Conditions Of Parametric Uncertainty Research Articles

Disturbance rejection based consensus controller for swarm system

This article introduces a robust controller designed to facilitate consensus among leader-follower agents in a swarm system. The method employs algebraic graph theory to establish a consensus framework and incorporates a sliding surface-based uncertainty and disturbance estimator (SUDE) to mitigate the effects of nonlinearities, external disturbances, and parametric uncertainty inherent in swarm systems. The stability of the SUDE controller is examined using Lyapunov theory. Importantly, this approach operates without prior knowledge of disturbance bounds, relying instead on the dynamics of disturbance error. Simulation studies demonstrate the effectiveness of the proposed control methodology, showing that the SUDE controller expedites consensus achievement while ensuring smooth and non-chattering control behavior. The proposed SUDE controller demonstrates a notable improvement in achieving position and velocity consensus in both the X and Y directions, with a 75% and 66% faster convergence rate, respectively, when compared to the conventional sliding mode controller (SMC). Additionally, the performance of the proposed controller is assessed under various external disturbances and parametric uncertainty conditions. Comparative analyses using root mean square error (RMSE) and integral absolute time error (IATE), both based on consensus error, confirm the effectiveness of the proposed SUDE consensus controller.

KONTROL SLIDING MODE ADAPTIF DENGAN VAKSINASI DAN TREATMENT PADA MODEL PENYEBARAN TUBERKULOSIS

This research examines the design of adaptive sliding mode control in a tuberculosis (TB) disease spread model, considering uncertainties within the model. The TB model involves five state variables (susceptible individuals, vaccinated individuals, individuals with latent TB infection, individuals with active TB infection, and individuals under treatment) and three control input variables consisting of vaccination and treatment. The objective of this control design is to reduce the number of susceptible individuals, individuals with active TB infection, and the number of individuals under treatment by tracking the given reference functions. Stability and convergence of tracking errors of the controlled system are proved using the Lyapunov Stability Theorem. Numerical simulations are conducted to evaluate the performance of the designed control under various parameter uncertainty conditions. Based on the simulation results, it is shown that the adaptive sliding mode controllers guarantee the convergence of tracking errors is achieved despite the presence of uncertainty in the model.

Heuristic algorithms based optimal tuning of FOLQI controller for quadruple tank process under disturbance conditions

Design of centralised disturbance rejection controllers for an highly interacting MIMO quadruple tank process is tedious. Recently, centralised disturbance rejection Fractional Order LQI (FOLQI) controller is designed for such system to meet the desired specifications with better performance than various disturbance rejection controllers that are available in the literature. In this paper, an optimisation problem is formulated to obtain the optimal parameters of FOLQI controller providing minimum control effort by applying various widely used heuristic methods like Cuckoo Search (CS), Accelerated Particle Swarm Optimisation (APSO) and FireFly (FF) algorithms. A detailed simulation study is carried out to compare the performance of the FOLQI and Integer Order LQI (IOLQI) controllers obtained by these heuristic algorithms under disturbance and parameter uncertainty conditions. From the simulation study it is inferred that (i) FOLQI controller provides better time domain specifications % Mp, ts and J in comparison to IOLQI controllers and (ii) FF tuned FOLQI and IOLQI controllers provide better robustness characteristics compared to CS and APSO tuned controllers.

Improved control of integrating cascade processes with time delays using fractional-order internal model controller with the Smith predictor

This article proposes an enhanced control of integrating cascade processes. The importance of the scheme shows by controlling a double integrating process with time delay under significant parametric uncertainties and load disturbances. A novel design is proposed based on the Smith predictor principle and uses a fractional-order internal model controller in the outer loop. The required fractional-order tuning parameter follows the desired gain and phase margins. At the same time, another tuning parameter, such as the fractional filter’s time constant, is calculated from the desired performance constraint. Numerical analysis and comparison are performed to showcase the feature of the hybrid structure. The simulation results show that the fractional-order internal model controller–based scheme provides enhanced tracking and faster regulation capabilities and works well under nominal and parameter uncertainty conditions.

