Linear System Model Research Articles

Toward Electrostatic Levitation of Macroscale Machines

Contact-less suspension of macroscale objects has traditionally been reserved almost exclusively for magnetic systems containing copper, steel laminations, and permanent magnets. Electrostatic systems, on the other hand, have been left out primarily due to their much lower force density in atmosphere. However, the ability to levitate nonferromagnetic materials, and recent developments in high-torque density electrostatic motors may allow for practical electrostatic suspensions at meaningful scales. To transport electrostatic forces out of the micro–electro–mechanical systems scale and potentially into the power domain, this article proposes a unique electrostatic bearing for vacuum condition. Basic operating principles, force analysis, linear system model, and control strategy are presented, then experimentally validated using a prototype bearing. Stable levitation of a 62 g, 130 mm diameter aluminum disk is achieved at an air-gap length of 0.5 mm, requiring approximately 27.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\,\mathit{\mathrm{\mu }}\mathrm{W}$</tex-math></inline-formula> for suspension.

Using Magnetic Fields to Navigate and Simultaneously Localize Catheters in Endoluminal Environments

Remote magnetic navigation offers an ideal platform for automated catheter navigation. Magnetically guided catheters show great dexterity and can reach locations that are otherwise challenging to access. By automating aspects of catheterization procedures, we can simplify and expedite the procedure to allow surgeons to focus on other critical tasks during the surgery. In this article, we describe an automation strategy that is based on the center line of extracted and registered vascular geometries. Position feedback is accomplished with a Hall-effect sensor embedded near the distal end of the catheter. Sensor measurements are compared to the magnetic field predicted by a linear model of the electromagnetic navigation system. By defining specific magnetic field gradients and applying the known vascular geometry, the magnetic fields can be utilized for the simultaneous navigation and localization of the catheter. This eliminates the need for other external, dedicated mapping systems, and the use of fluoroscopy imaging is minimized. The concept is tested in 2d vascular models and the accuracy of the localization is assessed with overhead camera tracking.

Stability Preservation of Stochastic Systems With Over Large Variable Time Delays

This article aims to reveal an interesting phenomenon about the time delays (TDs) in control systems: stability preservation with over large intermittently or unbounded variable TDs. That is, given a system asymptotically stable for bounded TDs, it could remain to be stable when the TDs are enhanced to have peak values over large intermittently or unbounded. The investigation is carried out by theoretical analysis plus case studies with the stochastic system models. The stability of systems is the core issue; the explicit involvement of the TDs in the stability criteria is the premise for them being concerned. To these ends, a series of lemmas are established preliminarily, a new type stability theorem with its practical version is established in succession, and a stability criterion with the TDs as explicit parameters is induced. The Lyapunov function method is applied for less restriction and conservativeness. The mechanism and the approach for the emergence of the phenomenon under probe are analyzed. Implementation is considered for the linear system model. Typical variable TDs are constructed for illustrations theoretically. Finally, the obtained results are verified with numerical experiments.

Study on Dynamic analysis of Grid Interactive of offshore wind and Marine Current Farm

