Measurements Of State Variables Research Articles

Synthesis of a Control System for a Waste Heat Boiler with Forced Circulation under Restrictions on Control Actions

As is often the case in practice, classical control theory does not fully provide sufficient tools for solving applied problems of a certain specific class. Such problems include automatic control of waste heat boilers where physical restrictions are imposed on controls, namely, they must belong to the class of non-negative functions with restrictions on amplitude and growth rate. In the theoretical part of this paper, within the framework of the block approach, methods for the synthesis of general linear systems with one-sided restrictions on control actions and state variables were developed. Then, the developed algorithms were applied to the waste heat boiler control system under conditions of incomplete measurements of state variables and the action of parametric and external uncontrolled disturbances. The effectiveness of the proposed algorithms was confirmed by numerical simulation.

The Impact of Sensor Errors on Flight Stability

Sensors play a significant role in flight control systems. The accuracy of the measurements of state variables affects the quality and effectiveness of flight stabilization. When designing closed-loop systems, it is desirable to use sensors of the highest class and reliability, the signals of which will be as error-free as possible. False indications lead to malfunctioning of the stabilization system, and its operation does not meet the requirements set for it. There are many types of errors—bias, white noise, hysteresis, or bias drift—which affect the measurement signals from the sensors. One of the significant problems is assessing what maximum level of sensor errors stabilization system will still operate as required. In this paper, the impact of different sensor errors on flight stabilization was presented. The research was carried out using the example of an automatic flight stabilization system using aircraft trimming surfaces in a longitudinal control channel in Hardware-in-the-Loop simulations. The model simulates various types of sensor errors during flight, while the stabilization system is implemented in hardware interfaced with a real-time computer. The results of the simulations are presented and analyzed. Their comparison indicated which sensor errors affects the flight stability the most and how the effectiveness of the stabilization system changes as error increases. The presented results show changes in flight parameters due to added sensor errors. Depending on the accuracy class of the IMU, the errors more or less disrupt the operation of the system.

Configuration and Validation of an Oceanic Physical and Biogeochemical Model to Investigate Coastal Eutrophication in the Southern California Bight

AbstractThe Southern California Bight (SCB), an eastern boundary upwelling system, is impacted by global warming, acidification, and oxygen loss and receives anthropogenic nutrients from a coastal population of 20 million people. We describe the configuration, forcing, and validation of a realistic, submesoscale‐resolving ocean model as a tool to investigate coastal eutrophication. This modeling system represents an important achievement because it strikes a balance of capturing the forcing by U.S. Pacific Coast‐wide phenomena, while representing the bathymetric features and submesoscale circulation that affect the transport of nutrients from natural and human sources. Moreover, the model allows simulations at time scales that approach the interannual frequencies of ocean variability. The model simulation is evaluated against a broad suite of observational data throughout the SCB, showing realistic depiction of the mean state and its variability with satellite and in situ measurements of state variables and biogeochemical rates. The simulation reproduces the main structure of the seasonal upwelling front, the mean current patterns, the dispersion of wastewater plumes, as well as their seasonal variability. Furthermore, it reproduces the mean distributions of key biogeochemical and ecosystem properties and their variability. Biogeochemical rates reproduced by the model, such as primary production and nitrification, are also consistent with measured rates. This validation exercise demonstrates the utility of using fine‐scale resolution modeling and local observations to identify, investigate, and communicate uncertainty to stakeholders to support management decisions on local anthropogenic nutrient discharges to coastal zones.

Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

Application of State Feedback Controller to Ensure Robust D-Stable Operation of Virtual Synchronous Generators

This article presents a comprehensive small signal model for a virtual synchronous generator (VSG) integrated into a microgrid. In the developed state-space, active and reactive power control loops of the VSG as well as resistances, and reactances of the lines are considered. Based on this model, the robust D-stable region in terms of different values of inertia constant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(M)$</tex-math></inline-formula> , and damping coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(D)$</tex-math></inline-formula> values of the VSG is obtained. To enhance both transient performance as well as guarantee the robust D-stable operation of VSG, an optimization problem is also proposed. Moreover, a new control method is suggested to damp frequency oscillations of the microgrid. The proposed controller is a state feedback which uses the pole placement method and can be used in both of the grid-connected, and isolating modes of operation. In this controller, poles can be placed in the desired locations in order for the microgrid to achieve the desired transient response. In order to access all state variables, a state observer is used to avoid measurement of state variables. Accordingly, one of the features of the proposed method is its ability to move poles without requiring additional sensors. The performance of the proposed method is evaluated in MATLAB/SIMULINK, which shows enhanced dynamic behavior of the microgrid.

Observer-based Adaptive Control for a Class of Uncertain Switched Systems with Time-delay: A Sliding Mode Approach

This article is devoted to the exponential stabilization problem of time-varying delay switched systems with nonlinear uncertainties via sliding mode technique. First of all, anon-fragile observer is established to deal with the case that the partial or total measurement of state variables is unavailable, from which a new sliding manifold is constructed. An exponential stability criteria of the dynamics in sliding phase is then devised in accordance with Lyapunov stability theory, linear matrix inequality and average dwell time method. Furthermore, the arrival condition of the designated sliding manifold is satisfied by utilizing the developed adaptive sliding mode controller. At last, the feasibility of the proposed control scheme is confirmed through two illustrative examples.

Uncertainty Space Expansion: A Consistent Integration of Measurement Errors in Linear Inversion

Summary In general, a probabilistic framework for a modeling process involves two uncertainty spaces: model parameters and state variables (or predictions). The two uncertainty spaces in reservoir simulation are connected by the governing equations of flow and transport in porous media in the form of a reservoir simulator. In a forward problem (or a predictive run), the reservoir simulator directly maps the uncertainty space of the model parameters to the uncertainty space of the state variables. Conversely, an inverse problem (or history matching) aims to improve the descriptions of the model parameters by using the measurements of state variables. However, we cannot solve the inverse problem directly in practice. Numerous algorithms, including Kriging-based inversion and the ensemble Kalman filter (EnKF) and its many variants, simplify the system by using a linear assumption. The purpose of this paper is to improve the integration of measurement errors in the history-matching algorithms that rely on the linear assumption. The statistical moment equation (SME) approach with the Kriging-based inversion algorithm is used to illustrate several practical examples. In the Motivation section, an example of pressure conditioning has a measurement that contains no additional information because of its significant measurement error. This example highlights the inadequacy of the current method that underestimates the conditional uncertainty for both model parameters and predictions. Accordingly, we derive a new formula that recognizes the absence of additional information and preserves the unconditional uncertainty. We believe this to be the consistent behavior to integrate measurement errors. Other examples are used to validate the new formula with both linear and nonlinear (i.e., the saturation equation) problems, with single and multiple measurements, and with different configurations of measurement errors. For broader applications, we also develop an equivalent formula for algorithms in the Monte Carlo simulation (MCS) approach, such as EnKF and ensemble smoother (ES).

Predictive deviation filter for deadbeat control

Deadbeat control calculates excitation by a differential equation model that takes measurements of state variables as initial values and instructions as terminal values. Ideally, this excitation can force state variables to track instructions in the succeeding control interval. However, in a practical system, noises in measured signals can mislead the control system and degrade its performance with increased ripples and harmonics. Therefore, some filters are often used in measurements, but their inherent delay can lead to inaccurate initial values and cause severe overshoots in dynamic cases. In this study, a new type filtering algorithm called predictive deviation filter is proposed for deadbeat control, which filters only the predicted deviation obtained by removing the expected values from measured ones, and then the real values of state variables are recovered. With this method, the influence of the filter delay can be limited only on the deviation which is a small part of the signal, significantly improving the accuracy of the initial values. Furthermore, a comprehensive frequency-domain analysis of the predictive deviation filter is provided. Finally, the new filtering algorithm is applied to drive control of permanent magnet synchronous motor. The simulation and experimental results verify the effectiveness and performance of this approach.

