Input-output Feedback Linearization Control Research Articles

Sliding Input Output Control of Squirrel Cage Motor Based NPC Five Level Inverter

Speed squirrel cage motor control is an area of research that has been in evidence for some time now. In this paper, a nonlinear controller is presented for the squirrel cage motor drives, based on a combination between input-output feedback linearization control (IOLC) technique and sliding mode control (SMC) to create a new control which is sliding input-output linearization (SIOLC) control of squirrel cage motors which associates by an NPC five levels inverter PWM technical used for the variation of its speed, where the sliding mode control it used for controlling the speed of squirrel cage motor and the input-output linearization control applied for two input witch are flux and current. To test robustness and performance of sliding input-output linearization control (SIOLC) we created a variation of internal parameters of the motor. The simulation results show robustness the sliding input-output linearization control of squirrel cage motor responses.

An Extendable Bidirectional High-Gain DC–DC Converter for Electric Vehicle Applications Equipped With IOFL Controller

High gain bidirectional dc-dc converters are so popular in many applications such as drives, photovoltaic systems, Electric Vehicles (EV) and etc. This paper proposes an extendable bidirectional common-ground dc-dc converter suitable for high gain applications such as EVs. The proposed converter uses fewer number of switches to provide high step-up and high step-down voltage converting capabilities in both power flow directions. Since the input and output of this converter share the same ground, the electromagnetic interface (EMI) is alleviated. In order to assess the versatility of the proposed dc-dc converter and to achieve a fast response, by equipping this converter with an Input-Output Feedback Linearization (IOFL) controller, its performance is analyzed in EV applications. To this end, the steady state calculations are worked out for both charge and discharge directions, then dynamic analysis and control design is presented based on nonlinear controller by IOFL. Simulation and experimental results are provided to prove the feasibility of the proposed converter.

INPUT-OUTPUT LINEARIZATION CONTROL BASED ON THE SLIDING MODE OF THE SQUIRREL CAGE MOTOR

Speed squirrel cage motor control is an area of research that has been in evidence for some time. In this paper, a nonlinear controller is presented for the squirrel cage motor drives, based on a combination between input-output feedback linearization control (IOLC) technique and sliding mode control (SMC) to create a new control which is sliding input-output linearization (SIOLC) control of squirrel cage motors, where the sliding mode control is used for controlling the speed of squirrel cage motor and the input-output linearization control applied for two input witch are flux and current. To test the robustness and performance of sliding input-output linearization control (SIOLC) we created a variety of internal and external parameters of the motor. The simulation results are done using Matlab/Simulink, which shows the robustness of the sliding input-output linearization control of squirrel cage motor responses.

Adaptive Feedback Linearization Control of SynRM Drives With On-Line Inductance Estimation

This article proposes an adaptive input-output Feedback Linearization Control ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FLC</i> ) techniques for Synchronous Reluctance Motor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SynRM</i> ) drives, taking into consideration the iron losses. As a main original content, this work proposes a control law based on a new dynamic model of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SynRM</i> including iron losses as well as the on-line estimation of the static inductances. The on-line estimation of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SynRM</i> static inductances permits to inherently take into consideration the magnetic saturation phenomena occuring on both axes. As a major result, it permits a null stator current steady state tracking error even with a proportional derivative controller. The estimation law is obtained thanks to a Lyapunov-based analysis and thus the stability of the entire control system, including the estimation algorithm, is intrinsically guaranteed. The proposed adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FLC</i> technique, has been tested experimentally on a suitably developed test set-up, and compared experimentally with its non-adaptive versions in both tuned and detuned working conditions. Moreover, a sensitivity analysis of the performance of the adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FLC</i> to the variations of the stator resistance at low speed has been made. Finally, an analysis of the effects of the iron losses on the control performance and stability at high speed in the field weakening region at medium/high loads has been made.