Abstract:-In day-to-day life due to growth of power demand worldwide. The use of conventional energy is increased and there is reduction in conventional resources. To fulfill the Demand, it is essential to increase the renewable resources like wind, tidal, solar etc. The energy which is sustainable means which is endless. The ocean has untapped energy resources in the form of tidal wave, geothermal, offshore wind etc. at different geographical locations.OWF combined with MCF, owing to their natural availability in close proximity, would become a new kind of integrated energy generation system in near future. Mainly there are TWO types of generators synchronous and asynchronous generator we use asynchronous generator because wind is not constant in offshore wind farm .There are FOUR types of generators used in offshore wind farm (OWF) which are named as:- Doubly fed induction generator (DFIG), Squirrel cage induction generator(SCIG) ,Wound-Rotor induction generator(WRIG) ,Permanent Magnet Synchronous generator (PMSG). And in our project, we have taken wind Energy as a source and our system is hybrid of offshore wind and marine-current farm connected to an onshore power grid via a high-voltage alternating current (HVAC) link. The performance of the studied offshore wind farm (OWF) is simulated by a doubly-fed induction generator (DFIG). DFIG system is a popular system in which the power electronic interface controls the rotor current to achieve the variable speed necessary for maximum energy capture in variable winds .while the operating characteristics of the studied marine current farm (MCF) are simulated by a squirrel-cage induction generator (SCIG).SCIG is attached to the wind turbine by means of a gearbox. The wind turbine is responsible for transforming wind power into kinetic energy .Both frequencydomain approach based on a linearized system model using Eigen value analysis and time-domain scheme based on a nonlinear system model subject to a disturbance condition are carried out. It can be concluded from the simulated steady-stated transient results that the proposed HVAC link can effectively stabilize the hybrid OWF and MCF under various disturbance conditions

Virtual sensing for dynamic industrial process based on localized linear dynamical system models with time-delay optimization

Virtual sensors play an important role in real-time sensing of key quality-related variables in industrial processes. Linear dynamical system (LDS) paradigm has established itself as a powerful tool for developing dynamic virtual sensors. However, there are still some practically pivotal issues unresolved, such as how to improve the generalization reliability and accuracy when accounting for the time delays and how to broaden the application sphere by breaking their limitations to linear processes. Motivated by dealing with these challenging issues this paper proposes a virtual sensing framework called ‘localized LDS (LoLDS)’. In the LoLDS framework, the process dynamics and nonlinearities are taken into consideration from different scales without increasing the model complexity, and the time delays are intelligently optimized which triggers the model inconsistency by a designed diversified localization scheme at the offline stage. Moreover, an adaptive online model switch scheme is developed to enable the real-timely best LDS models to be responsible to predict the quality variables. The offline and online operations together enable the LoLDS to improve the generalization performance of the dynamic virtual sensor. The LoLDS framework is highly automated, and its performance has been extensively evaluated by two real-life industrial processes, showing very promising application foregrounds.

Robust data-driven predictive control using reachability analysis

We present a robust data-driven control scheme for an unknown linear system model with bounded process and measurement noise. Instead of depending on a system model in traditional predictive control, a controller utilizing data-driven reachable regions is proposed. The data-driven reachable regions are based on a matrix zonotope recursion and are computed based on only noisy input-output data of a trajectory of the system. We assume that measurement and process noise are contained in bounded sets. While we assume knowledge of these bounds, no knowledge about the statistical properties of the noise is assumed. In the noise-free case, we prove that the presented purely data-driven control scheme results in an equivalent closed-loop behavior to a nominal model predictive control scheme. In the case of measurement and process noise, our proposed scheme guarantees robust constraint satisfaction, which is essential in safety-critical applications. Numerical experiments show the effectiveness of the proposed data-driven controller in comparison to model-based control schemes.

Interactions analysis and quadrature voltage compensation control for stabilizing LCC-HVDC system with multiple STATCOMs

Instability may occur in LCC-HVDC system when multiple STATCOMs operate simultaneously at the inverter station. To deeply investigate this problem, firstly a structured linear model of LCC-HVDC system with STATCOMs is presented. Then, a single-input single-output (SISO) form of the model is obtained. Based on the movement of dominant poles (MODPs) of the SISO model, the interactions at the receiving end of the system are investigated. The results reveal that the interactions between LCC and STATCOMs mainly affect the high-frequency (HF) characteristics of the system. With the increase in the number of online STATCOMs, the negative effects of the interactions on the system HF stability gradually increase, while that on low-frequency (LF) stability become weaker. In order to improve such negative interactions, a quadrature voltage compensation control (QVCC) of STATCOM is proposed in this paper. This method introduces the q-axis AC voltage into the constant AC voltage control (CAVC). The theoretical analysis indicates that the QVCC can enhance the stability of the LCC-HVDC system with multiple STATCOMs, which is verified by the simulation results conducted in PSCAD/EMTDC. Finally, the intuitive and physical explanations on the mechanism of instability caused by increasing number of online STATCOMs are given from two perspectives.

