Measurable State Variables Research Articles

Improving Low-Frequency Digital Control in the Voltage Source Inverter for the UPS System

Today voltage source inverters (VSIs) operate with high switching frequencies (let us assume higher than 50 kHz) owing to the fast Si (Silicon) or SiC (Silicon Carbide) switching transistors. However, there are some applications, e.g., with slower switches (e.g., IGBT—Isolated Gate Bipolar Transistor) or when lower dynamic power losses are required when the switching frequency is low (let us assume about 10 kHz). The resonant frequency of the output filter is usually below 1 kHz. The measurements of Bode plots of the measurement traces of various microprocessor-controlled VSIs show that in this frequency range, the characteristics of these channels can be simply approximated through two or three switching periods delay. For the high switching frequency, it is not noticeable, but for the low frequency it can cause some oscillations in the output voltage. One of the solutions can be to use the predictor of the measured state variables based on the full-state Luenberger observer or the linear Kalman filter. Both solutions will be simulated in MATLAB/Simulink and the chosen one will be tested in the experimental VSI. The research aims to omit delays in the measurement channels for the low switching frequency by using the predictions for the measured state variables and finally increasing the gains of the controller to decrease the output voltage distortions.

Nonlinear Multi-Object Differential Game Simulation Model in LabVIEW

This article presents the synthesis of a nonlinear multi-object differential game model in relation to the process of safe ship control in collision situations at sea. Nonlinear dynamic equations of a target ship and linear kinematic equations of passing ships were used to formulate the game state equations. The model of such a differential game was developed using LabVIEW 2022 version software. This was then subjected to simulation tests using the example of a navigational situation in which the target ship passed three encountered ships at a safe distance under the conditions of non-cooperation of ships, their cooperation, and optimal non-game control. The results of the computer simulation are presented in the form of ship trajectories and time courses of individual game control variables. The distinguishing feature of the model built in LabVIEW software is the ability to conduct research in online mode, where the user has the opportunity to track the impact of changes in the model parameters on the course of the differential game simulation on an ongoing basis. Further refinements of the simulation model should concern the larger number of ships and test the sensitivity of the game control quality to inaccuracies in the measured state variables and to changes in the parameters of the ship’s dynamics.

Hybrid deep modeling of a CHO-K1 fed-batch process: combining first-principles with deep neural networks.

Introduction: Hybrid modeling combining First-Principles with machine learning is becoming a pivotal methodology for Biopharma 4.0 enactment. Chinese Hamster Ovary (CHO) cells, being the workhorse for industrial glycoproteins production, have been the object of several hybrid modeling studies. Most previous studies pursued a shallow hybrid modeling approach based on three-layered Feedforward Neural Networks (FFNNs) combined with macroscopic material balance equations. Only recently, the hybrid modeling field is incorporating deep learning into its framework with significant gains in descriptive and predictive power. Methods: This study compares, for the first time, deep and shallow hybrid modeling in a CHO process development context. Data of 24 fed-batch cultivations of a CHO-K1 cell line expressing a target glycoprotein, comprising 30 measured state variables over time, were used to compare both methodologies. Hybrid models with varying FFNN depths (3-5 layers) were systematically compared using two training methodologies. The classical training is based on the Levenberg-Marquardt algorithm, indirect sensitivity equations and cross-validation. The deep learning is based on the Adaptive Moment Estimation Method (ADAM), stochastic regularization and semidirect sensitivity equations. Results and conclusion: The results point to a systematic generalization improvement of deep hybrid models over shallow hybrid models. Overall, the training and testing errors decreased by 14.0% and 23.6% respectively when applying the deep methodology. The Central Processing Unit (CPU) time for training the deep hybrid model increased by 31.6% mainly due to the higher FFNN complexity. The final deep hybrid model is shown to predict the dynamics of the 30 state variables within the error bounds in every test experiment. Notably, the deep hybrid model could predict the metabolic shifts in key metabolites (e.g., lactate, ammonium, glutamine and glutamate) in the test experiments. We expect deep hybrid modeling to accelerate the deployment of high-fidelity digital twins in the biopharma sector in the near future.

A Novel relaxation of the static output feedback problem for a class of plants

The static output feedback control problem is important, as it is concerned with the case when one cannot measure all state variables. It seeks to obtain a stabilising control under these conditions and is, often, difficult to solve. For a certain class of linear dynamical systems, we provide a novel approach to obtain a stabilising control gain matrix. The class of plants considered is of those, for which we can measure at least half of all state variables and where those measurements affect at least half of all state variables. Moreover, when we write the (linearly transformed) system matrix in block form, we require that either the two lower block matrices are nonsingular, or, if the inputs affect the measured state variables directly, at least the lower left one is nonsingular. We then show that we can determine the stabilising control gain matrix from the solution of a linear matrix inequality. Finally, we apply the approach to different benchmark problems, where it performs well, and confirm its good performance through additional numerical experiments.