Q-learning based model-free input-output feedback linearization control method⋆

Input-output feedback linearization is a nonlinear control method that relies on a precise dynamical model. Combining Q-learning techniques, an input-output feedback linearization correction framework is presented to accomplish model-free feedback linearization of affine nonlinear systems in order to tackle the problem caused by the unknown dynamics model. This framework formulates a model reference tracking control problem that guides the input-output relationship of the nonlinear system into a linear relationship. Due to the two Lie derivative terms present in the feedback linearized controller, the controller is designed as a dual network structure. To overcome the issue of coupling in the dual-network controller, a model-free Q-learning method is presented to solve the unknown controller network weights. The proposed method is experimentally validated on a single-link flexible joint manipulator system, and the resultant linearized system exhibits dynamics similar to the desired linear system in a new tracking task, proving the effectiveness of the proposed method.

Effect of Defective Five Levels Inverter on Input Output Linearization Control for Induction Motor

Induction Motor (IM) speed control is an area of research that has been in prominence for some time now. In this paper, a nonlinear controller is presented for induction motor drives. The nonlinear controller is designed based on input-output feedback linearization control technique. The input-output feedback linearization control decouples the flux control from the speed control and makes the synthesis of linear controllers possible. This article presented input output linearization control for induction motor associated with NPC three levels inverter defective, the inverter faults are usually caused by operating faults in the switch elements. Switching faults occur in rectifier diodes, the capacitor and IGBT switches of the inverter. The inverter switches defected reduce the performance of the motor, in our study , we apply the input-output linearization control to test their robustness and performance for detection of fault influence on the physical parameters of the motor, for this purpose we apply two faults, we start by creating a fault in two switches K1 and K7 which have in the first and second arm of the inverter then we create a faults in the three switches K1, K7 and K10 which are in the three arms of the inverter. The simulations results are done by the use of MATLAB/Simulink that show the detection fault effect by input output linearization control on the different induction motor responses.

Input-output feedback linearization control of a tractor with n-trailers mechanism considering the path curvature

In this paper, an observer-based proportional-integral-derivative (PID) controller is proposed for an autonomous tractor with n-trailer units with prescribed transient and steady-state performances by compensating the negative effect of the path curvature during the turning of the trailers. The control objective is to place the position of the last trailer on a desired trajectory without any deviation in the course of its turning. Since the curvature of the trajectory causes a remarkable unwanted deviation of the trailers’ positions from the path, an input-output model is derived for the tractor with n-trailers based on the extended look-ahead control concept which employs a corrective angle to compensate the path curvature. A nonlinear transformation is employed to transform constrained errors into unconstrained ones and to obtain an open-loop error dynamic model in terms of the unconstrained errors. Then, a neural network adaptive PID output-feedback linearization-based controller is proposed to force the tracking errors to the zero neighbourhood with a guaranteed prescribed performance with preferable overshoot/undershoot, convergence speed and final tracking accuracy. The Kalman–Yakobovich (KY) lemma is employed to prove the closed-loop stability of the observer-controller system strongly. Numerical simulations are given to show the controller’s effectiveness in practice.

Input–Output Feedback Linearization Control of a Linear Induction Motor Taking Into Consideration Its Dynamic End-Effects and Iron Losses

This article proposes a new input&#x2013;output feedback linearization control (FLC) technique of linear induction motors (LIMs), taking into consideration both the dynamic end-effects and the iron losses. Starting from a previously conceived dynamic model, including the dynamic end-effects and the iron losses, all the theoretical framework of the FLC has been developed. The proposed FLC improves a previous version of FLC in accounting also the iron losses, which in LIMs with fixed-secondary sheet play a pivotal role more than in rotating induction motors (RIMs). The proposed FLC has been experimentally tested on a suitably developed test setup, and experimental comparisons between the proposed FLC, the classic field-oriented control and a previously developed FLC, not accounting for the iron losses, have been shown in variable flux working conditions.