Parameters Estimation and Prediction of Water Movement and Solute Transport in Layered, Variably Saturated Soils Using the Ensemble Kalman Filter

The parameters of water movement and solute transport models are essential for the accurate simulation of soil moisture and salinity, particularly for layered soils in field conditions. Parameter estimation can be achieved using the inverse modeling method. However, this type of method cannot fully consider the uncertainties of measurements, boundary conditions, and parameters, resulting in inaccurate estimations of parameters and predictions of state variables. The ensemble Kalman filter (EnKF) is well-suited to data assimilation and parameter prediction in Situations with large numbers of variables and uncertainties. Thus, in this study, the EnKF was used to estimate the parameters of water movement and solute transport in layered, variably saturated soils. Our results indicate that when used in conjunction with the HYDRUS-1D software (University of California Riverside, California, CA, USA) the EnKF effectively estimates parameters and predicts state variables for layered, variably saturated soils. The assimilation of factors such as the initial perturbation and ensemble size significantly affected in the simulated results. A proposed ensemble size range of 50–100 was used when applying the EnKF to the highly nonlinear hydrological models of the present study. Although the simulation results for moisture did not exhibit substantial improvement with the assimilation, the simulation of the salinity was significantly improved through the assimilation of the salinity and relative solutetransport parameters. Reducing the uncertainties in measured data can improve the goodness-of-fit in the application of the EnKF method. Sparse field condition observation data also benefited from the accurate measurement of state variables in the case of EnKF assimilation. However, the application of the EnKF algorithm for layered, variably saturated soils with hydrological models requires further study, because it is a challenging and highly nonlinear problem.

A Flatness Based Nonlinear Sensorless Control of Induction Motor Systems

&lt;p&gt;This paper deals with the flatness based approach for sensrless control of induction motor systems.Two main features of the proposed flatness based control are worth to be mentioned. Firstly, the simplicity of implementation of the flatness approach as a nonlinear feedback linearization control technique. Secondly, when the chosen flat outputs involve non available state variable measurements a nonlinear observer is used to estimate them&lt;strong&gt;. &lt;/strong&gt;The main advantage of the used observer is its ability to exploite the properties of the system nonlinearties. The simulation results are presented to illustrate the effectiness of the proposed approach for sensorless control of the considered induction motor.&lt;/p&gt;

Finding approaching zero modal by management and expansion area stability robust digital control system traffic side drones

The first step in the synthesis of robust control systems is to determine the structure and beginning to labor-tkovyh options in terms of accommodation poles closed system in a given area. In this case, it uses the modal control method, which allows one-to identify management options. In the article the algorithm calc-definition hunku initial approximation method modal control for discrete dynamical systems with incomplete measurement of state variables based approach, intro, is provided in [1]. The next step in the synthesis of robust digital control is the procedure robastyzatsiyi, ie expansion of robust sustainability topics of digital system control facility. The example of using this approach for digital control system lateral movement of the unmanned aerial vehicle

Adaptive fuzzy control design and applications of uncertain stochastic nonlinear systems with input saturation

In this paper, the problem of adaptive fuzzy output feedback control is investigated for a class of uncertain stochastic nonlinear systems. The stochastic nonlinear systems under study have unknown nonlinear uncertainties, unmodeled dynamics, input saturation and without the direct measurement of state variables. Fuzzy logic systems and a smooth function are first utilized to approximate the unknown nonlinear functions and the input saturation, respectively, and then a fuzzy state observer is designed to estimate the unmeasured states. By combining the adaptive backstepping design technique with the dynamic surface control (DSC) design technique and using the concept of the dynamical signal, a new adaptive fuzzy output feedback tracking control approach is developed. It is proved that all the variables involved in the closed-loop system are semi-globally uniformly ultimately bounded (SUUB) in probability, and also that both observer errors and tracking errors converge to a small neighborhood of the origin by appropriate selecting the design parameters. The proposed adaptive fuzzy control approach is finally applied to the two-stage chemical reactor with recycle streams, and achieves the satisfactory control performance.

