In this paper, the robust gain-scheduled state-feedback controller problem is studied for uncertain Linear Parameter-Varying systems whose state-space representations are the Linear combination of the uncertain time-Varying Parameters including time-invariant parametric uncertainties (TIPU). It is supposed that these uncertainties are bounded by the given intervals and cannot be pulled out as an uncertain block. It is considered a serious challenge because the exact information about the plant dynamics cannot be extracted from the uncertain time-Varying Parameters. This is while; the gain-scheduled controllers need to have the exact information about the plant dynamics to satisfy the desired control purposes. To handle this challenge, we introduce a state-feedback gain, which is formed by a set of the new scheduling Parameters and a secondary time-Varying term. The stabilization conditions are obtained in terms of the Linear matrix inequalities (LMIs). The effectiveness of the proposed method is shown using an example.

In this article, a new method is proposed to design robust dynamic output feedback control for switched uncertain continuous-time Linear Parameter Varying (LPV) systems. The system matrices are supposed to depend on both the time-Varying scheduling and uncertain Parameters. Hysteresis switching law is exploited for the switching controller synthesis. Firstly, the robust switching controllers are robustly designed so that the stability and the induced L2-gain performance of the switched closed-loop uncertain LPV system can be guaranteed. The usual switching logics can cause discontinuous chattering control signal which are not acceptable in a practical situation. Accordingly, a smooth switching strategy for control design has been tackled in the following. By utilizing an independent family of Parameter-dependent Lyapunov matrices and slack variables, performance improvement is achieved when compared to the previous works. The proposed method is formulated in terms of solutions to a set of Parameter-dependent LMIs using the presented iterative algorithm. Finally, an inverted pendulum on a cart is illustrated to verify the advantages of the presented approach.

This paper proposes a new computed torque strategy to control the robotic manipulators using polytopic Linear Parameter Varying (LPV) modeling method. An LPV representation of the robot is generated by identification of its dynamic in different workspace points about an arbitrary full-path trajectory. Identification of the models is done by using the least square of errors algorithm. By utilization of Parameter set mapping based on Parameter component analysis (PCA) a reduced polytopic LPV model is obtained that reduces the complexity of the implementation. In the conventional computed torque control, it is necessary to know the dynamical model of the robot while in the proposed method an estimation of the required torque is computed based on the obtained LPV model. The control gain matrix is derived by solving a set of Linear matrix inequalities (LMIs) that takes the time derivative of the Lyapunov function to the sufficiently small value. Sufficient conditions are provided to ensure the asymptotic stability of the closed loop LPV system against the torque estimation error. The proposed scheme is applied to trajectory tracking control of a six degree of freedom (DOF) PUMA 560 robot manipulator.

Background and Objectives: In this paper, a novel Linear Parameter Varying (LPV) model of a wind turbine is developed based on a benchmark model presented by Aalborg University and KK-electronic a/c. The observability and validity of the model are investigated using real aerodynamic data.Methods: In addition, a robust fault detection and reconstruction method for Linear Parameter Varying systems using second-order sliding mode observer is developed and implemented on the Linear Parameter Varying model. The fault signal is reconstructed using a nonLinear term named equivalent output error injection during sliding motion and a proper transformation. The effect of uncertainties and incorrect measurements are minimized by employing an oriented method that requires solving a nonLinear matrix inequality. During numerical simulations, an actuator fault in the pitch system is considered and the performance of the method in fault reconstruction is investigated.Results: Wind speed range is considered from 14 m/s to 16  and it is regarded as a stochastic input exerting aerodynamic torque. Fast and accurate fault reconstruction happens in 0.6 seconds with less than one percent error. The observer performance is not affected by the fault and fault is estimated in 2.5 seconds with an error smaller than 2.48 percent.Conclusion: Results illustrate fast and accurate fault reconstruction and accurate state estimations in the presence of actuator fault.

Fault occurrence in real operating systems usually is inevitable and it may lead to performance degradation or failure and requires to be meddled quickly by making appropriate decisions, otherwise, it could cause major catastrophe. This gives rise to strong demands for enhanced fault tolerant control to compensate the destructive effects and increase system reliability and safety in the presence of faults. In this paper, an approach for estimation and control of simultaneous actuator and sensor faults is presented by using integrated design of a fault estimation and fault tolerant control for time-Varying Linear systems. In this method, an unknown input observer-based fault estimation approach with both state feedback control and sliding mode control was developed to assure the closed-loop system's robust stability via solving a Linear matrix inequality formulation. The presented method has been applied to a Linear Parameter Varying system and the simulation results show the effectiveness of this method for fault estimation and system stability.

In this paper, a new approach is presented to design a gain-scheduled state-feedback controller for uncertain Linear Parameter-Varying systems. It is supposed that the state-space matrices of them are the Linear combination of the uncertain scheduling Parameters. It is assumed that the existed uncertainties are of type of timeinvariant parametric uncertainties with specified intervals. Simultaneous presence of the concepts of the gainscheduling and the time-invariant uncertainties is a serious challenge. Because, the philosophy of the gainscheduling is variability with time. But, the time-invariant parametric uncertainties are constant and unknown. To obviate this challenge, a robust state-feedback law is proposed that is robust against the time-invariant uncertainties. In this method, the arbitrary values are selected for the uncertainties from the defined intervals. But, the selected values are not necessarily equal to the true ones. Hence, the new scheduling Parameters are presented to calculate the proposed controller. Finally, the proof of the proposed scheme is presented based on Lyapunov concept. To show the effectiveness of the final controller, the proposed method is simulated to stabilize the roll rate of a typical missile. Also, the simulation results are compared with the experimental ones in the presence of the INS (Inertial Navigation System) module.

This paper presents a modified method for approximating systems by a sequence of ‎Linear time Varying systems. The convergence proof is outlined and the potential of ‎this methodology is discussed. Simulation results are used to show the effectiveness ‎of the proposed method‏.‏

AbstractFinancial statement fraud has become a serious problem for market participants and policy makers. In fact, it threatens the reliability of capital markets, corporate executives and even the auditing profession. The purpose of this study is to use the approach of dynamic averaging models to predict fraud in financial statements.The present research is applied in terms of method. The research period is 1390 to 1399 and in estimating the model, the data of selected companies in Tehran Stock Exchange has been used.Using the systematic elimination approach, the research sample size of 125 companies was selected. To estimate the model, MATLAB 2021 software has been used.In this research, based on the dynamic averaging model, we predicted the fraud and accuracy of the estimation models. Based on the results of asset return variables; Return on equity; Operating profit margin; Asset turnover ratio and operating cash-to-sales ratio have a negative effect on fraud and other variables have a positive effect.

A novel data-driven soft sensor is designed for online product quality prediction and control performance modification in industrial units. A combined approach of time variable Parameter (TVP) model, dynamic auto regressive exogenous variable (DARX) algorithm, nonLinear correlation analysis and criterion-based elimination method is introduced in this work. The soft sensor performance validation is tested by data set of an industrial SRU. The comparative study indicated the result associated with more robust soft sensor and more appropriate performance index values compared to other methods for SRU soft sensor design in diverse achievements. Due to high prediction accuracy, the low complication of the model and also saving of time, this technique can be very noticeable in industrial processes control.