萩原 朋道, 准教授 古谷 栄光 / 学位規則第4条第1項該当

This research is related to the control of energy consumption and efficiency in building Heating Ventilation and Air Conditioning (HVAC) systems and is primarily concerned with controlling the function of heating. The main goal of this thesis is to develop a control system that can achieve the following two main control functions: a) detection of unexpected indoor conditions that may result in unnecessary power consumption and b) energy efficiency control regarding optimal balancing of two parameters: the required energy consumption for heating, versus thermal comfort of the occupants. Methods of both orientations were developed in a multi-zone space composed of nine zones where each zone is equipped with a wireless node consisting of temperature and occupancy sensors while all the scattered nodes together form a wireless sensor network (WSN). The main methods of both control functions utilize the potential of the deterministic subspace identification (SID) predictive model which provides the predicted temperature of the zones. In the main method for detecting unexpected situations that can directly affect the thermal condition of the indoor space and cause energy consumption (abnormal situations), the predictive temperature from the SID model is compared with the real temperature and thus possible temperature deviations that indicate unexpected situations are detected. The method successfully detects two situations: the high infiltration gain due to unexpected cold air intake from the external surroundings through potential unforeseen openings (windows, exterior doors, opened ceilings etc) as well as the high heat gain due to onset of fire. With the support of the statistical algorithm for abrupt change detection, Cumulative Sum (CUSUM), the detection of temperature deviations is accomplished with accuracy in a very short time. The CUSUM algorithm is first evaluated at an initial approach to detect power diversions due to the above situations caused by the aforementioned exogenous factors. The predicted temperature of the zone from the SID model utilized appropriately also by the main method of the second control function for energy efficiency control. The time needed for the temperature of a zone to reach the thermal comfort zone threshold from a low initial value is measured by the predicted temperature evolution, and this measurement bases the logic of a control criterion for applying proactive heating to the unoccupied zones or not. Additional key points for the control criterion of the method is the occupation time of the zones as well as the remaining time of the occupants in the occupied zones. Two scenarios are examined: the first scenario with two adjacent zones where the one is occupied and the other is not, and the second scenario with a multi-zone space where the occupants are moving through the zones in a cascade mode. Gama and Pareto probability distributions modeled the occupation times of the two-zone scenario while exponential distribution modeled the cascade scenario as the least favorable case. The mobility of the occupants modeled with a semi-Markov process and the method provides satisfactory and reasonable results. At an initial approach the proactive heating of the zones is evaluated with specific algorithms that handle appropriately the occupation time into the zones.

This thesis proposes new algorithms for a group of sensing robots to learn a para- metric model for a dynamic spatio-temporal field, then based on the learned model trajectories are planned for sensing robots to best estimate the field. In this thesis we call these two parts learning and monitoring, respectively. For the learning, we first introduce a parametric model for the spatio-temporal field. We then propose a family of motion strategies that can be used by a group of mobile sensing robots to collect point measurements about the field. Our motion strategies are designed to collect enough information from enough locations at enough different times for the robots to learn the dynamics of the field. In conjunction with these motion strategies, we propose a new learning algorithm based on subspace identification to learn the parameters of the dynamical model. We prove that as the number of data collected by the robots goes to infinity, the parameters learned by our algorithm will converge to the true parameters. For the monitoring, based on the model learned from the learning part, three new informative trajectory planning algorithms are proposed for the robots to collect the most informative measurements for estimating the field. Kalman filter is used to calculate the estimate, and to compute the error covariance of the estimate. The goal is to find trajectories for sensing robots that minimize a cost metric on the error covariance matrix. We propose three algorithms to deal with this problem. First, we propose a new randomized path planning algorithm called Rapidly-exploring Random Cycles (RRC) and its variant RRC* to find periodic trajectories for the sensing robots that try to minimize the largest eigenvalue of the error covariance matrix over an infinite horizon. The algorithm is proven to find the minimum infinite horizon cost cycle in a graph, which grows by successively adding random points. Secondly, we apply kinodynamic RRT* to plan continuous trajectories to estimate the field. We formulate the evolution of the estimation error covariance matrix as a differential constraint and propose extended state space and task space sampling to fit this problem into classical RRT* setup. Thirdly, Pontryagin’s Minimum Principle is used to find a set of necessary conditions that must be satisfied by the optimal trajectory to estimate the field. We then consider a real physical spatio-temporal field, the surface water temper- ature in the Caribbean Sea. We first apply the learning algorithm to learn a linear dynamical model for the temperature. Then based on the learned model, RRC and RRC* are used to plan trajectories to estimate the temperature. The estimation performance of RRC and RRC* trajectories significantly outperform the trajectories planned by random search, greedy and receding horizon algorithms.