Integrated Model Considering Effects of Zero Injection Buses and Conventional Measurements on Optimal PMU Placement

The main requirements of the wide-area monitoring system is to acquire the real-time measurement of state variables that can be provided by phasor measurement units (PMUs). The optimal PMU placement is the leading object of this paper, which is presented as an integer linear programming problem. The zero injection buses (ZIBs) and conventional measurements (CMs) enhance the system observability. In this paper, a novel integrated model is presented to consider the effects of the ZIBs and CMs for PMU placement. The proposed model considers limitations caused by ZIBs and CMs that have not been studied earlier. The single branch and single PMU outage, as two common contingencies, are also separately and simultaneously taken into account. The proposed method is applied to five different IEEE test systems (14, 30, 39, 57, and 118 bus) in order to demonstrate its effectiveness. The obtained results are compared with that of the other studies.

Adaptive fuzzy decentralized tracking fault-tolerant control for stochastic nonlinear large-scale systems with unmodeled dynamics

This paper studies the adaptive fuzzy decentralized tracking fault-tolerant control design problem for a class of unknown stochastic nonlinear strict-feedback systems with actuator faults. The stochastic systems under study have the characterizations of unknown functions, unmodeled dynamics and without the direct measurements of state variables. Fuzzy logic systems are employed to identify the unknown stochastic nonlinear systems, and a fuzzy state observer is established for estimating the immeasurable states. The dynamic surface control (DSC) design approach based on the backstepping technique is presented to design adaptive decentralized tracking fault-tolerant controller. It is proved that proposed control approach guarantees that all the variables of the closed-loop system are bounded in probability, and also that the tracking errors converge to an adjustable neighborhood of the origin regardless of actuator faults and unmodeled dynamics. The simulation results are provided to illustrate the effectiveness of the proposed control approach.

Fuzzy Adaptive Actuator Failure Compensation Control of Uncertain Stochastic Nonlinear Systems With Unmodeled Dynamics

This paper investigates fuzzy adaptive actuator failure compensation control for a class of uncertain stochastic nonlinear systems in strict-feedback form. These stochastic nonlinear systems contain the actuator faults of both loss of effectiveness and lock-in-place, unmodeled dynamics, and without direct measurements of state variables. With the help of fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is established to estimate the unmeasured states. By introducing the dynamical signal and the changing supply function technique design into the backstepping control design, a robust adaptive fuzzy fault-tolerant control scheme is developed. It is proved that the proposed control approach can guarantee that all the signals of the closed-loop system are bounded in probability in the presence of the actuator failures and the unmodeled dynamics. Simulation results are provided to show the effectiveness of the control approach.

Adaptive fuzzy decentralised fault-tolerant control for nonlinear large-scale systems with actuator failures and unmodelled dynamics

This paper discusses the adaptive fuzzy decentralised fault-tolerant control (FTC) problem for a class of nonlinear large-scale systems in strict-feedback form. The systems under study contain the unknown nonlinearities, unmodelled dynamics, actuator faults and without the direct measurements of state variables. With the help of fuzzy logic systems identifying the unknown functions and a fuzzy adaptive observer is designed to estimate the unmeasured states. By using the backstepping design technique and the dynamic surface control approach and combining with the changing supply function technique, a fuzzy adaptive FTC scheme is developed. The main features of the proposed control approach are that it can guarantee the closed-loop system to be input–to-state practically stable, and also has the robustness to the unmodelled dynamics. Moreover, it can overcome the so-called problem of ‘explosion of complexity’ existing in the previous literature. Finally, simulation studies are provided to illustrate the effectiveness of the proposed approach.