Effect of defective NPC three level inverter on nonlinear command of induction motor

Speed Induction Motor (IM) control is an area of research that has been in evidence for some time now. In this paper, a nonlinear controller is presented for the induction motor drives. The nonlinear controller is designed based on an input-output feedback linearization control technique. The input-output feedback linearization control decouples the flux from the speed control and makes the synthesis of linear controllers possible. This article presented input-output linearization control of the induction motor associated with NPC three level inverter defective, the inverter faults are usually caused by operating faults in the switch elements. Switching defects occur in rectifier diodes, the capacitor and inverter IGBT switches. The inverter switches defected reduce the performance of the motor, in our study. We applied the input-output linearization control to test their robustness and performance for detection of fault influence on the physical parameters of the motor, for this purpose, we applied two faults, we started by creating a fault in two switches K1 and K7 which are in the first and second arm of the inverter. Then we created a fault in the three switches K1, K7 and K10 which are in the three arms of the inverter. The simulation results are done by the use of Matlab/Simulink that show the detection, fault effect on input-output linearization control of the different induction motor responses.

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

Combined adaptive neural network and regressor‐based trajectory tracking control of flexible joint robots

By relying on the input–output feedback linearization approach, a novel adaptive controller for flexible joint robots is proposed in this work. First, a model-based controller is developed to get a structure that is useful in the development of the adaptive controller. The adaptive version is developed by using two techniques. To stabilize the output function, an adaptive neural network controller is used, which approximates the non-linear function that contains the uncertainties. The desired rotor position required by the input–output feedback linearization controller is defined with the structure of a link dynamics adaptive regressor-based controller. The main reason to adopt the mentioned structure in the definition of the desired rotor link position is to guarantee its differentiability. Real-time experiment comparisons among the model-based controller, a model-based controller with desired compensation, an adaptive controller based on joint torque feedback, and an adaptive neural network-based controller are carried out. Experimental results support the theory reported in this document and the accuracy of the proposed approach.

Temperature Gradient Control of the Solid Oxide Fuel Cell under Variable Load.

Nowadays, the temperature gradient is considered as one of the most important parameters which impact the performance of the solid oxide fuel cell (SOFC). In this paper, a control strategy based on an input–output feedback linearization technology is derived for controlling the maximum temperature gradient within the anode fuel flow channel at the desired value. For the controller design, the temperature dynamic model is proposed and simplified to a control-oriented multi-input and multioutput nonlinear dynamic model. Then, this paper presents an input–output feedback linearization controller to realize the control objective by adjusting the cathode input air flow. Finally, the simulation results are given to demonstrate the accuracy of the proposed model in reflecting the temperature dynamic characteristics. Moreover, the compound controller is added for simulation as a comparison, which shows that the proposed controller is equipped with better effectiveness and efficiency in the presence of external disturbances.

An Input–Output Feedback Linearization Control Method Synthesized by Artificial Neural Network for Grid-Tied Packed E-Cell Inverter

In this article, a trained artificial neural network (ANN) input-output feedback linearization (IOFL) control strategy is proposed for a grid-connected nine-level packed E-cell (PEC9) converter under various operating variations. For the first time, a second-order dynamic model in d-q reference frame is developed for PEC9 converter. This model enforces the converter to employ IOFL-based proportional-resonance compensators for accurately contributing to current tracking enhancement, mitigation at the current ripple, and consequently, the capacitors dc voltages ripple. As another contribution, the current tracking ability of the proposed controller is much more promoted by decoupling the d-q dynamic components. Indeed, the effect of each proposed control component is fortified through decoupling feedback coefficients. In addition, as the third contribution, ANN as the complementary controller adapts the proposed IOFL-based controller coefficients to improve efficiency and stability of the whole control loop system, including the PEC9 capacitor voltages in unstable operation of load, grid, and dc source as well as in the presence of parameters mismatch and disturbances. To support the aforementioned contributions with more details, the closed-loop description and the dynamic model of the proposed controller-based PEC9 converter are utilized to present several stability evaluation processes in both time and frequency domains. This assessment process is accurately executed to confirm the stable performance of PEC9 when the errors of the PEC9 converter currents are changed as well as the decoupling feedback coefficients are varied. Finally, comparative dSPACE setup-based experimental and simulation results are attained to further verify the validity of the proposed control strategy under various operating conditions.