Centralised fractional order LQI controller design for quadruple tank process — An optimisation approach

In this paper, a centralised Fractional Order Linear Quadratic Integral (FOLQI) controller is proposed for highly interacting Multi Input Multi Output (MIMO) system to reject the load disturbance conditions. An optimisation problem to minimise the control effort as an objective function is formulated in addition to meet the desired time domain inequality constraints. The parameters of the FOLQI controller are obtained by using sequential quadratic programming optimisation approach. For illustration, the quadruple tank process is considered as a case study which exhibits both minimum and non-minimum phase characteristics. To show the performance of the FOLQI controller, the simulation is performed under disturbance and parameter uncertainty conditions. The performance indices such as maximum peak overshoot, settling time and steady state error are computed for FOLQI controller. To show the superiority of FOLQI controller, these indices are compared with the responses obtained using the existing Integer Order Linear Quadratic Integral (IOLQI) and Linear Active Disturbance Rejection (LADR) controllers.

Analysis dynamic characteristics brushless motor of the mechatronic system in conditions of parametric uncertaintyby computer simulation methods

THE PURPOSE. Currently, brushless motors – electric machines with permanent magnets on the rotor and a rotor position sensor controlled by a sinusoidal voltage from frequency converters, are widely used in mechatronic and robotic systems. The control algorithm is formed on the basis of information about the current values of the parameters brushless motor mechatronic system using a nominal or reference mathematical model, which is, as a rule, an idealized representation of a real device. The non-stationarity of the parameters object of study, as well as the possible uncertainty of its mathematical description due to the simplification of the mathematical model, lead to undesirable or unacceptable results when forming the control algorithm of the mechatronic system. The problem arises of analyzing the dynamic characteristics of a brushless motor under conditions of parametric uncertainty in order to determine the parameters that most affect the functioning of the mechatronic system and the phase coordinates that are sensitive to these changes.METHODS. When solving the problem, methods of the sensitivity theory are used to obtain the corresponding vector-matrix equations, the solution of which is carried out by means of the MatLab software environment.RESULTS. In this paper, sensitivity equations are obtained for the active resistance and projections of the stator winding inductance on the longitudinal and transverse coordinate axes, as well as for the moment of inertia of the brushless motor. A vector-matrix block diagram for calculating the sensitivity functions of a brushless motor is formed, the characteristic feature of which is the presence of a non-zero matrix of free terms, reduced to the input of the sensitivity model. The corresponding Simulink models were built to study the influence of the listed quasi-stationary parameters on the rotation speed and torque on the shaft of the object of study. An analysis of the statistical characteristics additional motion of the specified phase coordinates of the brushless motor has been carried out, and graphical dependencies and steady-state values of dispersions and relative estimates have been obtained.CONCLUSION. An analysis of the dynamic characteristics of a brushless motor under conditions of parametric uncertainty made it possible to determine that the rotation speed of the machine is the most sensitive to parametric disturbances. This coordinate is the most informative and is of maximum interest in the formation of an optimization algorithm for a mechatronic system. The decisive role in the formation of the additional movement of the output coordinates of the brushless motor is made by a change in the projection of the stator inductance on the transverse coordinate axis, which is an order of magnitude greater than the contribution to the additional movement of coordinates from other unstable parameters. It is expedient to use the results obtained in the course of the study when constructing an optimal control algorithm for a mechatronic system under conditions of parametric uncertainty.

Current Sensorless MPPT Control for PV Systems Based on Robust Observer

Photovoltaic (PV) systems are among the most used alternatives for electrical power generation from renewable sources. To ensure that PV systems make the most of the available solar energy, maximum power point tracking (MPPT) schemes must be implemented, which usually require voltage and current sensors to track the PV power. This paper presents the design of a robust observer using the Attractive Ellipsoid Method to achieve a precise estimation of PV current under parametric uncertainty and output perturbations. The application of such an observer enables the PV generation system to operate in a current sensorless mode, which reduces the overall cost of the system and enhances its reliability. The convergence of the observer is guaranteed by solving an optimization problem which generates the optimal gains using Linear Matrix Inequalities (LMI). To prove the effectiveness of the proposed sensorless scheme, simulations are performed in Matlab under test profiles based on the EN50530 standard and parameter uncertainty conditions, obtaining an accurate estimation which is used for MPPT operation.