Adaptive Neural Network Output Feedback Control for Stochastic Nonlinear Systems With Unknown Dead-Zone and Unmodeled Dynamics

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

OBSERVER-BASED INPUT-OUTPUT LINEARIZATION CONTROL OF A MULTIVARIABLE CONTINUOUS CHEMICAL REACTOR

The goal of this paper is to develop a nonlinear observer- based control strategy for a multi-variables continuous stirred tank reac- tor (CSTR). A new robust nonlinear observer is constructed to estimate the whole process state variables. The observer is coupled with a non- linear controller, designed based on the input-output linearization for controlling the concentration and reactor temperature. The closed loop system is shown to be globally asymptotically stable based on Lyapunov arguments. Finally, computer simulations are developed for showing the performance of the proposed controller. Continuous stirred tank reactors (CSTR) are ones of the most important plants in chemical industry. In practice, however, the control of these reac- tors poses a number of challenging problems. These problems arise from the presence of severe nonlinearities of these chemical reactors, as well as for mon- itoring and control applications, only a few measurements are available, either because the measuring devices do not exist or are too expensive. Therefore, we can deduce that the main difficulties arising in the control of CSTR's reactors arrive from two main sources: the process complexity and the difficulty to have reliable measurements of state variables ((1), (16)). In recent years, a various design methods of nonlinear control strategies have been proposed. Most of these based on differential geometric concepts ((15)-(18), (23)). This method allows a certain class of systems to be linearized using state feedback and co- ordinate transformations ((12), (14)). Extensions of the method like adaptive linearization ((21), (25)), robust linearization (24), and asymptotically exact linearization (13) account for small model-plant-mismatch.

A least squares temporal difference actor–critic algorithm with applications to warehouse management

AbstractThis article develops a new approximate dynamic programming (DP) algorithm for Markov decision problems and applies it to a vehicle dispatching problem arising in warehouse management. The algorithm is of the actor‐critic type and uses a least squares temporal difference learning method. It operates on a sample‐path of the system and optimizes the policy within a prespecified class parameterized by a parsimonious set of parameters. The method is applicable to a partially observable Markov decision process setting where the measurements of state variables are potentially corrupted, and the cost is only observed through the imperfect state observations. We show that under reasonable assumptions, the algorithm converges to a locally optimal parameter set. We also show that the imperfect cost observations do not affect the policy and the algorithm minimizes the true expected cost. In the warehouse application, the problem is to dispatch sensor‐equipped forklifts in order to minimize operating costs involving product movement delays and forklift maintenance. We consider instances where standard DP is computationally intractable. Simulation results confirm the theoretical claims of the article and show that our algorithm converges more smoothly than earlier actor–critic algorithms while substantially outperforming heuristics used in practice. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012

Intelligent Linear-Quadratic Optimal Output Feedback Regulator for a Deregulated Automatic Generation Control System

One of the main observed problems in the control of automatic generation control systems is the limitation to access and measurement of state variables in the real world. In order to solve this problem, an optimal output feedback method, the linear-quadratic regulator controller, is used. In the output feedback method, only measurable state variables within each control area are required to use for feedback. But in order to improve dynamic performance and provide a better design for this controller, the concept of an intelligent regulator is added to the linear-quadratic regulators; as a result, the particle swarm optimization based linear-quadratic output feedback regulator and the imperialist competitive algorithm based linear-quadratic output feedback are proposed to calculate the global optimal gain matrix of controller intelligently. The optimal control law of this controller must be determined by minimizing a performance index under the output feedback conditions leading to coupled matrix equations. In conventional methods, the control law is handled by pole placement, iterative, or trial-and-error methods for choosing controller gains; thus, intelligent optimization techniques are applied to solve this problem. The proposed controllers are tested on a two-area automatic generation control power system, and a complete comparison between the proposed output feedback controllers with adaptive weighted particle swarm optimization, particle swarm optimization, the imperialist competitive algorithm, and a conventional output feedback controller is presented. The results show that the proposed intelligent controller improved the dynamic response of the system faster than the conventional controller and provided a control system that satisfied the load frequency control requirements.