Reduced Order Modeling For Large-Scale Linear Systems

Reduced Order Modeling For Large-Scale Linear Systems

Superachromatic polarization modulator for stable and complete polarization measurement over an ultra-wide spectral range.

The polarization measurement system deals with polarized light-matter interactions, and has been a kind of powerful optical metrology applied in wide fields of physics and material. In this paper, we address several general theoretical aspects related to the system model and optimization for linear polarization systems from a view of the matrix algebra. Based on these theories, we propose a new framework of superachromatic polarization modulator (PM) by combining a linear polarizer and a sequence of parallel linear retarders (LRs) for a typical kind of linear polarization system based on the rotating compensator (RC) principle. In the proposed PM, the LRs are made of quarter-wave plates and as a whole act as the RC. Compared with conventional achromatic/superachromatic composite waveplates, the LR sequence has general axis orientations and is optimized by the condition number of the instrument matrix of the PM, which thereby provide much more flexibility to achieve uniform, stable and complete polarization modulation over ultra-wide spectral range. The intrinsic mechanisms, including the working principle, optimization strategy and in-situ calibration method of the proposed PM, are presented and revealed mathematically by the matrix algebra. Results on several prototypes of the PM demonstrate the validity and capability of the proposed methods for applications in broadband polarization measurement systems. The fabricated PM is further applied to a home-made dual RC Mueller matrix ellipsometer, and the accuracy and precision in the full Mueller matrix measurement are better than 2‰ and 0.6‰ respectively over the ultra-wide spectral range of 200∼1000 nm. Compared with existing techniques, the proposed PM has advantages due to superachromatic performances over ultra-wide spectral ranges, stable and complete modulation of the polarized light, and convenience for adjustment and calibration.

Data-driven model-free adaptive sliding mode control for electromagnetic linear actuator

A data-driven model-free adaptive sliding mode control (MFASMC) method is proposed to tackle the problems of inaccurate model and time-varying parameters of a micro electro-magnetic linear actuator system, considering the dependence of existing model-based control methods on the system dynamics model and the impact of unmodeled dynamics on the control performance. Firstly, the pseudo-gradient concept in the model-free adaptive control (MFAC) framework is used to transform the electromagnetic linear actuator dynamics model, which is difficult to obtain parameters accurately, into a full-format dynamic linearized data model, the dependence of the controller on the electromagnetic linear actuator model is reduced. To compensate for the effects of unknown perturbations, an improved discrete sliding mode exponential convergence law is introduced to derive a new composite control algorithm based on the pseudo partial derivative estimator, which improves the robustness of the control system. Then, the convergence of the control error and the stability of the system are demonstrated by theoretical analysis. The results show that the proposed MFASMC reduces the 10 mm step response time of the electromagnetic linear actuator by 46.3% and 25.3% compared with proportional-integral-derivative control (PID) and MFAC. The phase lag time and root-mean-square error of MFASMC under sinusoidal conditions are 0.8 ms, 0.178 mm respectively, and the steady-state error does not exceed 0.05 mm. The experimental and simulation results keep the error within 5%, the proposed control algorithm has good trajectory tracking response speed and control accuracy, and has good robustness to system uncertainty and external force disturbance.