Process Optimization under Parameter Uncertainty Conditions in CCU Process

In order to produce methanol using CO2 and H2, several processes about the related catalytic reaction has been suggested and simulated. There may be systematic uncertainties like parameters of reaction kinetics, so a modelling method considering the parametric uncertainty is proposed and can give more informative data compared to the conventional modelling methods. However, the resulting distributional model requires large computational burdens due to the iterative calculations for convergence. To solve the problem, generalized extreme value distribution (GEVD) and neural network (NN) modellings are utilized. The formation parameters of GEVD are fitted by NN and as a result distributional reactor model in an explicit formulation is proposed. Compared to the shallow structured neural network for learning the formulation parameters, the deep neural network shows improved performance especially adjacent to the boundary layers of process inputs. As its explicit and distributional formulation, the proposed model is expected to be utilized for real-time stochastic model based approaches in optimization and control with reduced computational load.

Machine learning for quality of transmission: a picture of the benefits fairness when planning WDM networks

In the present day, the evaluation of machine learning (ML) as a candidate for substituting analytical quality of transmission (QoT) estimators is done in a compartmentalized way. The assessment is not produced from a global optical network design perspective and with accurate optical design metrics; on the contrary, the evaluation heavily focuses on the physical layer impairment precision capabilities while underemphasizing the effects at the network layer. In this paper, we recommend a suitable methodology for evaluating the QoT substitution based on the foundational idea that different QoT estimators should be examined on a comparative basis by analyzing network-relevant metrics at parity of availability performance. Pragmatically, we recommend comparing performance estimation solutions through the aggregate network throughput, i.e., capacity, at the equity of their overestimation likelihood, which drives system margins. To demonstrate the need for such a network viewpoint and illustrate the potential drawbacks of an inadequate assessment of the QoT substitution, we use the proposed method in several scenarios (altering network topologies, input parameter uncertainty conditions, and availability requirements), showing that we can achieve gains in QoT estimation error or design margins while observing notable losses in terms of network throughput. Considering the results were contrary to what one may expect, we decided to undergo a statistical analysis in order to investigate and grasp the consequences of the model error distribution in relation to the network capacity.

Automatic calibration of the two-dimensional hydrodynamic and water quality model using sequential uncertainty fitting approach.

Reservoir hydrodynamic and water quality modeling, in conjunction with the monitoring programs, is one of the essential tools for controlling the pollution of these types of water bodies. The complexity of the model, data scarcity, and the variable nature of natural phenomena lead to uncertainty in models, which should be considered in the calibration process of these models. Uncertainty-based automatic calibration is one of the methods that can be effective in achieving a high-reliability model. In this paper, the Sequential Uncertainty Fitting (SUFI-2) algorithm was used for the automatic calibration of the two-dimensional hydrodynamic and water quality model (CE-QUAL-W2) for the reservoir under parameter uncertainty conditions. To this end, the CE-QUAL-W2 model was developed to simulate the temperature and water surface elevation of the Karkheh Dam reservoir (western Iran). The parameters affecting temperature were regarded as uncertain parameters in the calibration process, including the coefficients of longitudinal eddy viscosity, longitudinal eddy diffusivity, Chezy coefficient or Manning, wind sheltering, solar radiation absorbed in the surface layer, extinction coefficient for pure water, and the experimental coefficients of wind speed function. The developed method demonstrated a high potential for matching the simulated temperature and water surface elevation for the reservoir with the measured data. Averagely, 69% of the simulated temperature and 90% of the simulated water surface elevation were located within the 95% confidence interval. The SUFI-2 algorithm also showed better performance in terms of the convergence rate compared with the particle swarm optimization (PSO) algorithm, which indicated a lower number of calls (80 calls compared to 2000 calls) and could reduce the total root-mean-square error by 9.6%.