We examine the related nature of two identification algorithms, subspace identification (SID) and Dynamic Mode Decomposition (DMD), and their correctness properties over a broad range of problems. This investigation begins by noting the strong relationship between the two algorithms, both drawing significantly on the pseudoinverse calculation using singular value decomposition, and ultimately revealing that DMD can be viewed as a substep of SID. We then perform extensive computational studies, characterizing the performance of SID on problems of various model orders and noise levels. Specifically, we generate 10,000 random systems for each model order and noise level, calculating the average identification error for each case, and then repeat the entire experiment to ensure the results are, in fact, consistent. The results both quantify the intrinsic algorithmic error at zero-noise, monotonically increasing with model complexity, as well as demonstrate an asymptotically linear degradation to noise intensity, at least for the range under study. Finally, we close by demonstrating DMD's ability to recover system matrices, because its access to full state measurements makes them identifiable. SID, on the other hand, can't possibly hope to recover the original system matrices, due to their fundamental unidentifiability from input-output data. This is true even when SID delivers excellent performance identifying a correct set of equivalent system matrices.

The proposed research is devoted to devising system identification and fault detection approaches and algorithms for a system characterized by nonlinear dynamics. Mathematical models of dynamical systems and fault models are built based on observed data from systems. In particular, we will focus on statistical subspace instrumental variable methods which allow the consideration of an appealing mathematical model in many control applications consisting of a nonlinear feedback system with nonlinearities at both inputs and outputs. Different solutions within the proposed framework are presented to solve the system identification and fault detection problems. Specifically, Augmented Subspace Instrumental Variable Identification (ASIVID) approaches are proposed to identify the closed-loop nonlinear Hammerstein systems. Then fast approaches are presented to determine the system order. Hard-over failures are detected by order determination approaches when failures manifest themselves as rank deficiencies of the dynamical systems. Geometric interpretations of subspace tracking theorems are presented in this dissertation in order to propose a fault tolerance strategy. Possible fields of application considered in this research include manufacturing systems, autonomous vehicle systems, space systems and burgeoning bio-mechanical systems.

In the modern dynamic engineering field, experimental dynamics is an important area of study. This area includes structural dynamics, structural control, and structural health monitoring. In experimental dynamics, methods to obtain measured data have seen a great influx of research efforts to develop an accurate and reliable experimental analysis result. A technical challenge is the procurement of informative data that exhibits the desired system information. In many cases, the number of sensors is limited by cost and difficulty of data archive. Furthermore, some informative data has technical difficulty when measuring input force and, even if obtaining the desired data were possible, it could include a lot of noise in the measuring data. As a result, researchers have developed many analytical tools with limited informative data. Subspace identification method is used one of tools in these achievements. Subspace identification method includes three different approaches: Deterministic Subspace Identification (DSI), Stochastic Subspace Identification (SSI), and Deterministic-Stochastic Subspace Identification (DSSI). The subspace identification method is widely used for fast computational speed and its accuracy. Based on the given information, such as output only, input/output, and input/output with noises, DSI, SSI, and DSSI are differently applied under specific assumptions, which could affect the analytical results. The objective of this study is to observe the effect of assumptions on subspace identification with various data conditions. Firstly, an analytical simulation study is performed using a sixdegree-of-freedom mass-damper-spring system which is created using MATLAB. Various conditions of excitation insert to the simulation test model, and its excitation and response are iv analyzed using the subspace identification method. For stochastic problems, artificial noise is contained to the excitation and followed the same steps. Through this simulation test, the effects of assumption on subspace identification are quantified. Once the effects of the assumptions are studied using the simulation model, the subspace identification method is applied to dynamic response data collected from large-scale 12-story buildings with different foundation types that are tested at Tongji University, Shanghai, China. Noise effects are verified using three different excitation types. Furthermore, using the DSSI, which has the most accurate result, the effect of different foundations on the superstructure are analyzed.