Robust Control Based on Observed States Designed by Means of Linear Matrix Inequalities for Grid-Connected Converters

This paper provides a procedure to design robust controllers based on observed states applied to three-phase inverters with LCL filters connected to a grid with uncertain and possibly time-varying impedances, which can arise in renewable energy systems and microgrid applications. Linear matrix inequalities are used to rapidly compute, off-line, based only on the choice of two scalar parameters for pole location, sets of gains for the controller and the observer, and also to provide a theoretical certificate of the closed-loop stability, including a limit for the rate of variations of the grid impedances. The proposed design procedure allows the easy implementation of robust state feedback controllers with a reduced number of sensors, ensuring good performance for different sets of grid impedances. Additionally, larger regions of guaranteed stability are provided by the proposed procedure, when compared with a similar condition from the literature. The control law using the observed states can ensure grid currents with low harmonic content, complying with the IEEE 1547 Standard requirements, with negligible loss of performance concerning the feedback of the measured state variables. Three optimal state feedback controllers from the literature are reproduced here and successfully implemented using the observed state variables based on the proposed procedure. In all cases, the viability of the proposal was confirmed by simulations and experimental results.

A Novel Detection and Localization Approach of Open-Circuit Switch Fault for the Grid-Connected Modular Multilevel Converter

The open-circuit fault detection and localization (FDL) technique can improve the reliability of the modular multilevel converter (MMC). However, the conventional software-based FDL methods usually have a heavy computation burden or a limited localization speed. This article proposes a simplified and fast software-based FDL approach for the grid-connected MMC. First, the errors between the measured state variables (the output current and the circulating current) and their estimated values are calculated. By comparing these errors with their threshold values, the switch fault can not only be detected but also be localized to the specific arm. Then, the capacitor voltages in this faulty arm are collected, and the submodule (SM) with the highest capacitor voltage is selected. To confirm the switch fault in this SM, a modified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pauta</i> criterion is presented to check the abnormal voltage data. As a result, the computation burden of the proposed software-based FDL approach is significantly reduced, and the faulty SM can be localized in a short period. Simulation and experimental results verify that the proposed approach can effectively detect and localize different open-circuit faults, and it is immune to the step of power references.

Model based reduced-order observers for a class of mechatronic systems with mitigation of disturbances effects using the Attractive Ellipsoid Method

State-estimation of mechatronic systems is essential because, in addition to position and velocity, there are electric and magnetic signals that must be measured, and can lead to sophisticated and expensive instrumentation systems. This paper addresses the problem of state-estimation for underactuated mechanical systems whose active joints are driven by permanent magnet dc-motors. Luenberger-type structures are designed, where the measured state variables and the dynamic model are used to construct the unavailable ones, in presence of uncertainties and disturbances as inputs. To avoid the peaking phenomenon is one of the main challenges on state observation for nonlinear systems. Underactuated mechatronic systems are not immune to this problem, which is exacerbated when external disturbances are taken into account. The major purpose here is to use a robust reduced observer to estimate the unmeasured state variables. The Attractive Ellipsoid Method provides reliable estimation of unmeasured state signals because it reduces the influence of external disturbances on the estimate error signals. This ensures that the estimated signals converge to an invariant set of minimal size around the real ones. The first design is based on the quasi-linear representation that meets the observability condition. The second considers the entire nonlinear model, which necessitates the availability of the entire position vector. The major findings are demonstrated experimentally on a non-traditional mechatronic system: the linear double pendulum system whose last joint is constrained by two linear springs. This shows a significant improvement of the estimation response, concerning robust techniques based on the linear approximation, towards its application in designing an adaptive observer to provide state and parameters estimation simultaneously, which is essential in robust and adaptive control systems.