Development of intelligent technology for control complex objects based on a unified artificial immune system

The research is devoted to the urgent problem of creating the fundamental theoretical foundations of artificial immune systems in order to develop a highly effective innovative technology for the synthesis of intelligent control systems for complex objects that function under conditions of uncertainty in parameters and further use in industrial automation systems and modern microprocessor technology. The definition of a unified artificial immune system is given and the relevance of the development of this direction is shown. Refs.: 15 titles.Keywords: intelligent technology; control system of complex object; unified artificial immune system.

INCREASING THE ROUGHNESS OF THE PROCEDURE FOR EVALUATING A VECTOR OF THE CONDITION OF OBJECTS TO THE INFLUENCE OF UNCERTAINTY FACTORS

Algorithms for increasing the roughness of the procedure for assessing the state vector of control objects to the influence of uncertainty factors are given. Expressions are obtained for extended state vectors and observations. Stable inversion algorithms are given for a nondegenerate block matrix with the allocation of its left and right zero divisors of maximum rank. The presented stable computational procedures allow us to regularize the problem of synthesis of algorithms for estimating the parameters of regulators in adaptive control systems with a customizable model and to improve the quality indicators of control processes under conditions of parametric uncertainty

Identification of Energy-Hidden Chirp Signals of Telecommunication Systems in Conditions of Parametric Uncertainty

The ambiguity diagram of rectangular chirp RF pulse has been analyzed. The characteristic point of ambiguity diagram was identified. It was proposed to identify the signal on the basis of correlation level at the characteristic point of ellipsoidal ambiguity diagram built in a special coordinate system. The quasi-optimal autocorrelation algorithm with quadrature processing is proposed. This algorithm is resistant to a priori uncertainty of parameters of input energy-hidden signals with unknown waveform and unknown initial phase against the background of Gaussian stationary noise. The tuning parameters of identification scheme and the decision-making rule regarding the availability of chirp signal in the input mixture were determined. The simulation modeling of identification procedure was conducted using the software package Matlab R2016a. The simulation results confirmed the ability of the proposed algorithm to identify the chirp signal in the input mixture at small values of the signal-to-noise ratio.

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

Zero-dimensional models based on the description of the thermo-gas-dynamic process are widely used in the design of engines and their control and diagnostic systems. The models are subjected to an identification procedure to bring their outputs as close as possible to experimental data and assess engine health. This paper aims to improve the stability of engine model identification when the number of measured parameters is small, and their measurement error is not negligible. The proposed method for the estimation of engine components’ parameters, based on multi-criteria identification, provides stable estimations and their confidence intervals within known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularized identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Synthesis of objective functions and subsequent scalar convolutions of these functions are used to estimate gas-path components’ parameters. A comparison with traditional methods showed that the main advantage of the proposed approach is the high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions that do not correspond to a priori assumptions and also helps to implement the gas path analysis with a limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, gas path analysis and adapting dynamic models to the needs of the engine control system.

Control of the mechanical system oscillation with a drive under conditions of parametric uncertainty

A method for adaptive control of oscillations by cascade systems based on the energy approach and the method of highspeed big-gradient with an adjustable model is proposed. The control objectives are all trajectories limitation of a closed system and the excitation in the mechanical subsystem of oscillations with a given energy level, as well as the identification of unknown parameters for the mechanical subsystem. An example and results of computer simulation are given. Keywords high-speed big-gradient method; cascade system; adaptive management; Lyapunov function; speed gradient algorithm

Synthesis of a Multifunctional Tracking System in Conditions of Uncertainty

Class of affine nonlinear single-input single-output systems, where the relative degree of the equivalent form of the input-output is invariant to the presence of external, unmatched disturbances, is formalized. Methods of synthesis of a multifunctional tracking system in the conditions of parametric uncertainty of the control plant model and incomplete measurements are designed for this class of systems. The original method of synthesis of a low dimension observer for estimating mixed variables (these are combinations of state variables, external influences and their derivatives) by measuring only tracking error is designed for information support of discontinuous control. In this observer, using the linear corrective effects with saturation, the method of separating the movements of observation errors is realized. As an illustration of the developed method, an electromechanical control object is considered-an inverted pendulum controlled by a DC motor. The simulation results for the worst case of varying parameters are given.