Cancer cell viscoelasticity measurement by quantitative phase and flow stress induction

Cell viscoelastic properties are affected by the cell cycle, differentiation, and pathological processes such as malignant transformation. Therefore, evaluation of the mechanical properties of the cells proved to be an approach to obtaining information on the functional state of the cells. Most of the currently used methods for cell mechanophenotyping are limited by low robustness or the need for highly expert operation. In this paper, the system and method for viscoelasticity measurement using shear stress induction by fluid flow is described and tested. Quantitative phase imaging (QPI) is used for image acquisition because this technique enables one to quantify optical path length delays introduced by the sample, thus providing a label-free objective measure of morphology and dynamics. Viscosity and elasticity determination were refined using a new approach based on the linear system model and parametric deconvolution. The proposed method allows high-throughput measurements during live-cell experiments and even through a time lapse, whereby we demonstrated the possibility of simultaneous extraction of shear modulus, viscosity, cell morphology, and QPI-derived cell parameters such as circularity or cell mass. Additionally, the proposed method provides a simple approach to measure cell refractive index with the same setup, which is required for reliable cell height measurement with QPI, an essential parameter for viscoelasticity calculation. Reliability of the proposed viscoelasticity measurement system was tested in several experiments including cell types of different Young/shear modulus and treatment with cytochalasin D or docetaxel, and an agreement with atomic force microscopy was observed. The applicability of the proposed approach was also confirmed by a time-lapse experiment with cytochalasin D washout, whereby an increase of stiffness corresponded to actin repolymerization in time.

Uncovering Disturbance Observer and Ultra-Local Plant Models in Series PI Controllers

The paper settles two major liabilities and asymmetries of the theory of automatic control to the design of simple system controllers. It shows the most frequently used series proportional integral (PI) controllers as disturbance reconstruction and compensation-based structures and solves their designs using two types of linear system models. Beginning with the example of a simple integrator controlled by a P controller, it shows that constant input disturbances can be reconstructed by evaluating steady state values of the controller output. Thereby, the nearly steady state controller output can be simply achieved by using a low-pass filter with a time constant significantly longer than the time constant of stabilized processes. This disturbance observer (DOB) functionality can be demonstrated as being kept by series PI controllers designed by the pole assignment method. The DOB design can also be extended to first-order systems with internal feedback. However, there, the reconstructed disturbances depend both on the controller and the plant output steady state values. Because this feature is missing in industrial PI controllers, it indicates their connections with simpler, ultra-local (integral) linear system models. The interpretation of PI controllers as DOB-based structures allows a systematic consistent classification of all existing disturbance compensation structures and simplifies their comparisons with other modern and postmodern DOB-based alternatives. Given the breadth of use, improved understanding of PI control functionality also represents an important step to their optimal implementation and to research of innovative modifications, as illustrated by facilitating the flexible use of the new functional capabilities offered by embedded controls. By enhancing “the birth” of new solutions, it is then possible to better satisfy the permanently growing requirements of practice.

High-performance control of fast tool servos with robust disturbance observer and modified [formula omitted] control

Fast tool servo (FTS) is an effective technique for the generation of complicated surfaces in modern precision manufacture industry. In this paper, a novel three-degree-of-freedom (3-DOF) control scheme is proposed for the FTS to achieve good tracking and disturbance rejection performance. Specifically, the 3-DOF control scheme contains a hysteresis compensation term, a modified H∞ feedback control term, a robust disturbance observer (RDOB) term and a feedforward control term. Firstly, a hysteresis compensator is developed based on the modified Prandtl–Ishlinskii (MPI) model to eliminate the intrinsic hysteresis nonlinearity so as to identify the linear dynamic model of the FTS system. A modified mixed-sensitivity-based H∞ feedback control is then proposed to simultaneously provide an excellent damping and tracking performance with a deliberately designed input weighting function. A RDOB is designed for non-minimum phase (NMP) system based on all-pass factorization of the linear dynamics model and the standard H∞ control framework to compensate uncertain external disturbances as well as unmodeled dynamics while guaranteeing the robust stability of closed-loop system. To remove phase delay and enlarge control bandwidth, a closed-loop-inversion feedforward controller is synthesized by integrating a zero-phase error tracking control (ZPETC) with a finite impulse response (FIR) filter. A series of comparative experiments are conducted on a self-developed flux-biased normal-stressed electromagnetic actuated FTS. The experimental results consistently validate the effectiveness and superiority of the proposed scheme in terms of outstanding tracking performance and disturbance rejection.