Fractional order adaptive Kalman filter for sensorless speed control of DC motor

ABSTRACT State estimation is a challenging and most crucial issue in the industry for proper monitoring and controlling of the plants. These kinds of control systems have the requirement of costly measurement sensors/equipment for measurable and unmeasurable state variables of the dynamical plants. These drawbacks can be overcome by designing a sensorless system to estimate the state variables. In the proposed work, sensorless speed control of DC motor is implemented by using a fractional-order adaptive Kalman filter (FOAKF). The FOAKF algorithm uses a fractional feedback loop of the previous Kalman gain along with the current Kalman gain. The motor shaft speed is estimated by the FOAKF state estimator. Furthermore, a performance comparison of extended Kalman filter (EKF) and FOAKF estimator under a similar condition is realised using MATLAB/Simulink environment. To validate the performance of the FOAKF estimator, a hardware prototype model has been presented with the help of the arduino board. In the proposed work, the root-mean-square error (RMSE) and Euclidean distance error between reference speed and estimated speed have been used for the performance metric. The performance comparison result shows that FOAKF is a more robust and accurate state estimator in comparison with EKF.

Adaptive Neural Control of Uncertain MIMO Nonlinear Pure-Feedback Systems via Quantized State Feedback

We present an adaptive quantized state feedback tracking methodology for a class of uncertain multiple-input multiple-output (MIMO) nonlinear block-triangular pure-feedback systems with state quantizers. Uniform quantizers are considered to quantize all measurable state variables for feedback. Compared with the existing tracking approaches of MIMO lower-triangular nonlinear systems, the main contributions of the proposed strategy are developing (1) a quantized-state-feedback-based adaptive tracker in the presence of nonaffine interaction of states and control variables of MIMO systems and (2) an analysis strategy for quantized feedback stability using adaptive compensation terms to derive bounded quantization errors. In addition, the stability of the closed-loop system with quantized state feedback is analyzed based on the Lyapunov stability theorem. Finally, simulation examples, including interconnected inverted pendulums, are presented to validate the effectiveness of the proposed control strategy.

Bi-directional impulse chaos control in crystal growth.

Mono-silicon crystals, free of defects, are essential for the integrated circuit industry. Chaotic swing in the flexible shaft rotating-lifting (FSRL) system of the mono-silicon crystal puller causes harm to the quality of the crystal and must be suppressed in the crystal growth procedure. From the control system viewpoint, the constraints of the FSRL system can be summarized as not having measurable state variables for state feedback control, and only one parameter is available to be manipulated, namely, the rotation speed. From the application side, an additional constraint is that the control should affect the crystallization physical growth process as little as possible. These constraints make the chaos suppression in the FSRL system a challenging task. In this work, the analytical periodic solution of the swing in the FSRL system is derived using perturbation analysis. A bi-directional impulse control method is then proposed for suppressing chaos. This control method does not alter the average rotation speed. It is thus optimum regarding the crystallization process as compared with the single direction impulse control. The effectiveness and the robustness of the proposed chaos control method to parameter uncertainties are validated by the simulations.

Microphysical Modeling of Carbonate Fault Friction at Slip Rates Spanning the Full Seismic Cycle.

Laboratory studies suggest that seismogenic rupture on faults in carbonate terrains can be explained by a transition from high friction, at low sliding velocities (V), to low friction due to rapid dynamic weakening as seismic slip velocities are approached. However, consensus on the controlling physical processes is lacking. We previously proposed a microphysically based model (the “Chen–Niemeijer–Spiers” [CNS] model) that accounts for the (rate‐and‐state) frictional behavior of carbonate fault gouges seen at low velocities characteristic of rupture nucleation. In the present study, we extend the CNS model to high velocities (1 mm/s ≤ V ≤ 10 m/s) by introducing multiple grain‐scale deformation mechanisms activated by frictional heating. As velocity and hence temperature increase, the model predicts a continuous transition in dominant deformation mechanisms, from frictional granular flow with partial accommodation by plasticity at low velocities and temperatures, to grain boundary sliding with increasing accommodation by solid‐state diffusion at high velocities and temperatures. Assuming that slip occurs in a localized shear band, within which grain size decreases with increasing velocity, the model results capture the main mechanical trends seen in high‐velocity friction experiments on room‐dry calcite‐rich rocks, including steady‐state and transient aspects, with reasonable quantitative agreement and without the need to invoke thermal decomposition or fluid pressurization effects. The extended CNS model covers the full spectrum of slip velocities from earthquake nucleation to seismic slip rates. Since it is based on realistic fault structure, measurable microstructural state variables, and established deformation mechanisms, it may offer an improved basis for extrapolating lab‐derived friction data to natural fault conditions.