A formulation of a subspace identification method for multi-phase processes with applications to rotational moulding and suggestions for improvements and experimental applications. / This thesis focuses on the implementation of subspace identification (SSID) for nonlinear, chemical batch processes by introducing a model identification method for multi-phase processes. In this thesis, a multi-phase process refers to chemical or biological batch-like processes with properties that cause a change in the dynamics during the evolution of the process. This can occur, for example, when a process undergoes a change of state upon reaching a melting point. Existing SSID techniques are not designed to utilize any known, multiphase nature of a process in the model identification stage. The proposed approach, Multiphase Subspace Identification (MPSSID), is conducted by first splitting historical data into phases during the identification step and then building a subspace model for each phase. The phases are then connected via a partial least squares (PLS) model that transforms the states from one phase to the next. This approach makes use of existing SSID techniques that allow for model construction using batches of nonunifrom length. Here, MPSSID is applied to a uniaxial rotational moulding process. In rotational moulding, the dynamics switch as the process undergoes heating, melting, and sintering stages that are visibly distinct and recognizable upon a certain temperature (not time) being reached. Results demonstrate the ability of multiphase models to better predict the temperature trajectories and final product quality of validation batches. As an extension to this rotational moulding analysis, additional MPSSID methods of implementation are proposed and the results are compared. A MPSSID mixed integer linear program is then introduced for implementation within model predictive control. The applications to rotational moulding are presented within the context of plastics manufacturing and the impact of plastic on the global climate crisis, with suggestions for future work. / Thesis / Master of Applied Science (MASc) / The control of chemical processes is an important factor in achieving high quality products. To control a process well, the mathematical model of the system must be accurate. In the past, mathematical models for process control were designed based on engineering approximations. Now, with major advances in computing and sensor technology, it is possible to design a simulation of the entire process. These simulations can be designed using first-principles or black box approaches. First-principles approaches utilize rigorous models that are based on the complex chemical and physical formulas that govern a system. Black box approaches do not look at the first-principles dynamics. They only utilize the measured process inputs and outputs to form a model of the system. They are widely used because of their ease of implementation in comparison to first-principles approaches. In this thesis, a new black box process control model is proposed and is found to yield better theoretical results than existing techniques. This model is tested on data from a plastics manufacturing process called rotational moulding, which involves loading polymer powders into a mould that is simultaneously rotated and heated to yield seamless plastic parts. Lastly, a control framework that is compatible with the new black box model is proposed to be used for future experimental tests.

Data-driven model predictive control framework of semicontinuous distillation process / Distillation technology is one of the most sought-after operations in the chemical process industries. Countless research has been done in the past to reduce the cost associated with distillation technology. As a result of process intensification, a semicontinuous distillation system is proposed as an alternative for purifying the n-component mixture (n>=3) which has the advantage over both batch and continuous process for low to medium production rates. A traditional distillation setup requires n-1 columns to separate the components to the desired purity. However, a semicontinuous system performs the same task by integrating a distillation column with n-2 middle vessel (storage tank). Consequently, with lower capital cost, the total annualized cost (TAC) per tonne of feed processed is less for a semicontinuous system compared to a traditional setup for low to medium throughput. Yet, the operating cost of a semicontinuous system exceed those of the conventional continuous setup. Semicontinuous system exhibits a non-linear dynamic behavior with a cyclic steady state and has three modes of operation. The main goal of this thesis is to reduce the operating cost per tonne of feed processed which leads to lower TAC per tonne of feed processed using a model predictive control (MPC) scheme compared to the existing PI configuration This work proposes a novel multi-model technique using subspace identification to identify a linear model for each mode of operation without attaining discontinuity. Subsequently, the developed multi-model framework was implemented in a shrinking horizon MPC architecture to reduce the TAC/tonne of feed processed while maintaining the desired product purities at the end of each cycle. The work uses Aspen Plus Dynamics simulation as a test bed to simulate the semicontinuous system and the shrinking horizon MPC scheme is formulated in MATLAB. VBA is used to communicate the inputs from MPC in MATLAB to the process in Aspen Plus Dynamics. / Thesis / Master of Science in Chemical Engineering (MSChE)

Third submission of my master thesis, first one didn't finish for some reason, the second I think I forgot to include my name. / This thesis focuses on the creation of new parallel hybrid model designs for prediction and control in batch and fed-batch reactors within Model Predictive Control (MPC) frameworks. In the hybrid model, the first principle (FP) explains the dynamics and the residual Subspace Identification (SID) model explains the error between the FP and the process. Modifications to the structure of the hybrid model are motivated by limitations of MPC frameworks. MPCs need accurate models to explain the system dynamics to make informed control decisions, and mechanistic models can be difficult to implement due to challenges of solving the optimization problem in real time. Two tools are demonstrated to help solve these problems. The first tool, Residual First Principle 0 Hybrid (RFP0H) model, helps to deal with the intractability of a mechanistic model in a hybrid modelling framework. The input for the FP model is kept constant and the SID predicts the error between the first principle and the process. Allowing for the desired output to be subtracted by the predicted FP to create a desired error value. Thus, MPC control only needs to be solved using the linear SID model in a linear or quadratic framework. Making a potentially intractable problem, tractable in MPC. This is demonstrated using a simulated fed-batch crystallization process. The second tool, Scaling Factor First Principle 0 Hybrid (SFFP0H) model, modifies the hybrid model structure to multiple the sub-models’ outputs together. The SID data driven model predicts a factor to scale the FP output for the process prediction. The results demonstrate that the SFFP0H model has increased predictive ability and has smaller variability in control compared to the RFP0H model. Helping to solve the problem of needing accurate models within an MPC formulation. This is demonstrated by using a laboratory scale batch polymerization process. / Thesis / Master of Applied Science (MASc)