ПРИМЕНЕНИЕ ЧЕТЫРЁХПОЛЮСНИКА ПУАНКАРЕ-СТЕКЛОВА ДЛЯ ПОСТРОЕНИЯ ИНТЕРФЕЙСА ПРИ ПОЛУНАТУРНОМ МОДЕЛИРОВАНИИ СИСТЕМ

The article considers the possibility of using the Poincare-Steklov filter to build an interfacefor hardware in the loop (HIL) simulation of system. The Z and Y forms of the filter representationare given. HIL simulation involves splitting the initial system into parts, with one part being modelednumerically on a computer, and the second part is represented by a real physical object. Theparts of the system exchange data with each other through a hardware-software interface, whichcan be implemented in different ways and should ensure stability, as well as convergence of theresults of HIL simulation to the results of modeling the original system. The variants of constructingsoftware and hardware interfaces ITM, TLM, TFA, PCD, DIM, GCS and the Poincare-Steklovfilter are described in the relevant literature sources.The article shows how the original nonlinearsystem was divided into parts using the Poincare-Steklov filter, which, accordingly, led to the splittinginto parts of the system of equations describing the behavior of the original system. Next, itwas shown how the values of the stabilizing parameters of the Poincare-Steklov filter were calculatedand the systems of equations of the system divided into parts were corrected in accordancewith the obtained values. At the next stage, the article presents the results of numerical modelingof the initial and partitioned system in MATLAB. When modeling in parts, the parts of the systemexchanged data with each other at each step of the simulation only once with a delay of h. This method of numerical modeling of a system divided into parts is as close as possible to the processes occurringduring semi-natural modeling of systems. A comparison of the obtained simulation results ofthe initial and the system divided into parts allowed us to conclude that the Poincare-Steklov filter,with the correct choice of the values of the stabilizing parameters, allows for the stability and convergenceof the results of semi-natural modeling of both linear and nonlinear systems.

A novel control strategy based on a look-up table for optimal operation of MTDC systems in post-contingency conditions

Multi terminal VSC-HVDC systems are a promising solution to the problem of connecting offshore wind farms to AC grids. Optimal power sharing and appropriate control of DC-link voltages are essential and must be maintained during the operation of VSC-MTDC systems, particularly in post-contingency conditions. The traditional droop control methods cannot satisfy these requirements, and accordingly, this paper proposes a novel centralized control strategy based on a look-up table to ensure optimal power sharing and minimum DC voltage deviation immediately during post-contingency conditions by considering converter limits. It also reduces destructive effects (e.g., frequency deviation) on onshore AC grids and guarantees the stable operation of the entire MTDC system. The proposed look-up table is an array of data that relates operating conditions to optimal droop coefficients and is determined according to N-1 contingency analysis and a linearized system model. Stability constraints and contingencies such as wind power changes, converter outage, and DC line disconnection are considered in its formation procedure. Simulations performed on a 4-terminal VSC-MTDC system in the MATLAB-Simulink environment validate the effectiveness and superiority of the proposed control strategy.

A Simulation and Experimental Study on Identification of an Electromechanical System

The current applications in electromechanical energy conversion demand highly accurate speed and position control. For this purpose, a better understanding of the motion characteristics and dynamic behavior of electromechanical systems including nonlinear effects is needed. In this paper, a suitable model of Permanent Magnet Direct Current (PMDC) motor rotating in two directions is developed for identification purposes. Model is parameterized and identified via simulation and using real experimental data. Linear and nonlinear models for the system are built for identification, and the effective nonlinearities in the system, which are Coulomb friction and dead zone, are integrated into the nonlinear model. A Weiner- Hammerstein nonlinear system description is used for identification of the model. MATLAB is selected as the investigating tool, and a simulation model is used to observe the error between the simulated and estimated outputs. Identification of the linear and nonlinear system models using experimental data is performed using the least squares (LS) and recursive least squares (RLS) methods. Performance of the model and identification method with the real time experiments are presented numerically and graphically, revealing the advantages of the proposed nonlinear identification approach.