Acid hydrolysis-based sugarcane bagasse biorefining for levulinic acid production: Dynamic mechanistic modeling under varying operating conditions

The study on the lignocellulosic material conversion into bio-based platform chemicals, such as levulinic acid (LA), is one of the most promising routes to promote the development of advanced biorefineries. In this work, a dynamic mechanistic model is developed to simulate the LA production from lignocellulosic material. A wide operating range is used to estimate the parameters of the reaction kinetics. Because multi-parameter estimation problem is complex, a genetic algorithm-based optimization procedure is used to determine the optimum parameters values. Measurements are obtained for various reaction times (0 - 45 min) temperatures (150 – 200 °C) and acid concentration of 7.0 % w/v H2SO4. The calculated reaction rates for the state variables, concentrations of LA, glucose, 5-hydroxymethylfurfural and humins are used to construct the dynamic mechanistic model. The prediction of measured state variables was particularly accurate, as determined by the root mean square error (RMSE) and correlation coefficient (R2). Therefore, a satisfactory agreement between experimental LA yield of 57.2 mol% and computed LA yield of 56.4 mol% was achieved (at 200 °C, 7.0 % w/v H2SO4, 45 min). The proposed methodology drives the systematic development of an industrially reliable dynamic mechanistic model for LA production from sugarcane bagasse as a means to increase the LA yields in the biorefinery.

Design of an Optimal Controller in a Voltage Regulation System When Powering Submersible Electromechanical Equipment

The paper addresses the issue of designing an optimal controller using the measured state variables of a nonlinear multi-dimensional object with transient disturbances – power supply of submersible electromechanical equipment with AC transmission via a cable power line. We present an approach to the design of an adjustable PI-controller enabling to determine its closed-form parameters with the linearized model of a complex control object. A numerical example for simulation of a power supply highlighting capability of the design algorithm for the optimal controller with the quadratic criteria is provided. The obtained results can be used to tackle problems of adaptive control of remotely powered electromechanical objects without sensors.

Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems

SummaryThis work presents a neural observer‐based controller for uncertain nonlinear discrete‐time systems with unknown time‐delays. The proposed neural observer does not need previous knowledge of the model about the system under consideration, neither the value of its parameters, delays, nor their explicit estimations. The proposed neural observer is based on a neural network composed of two recurrent high order neural networks (RHONNs) for nonmeasurable state variables, one in a parallel configuration, and for measurable state variables one in a series‐parallel configuration. The neural network is trained on‐line with an extended Kalman filter algorithm. The proposed RHONN observer provides a mathematical model for the system. Based on such a resulting mathematical model, a control law is designed using discrete‐time sliding mode block control. Applicability is presented using real‐time results that show the performance of the proposal using a linear induction motor prototype as the selected system; this prototype is under the presence of varying time‐delays. A Lyapunov analysis is included to prove the semi‐globally uniformly ultimately boundedness (SGUUB) of the proposed RHONN observer‐controller scheme for uncertain nonlinear discrete‐time systems with unknown delays.

An operating state estimation model for integrated energy systems based on distributed solution

In view of the disadvantages of the traditional energy supply systems, such as separate planning, separate design, independent operating mode, and the increasingly prominent nonlinear coupling between various sub-systems, the production, transmission, storage and consumption of multiple energy sources are coordinated and optimized by the integrated energy system, which improves energy and infrastructure utilization, promotes renewable energy consumption, and ensures reliability of energy supply. In this paper, the mathematical model of the electricity-gas interconnected integrated energy system and its state estimation method are studied. First, considering the nonlinearity between measurement equations and state variables, a performance simulation model is proposed. Then, the state consistency equations and constraints of the coupling nodes for multiple energy subsystems are established, and constraints are relaxed into the objective function to decouple the integrated energy system. Finally, a distributed state estimation framework is formed by combining the synchronous alternating direction multiplier method to achieve an efficient estimation of the state of the integrated energy system. A simulation model of an electricity-gas interconnected integrated energy system verifies the efficiency and accuracy of the state estimation method proposed in this paper. The results show that the average relative errors of voltage amplitude and node pressure estimated by the proposed distributed state estimation method are only 0.0132% and 0.0864%, much lower than the estimation error by using the Lagrangian relaxation method. Besides, compared with the centralized estimation method, the proposed distributed method saves 5.42 s of computation time. The proposed method is more accurate and efficient in energy allocation and utilization.