A robust power control strategy to enhance LVRT capability of grid‐connected DFIG‐based wind energy systems

AbstractThis paper presents a new robust and effective control strategy to mitigate symmetrical voltage dips in a grid‐connected doubly fed induction generator (DFIG) wind energy conversion system without any additional hardware in the system. The aim is to control the power transmitted to the grid so as to keep the electrical and mechanical quantities above their threshold protection values during a voltage dip transient. To achieve this, the references of the powers are readjusted to adapt the wind energy conversion system to the fault conditions. Robust control strategies, combining the merits of sliding mode theory and fuzzy logic, are then proposed in this paper. These controllers are derived from the dynamic model of the DFIG considering the variations in the stator flux generated by the voltage drop. This approach is found to yield better performance than other control design methods which assume the flux in the stator to remain constant in amplitude. This control scheme is compliant with the fault‐ride‐through grid codes which require the wind turbine generator to remain connected during voltage dips. A series of simulation scenarios are carried out on a 3‐MW wind turbine system to demonstrate the effectiveness of the proposed control schemes under voltage dips and parameter uncertainty conditions.

Estimation of performance parameters of turbine engine components using experimental data in parametric uncertainty conditions

Gas Path Analysis and matching turbine engine models to experimental data are inverse problems of mathematical modelling which are characterized by parametric uncertainty. It results from the fact that the number of measured parameters is significantly less than the number of components’ performance parameters needed to describe the real engine. Inthese conditions, even small measurement errors can result in a high variation of results, and obtained efficiency, lossfactors etc. can appear out of the physical range. The paper presents new method for setting a priori information about the engine and its performance in view of fuzzy sets, forming objective functions and scalar convolutions synthesis of these functions to estimate gas-path components’ parameters. The comparison of the proposed approach with traditional methods showed that its main advantage is high stability of estimation in the parametric uncertainty conditions. It reduces scattering, excludes incorrect solutions which do not correspond to a priori assumptions, and also helps to implement the Gas Path Analysis at the limited number of measured parameters.

Preliminary Design With the Epistemic Uncertainty of Parameters

In design, especially in preliminary design, the assumption of parameter accuracy is not justified, since the parameters here are inaccurate (uncertain), due to insufficient knowledge or lack of statistics, as well as the fact that design parameters are further implemented in the production with some tolerance. The application of deterministic optimization methods under conditions of parametric uncertainty can lead to unacceptable solutions even with slight variation in the parameters. Currently, to account for uncertainty of the parameters there are commonly used stochastic methods designed to account for aleatory uncertainty with a priori known distribution functions of random parameters. However, in the preliminary design, most of the parameters are not random variables with known distribution functions. The necessary information on the parameters is obtained from the experts. In this paper, we develop methods and algorithms for preliminary design in conditions of epistemic uncertainty arising from lack of knowledge and observation results, replenished by expert assessments. In the paper the problem of optimal design in the presence of input and design parameters with epistemic uncertainty is considered. The choice of Liu's uncertainty theory for solving the problems of preliminary design is justified. The model of uncertain design parameter and optimization model with uncertain design and input parameters are proposed. The task of optimal design of the propulsion system parameters of supersonic maneuverable airplane is solved using the proposed models. The results are compared with the solution using Monte Carlo method. The solution time using the proposed model is two orders of magnitude less.

Synthesis of robust controllers for the control systems of technological units at iron ore processing plants

In order to synthesize a robust system of control over technological units, an analysis of appropriate mathematical models was performed. The uncertainty of models of technological iron ore processing units was accounted for by connecting a diagonal block at the top using a fractional linear transformation. To study robust control systems of technological units, we applied the following types of robust controllers: suboptimal H ∞ -controller, a controller that was synthesized using the method of circuit formation, and µ-controller. We performed an analysis of results of the study into indicators of robust quality and stability of control, created on the basis of these types of controllers. The best results were obtained using μ-controller, which ensures a minimal overshoot value of 2 %. Reducing the order of the selected μ-controller to the fourth order was performed by approximation using the Hankel norm. Under such a condition, a root-mean-square error relative to the base controller is 0.027. Results of present research could be used in the synthesis of control over technological iron ore processing units under conditions of uncertainty in parameters.