Algorithmic Differentiation of the MWGS-Based Arrays for Computing the Information Matrix Sensitivity Equations within the Problem of Parameter Identification

The paper considers the problem of algorithmic differentiation of information matrix difference equations for calculating the information matrix derivatives in the information Kalman filter. The equations are presented in the form of a matrix MWGS (modified weighted Gram–Schmidt) transformation. The solution is based on the usage of special methods for the algorithmic differentiation of matrix MWGS transformation of two types: forward (MWGS-LD) and backward (MWGS-UD). The main result of the work is a new MWGS-based array algorithm for computing the information matrix sensitivity equations. The algorithm is robust to machine round-off errors due to the application of the MWGS orthogonalization procedure at each step. The obtained results are applied to solve the problem of parameter identification for state-space models of discrete-time linear stochastic systems. Numerical experiments confirm the efficiency of the proposed solution.

Modeling and State Estimation of Linear Destination-Constrained Dynamic Systems

Goal-guided behavior and destination-directed motion appear in a wide range of human activities and physical processes. Taking advantage of such predictive information can produce better system models and state estimates. This paper focuses on modeling and filtering issues of linear dynamic systems with linear destination constraints. We examine deficiencies of the existing destination constrained (DC) estimation methods. Basically, they resort to post-treatment by imposing destination constraints on updated states only. Viewing destination constraints as an attribute implicit of the state evolution, we propose to incorporate the destination constraints accordingly, particularly on the predictive distribution of the whole state sequence. We construct a congruous DC dynamic model by sufficiently refining the relaxed dynamics with the destination constraint using the state augmentation technique, and analyze its characterization and properties. Next, for the proposed DC model, we develop an optimal DC state estimator and describe its properties. Finally, in the context of aerial surveillance, the superiority of the proposed estimator to existing DC estimators is verified by simulation results, and the effectiveness of the proposed DC dynamic model is demonstrated using real data.

Bézier trajectory tracking control of The Omnidirectional Mobile Robot based on a linear time- varying state feedback controller

Introduction: The controller design method based on linear time varying state feedback controller is proposed to enforce an omnidirectional mobile robots (OMRs) to track a given Bézier trajectory. Methods: Initially, the linear error model of the robot system is obtained and used in the design of the linear time varying state feedback controller. Next, the controller gains are determined according to the linear and angular velocities for the desired trajectory to providing small errors. To verify the effectiveness of the proposed control strategy, simulation is carried out with desired Bézier trajectory for the OMR to reduce sudden speed changes. Results: The simulation results are presented, which have verified the good performance of the proposed controller for tracking the complex motion trajectories. Conclusion: Therefore, it is possible to apply this result to control the OMRS in logistics applications of the modern manufacturing systems.

A Neuro-Adaptive Control Scheme to Improve Dynamic Stability of Wind Power System using Battery Energy Storage

This paper investigates the capability of the battery energy storage system (BESS) to enhance the stability of wind power systems using an improved intelligent control strategy based on neural identification and simultaneous control. The proposed neural identifer comprises of only a few adaptive parameters, which reduce the computational burden and make it easier to implement. It faithfully estimates the local linear model of a dynamic system and adjusts its parameters online for varying operating states of the system. The proposed neuro-adaptive controller needs local measurements only and is model-free. The performance of the proposed controller is compared with the lead-lag controller whose parameters are tuned using teacher learning-based optimization. The performance of the controllers is compared on the basis of various power system stability indices such as oscillation damping, wind-power smoothing and improving the voltage profile of the wind farm under varying operating conditions.