Input-to-State Stabilization of a Class of Uncertain Nonlinear Systems via Observer-Based Event-Triggered Impulsive Control

This article concerns the problem of input-to-state stabilization for a group of uncertain nonlinear systems equipped with nonabsolutely available states and exogenous disturbances. To appropriately cope with these partially measurable state variables as well as dramatically minimize controller updating burden and communication costs, an event-triggered mechanism is skillfully devised and an observer-based impulsive controller with the combination of sample control is correspondingly presented. By resorting to the iterative method and Lyapunov technology, some sufficient criteria are established to guarantee the input-to-state stability of the newly uncertain controlled system under the employed controller, in which an innovative approximation condition as to the uncertain term is proposed and the linear matrix inequality technique is utilized for restraining sophisticated parameter uncertainties. Furthermore, the Zeno behavior in the proposed event-triggered strategy is excluded. The control gains and event-triggered mechanism parameters are conjointly designed by resolving some inequalities of linear matrix. Eventually, the availability and feasibility of the achieved theoretical works are elucidated by two simulation examples.

Multifunction Control Strategy for Single-Phase AC/DC Power Conversion Systems With Voltage-Sensorless Power-Decoupling Function

In this article, a novel voltage-sensorless controller for the single-phase ac/dc power conversion systems with a self-adaptive power-decoupling function is proposed. Existing control strategies for achieving power decoupling usually require additional active switches or extra measurement circuits to a certain power-decoupling controller. By employing the proposed method, the decoupling voltage could be accurately evaluated with the fluctuations of the other measured state variables in the original converter, and the issues of instability and inaccuracy on power-decoupling control under transient conditions could be solved. Furthermore, the proposed control scheme involves a phase-shift modification to improve the power quality as well as achieve accurate power-decoupling reference, which could eliminate the harmonic influence on the original PFC circuit. Finally, a 1.2-kW single-phase PFC prototype using SiC-based semiconductor switching devices is tested to validate the proposed control scheme. The experimental results demonstrate that the power factor of 0.99 with total harmonic distortion around 3%, and a dc-link voltage with 5% ripple could be achieved with the proposed control strategy.

Model predictive control of a magnetic levitation system using two-level state feedback

The magnetic levitation system is a critical part to guarantee safe and reliable operations of a maglev train. In this paper, the control strategy is proposed for the magnetic levitation system based on the model predictive control incorporating two-level state feedback. Taking advantage of the measurable state variables, that is, air gap, electromagnet acceleration, and control current through high-resolution sensor measurement, the first-level nonlinear state feedback is to linearize the unstable nonlinear magnetic levitation system, and the second-level linear state feedback is to further stabilize the system and improve the dynamic performances, which together provide a stable prediction model. The simulation results demonstrate that the proposed control strategy can ensure high-precision air gap control and favorable disturbance resistance ability.

Approximation-Based Event-Triggered Control Against Unknown Injection Data in Full States and Actuator of Uncertain Lower-Triangular Nonlinear Systems

This paper addresses an approximation-based adaptive event-triggered control problem against unknown injection data in full state measurements and an actuator of systems with unknown strict-feedback nonlinearities. It is assumed that full state variables measured for state-feedback control are corrupted by unknown injection data that denote cyber attacks or fault signals, and all system nonlinearities are unknown. Owing to the corrupted state feedback information, error surfaces using exactly measured state variables become unknown during the recursive control design procedure for strict-feedback nonlinear systems. Thus, they cannot be used to implement the adaptive event-triggered controller. To address this problem, an approximation-based adaptive recursive event-triggered control design using the corrupted state variables is established to ensure that error surfaces using exactly measured state variables converge to an adjustable neighborhood of the origin in the Lyapunov sense. The adaptive controller and its event-triggering law using corrupted states are designed under uncertain injection data where the adaptive injection data compensators using the neural networks are constructed to deal with the unknown injection data effects. The stability of the closed-loop systems and the exclusion of Zeno behavior are analyzed.

Integral-Sliding-Mode-Observer-Based Structure and Motion Estimation of a Single Object in General Motion Using a Monocular Dynamic Camera

An integral sliding mode observer (ISMO)-based method for estimating the structure and motion (SaM) of an object in general motion is proposed using a monocular dynamic (moving) camera. As the unknown range and object velocity can be considered as disturbances and should be quickly estimated, it is necessary to maintain the robustness against these disturbances from the start by eliminating the reaching mode. Therefore, the ISMO-based method is proposed on the basis of a relative camera-object motion model. By formulating the relative motion model with three-dimensional measurable state variables, three unknown components among the range, unknown object velocity components, and unknown camera velocity components can be estimated by the proposed method in the following cases: i) the camera is in dynamic motion and the object is in semigeneral motion [i.e., initially static (stationary) and then in general (static or dynamic) motion], ii) the camera is in dynamic motion and the object is in general motion within a constrained space, and iii) both the object and camera are in general motion within a less constrained space when the range information is available. Simulation and experimental results demonstrate that the range and object velocity can be estimated with a satisfactory transient response using a monocular dynamic camera.