Decoupled Active and Reactive Power Control of a Grid-Connected Inverter-Based DG Using Adaptive Input–Output Feedback Linearization

This paper presents a new direct active and reactive power control for DC–AC converters connected to three-phase network through a filter and lossless/lossy line. The control is based on adaptive input–output feedback linearization control (AIOFLC). The controller computes the reference voltages of the inverter to bring active and reactive power errors to zero without any inner current loop. With a suitable Lyapunov function, the control law and the estimation laws for inductance and resistance of filter and line are obtained in synchronous reference frame. The proposed controller has a simple structure which needs less computation and increases accuracy. Using space vector pulse width modulation (SVM), a fixed switching frequency is achieved which simplifies harmonic design of AC filter and more utilizes DC voltage source. The parameters of filter and line are uncertain or unknown; only their nominal values are available. Simulation results show the capability and effectiveness of the proposed AIOFLC in direct active and reactive power control in several case studies considering various unknown filter/line parameters.

Integral Sliding Mode Control for Starting Speed Sensorless Controlled Induction Motor in the Rotating Condition

Starting in the rotating condition is an important technology for induction motor (IM) speed sensorless control. The key of this technology is that the initial speed of the IM in the rotating condition should be estimated quickly and accurately. In this article, an integral sliding mode control method based on the nonlinear model of the rotor back electromotive force is designed to start a speed sensorless controlled IM in the rotating condition. The stability is guaranteed by Lyapunov stability analysis, and the robustness to rotor time constant and disturbance are analyzed. Meanwhile, in comparison with the proposed control method, the existing input–output feedback linearization (IOFL) control method is introduced and the stability and robustness are also analyzed. Compared with the existing IOFL control method, the proposed control method can estimate the initial speed in the rotating condition without overshoot and has better dynamic performance and robustness. In addition, the proposed control method can be applied not only to the restart operation but also to the normal operation. Only a single control strategy is required from the restart operation to the normal operation. The proposed method is verified by simulation and experiments using a 7.5 kW IM.

A fault monitoring approach using model-based and neural network techniques applied to input–output feedback linearization control induction motor

This paper presents a contribution to the fault monitoring approach and input–output feedback linearization control of the induction motor (IM) in the closed-loop drive. Two kinds of faults are considered in the machine, particularly the broken rotor bars and stator inter-turn short circuit faults. This approach has been employed to detect and identify simple and mixed defects during motor operation by utilizing advanced techniques. To achieve it, two procedures are applied for the fault monitoring: The model-based strategy, which used to generate a residual speed signal to indicate the presence of possible failures, by means the high gain observer in the closed-loop drive. However, this strategy is not able to recognise the type of faults but it can be affected by the disturbances. Therefore, the neural network (NN) technique is applied in order to identify the faults and distinguish them. However, the NN required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform and fast Fourier transform is applied to extract the amplitude of the harmonics and used them as an input data set for NN. The obtained results show the efficiency of the fault monitoring system and its ability to detect and diagnosis any minor faults in a closed loop of the IM.

Sensorless Non-linear Control Applied to a PMSM Machine Based on New Extended MVT Observer

The focus of this paper is on the notion of an extended observer based on Input–output feedback linearization control. The mean value theorem (MVT) and sector non-linearity approaches were implemented for a class of the Lipchitz model of Permanent Magnet Synchronous Machine (PMSM). Based on these mathematical approaches, the proposed design allows the expression of nonlinear error dynamics for the extended observer become a convex combination of known matrices that have time varying coefficients. This is similar to a linear parameter varying (LPV) systems after the non-linear system of the PMSM is represented in the Lipchitz form. Through the Lyapunov theory, one can obtain and express the stability conditions in a term of linear matrix inequalities (LMI). The extended observer gain is separately determined based on the separation principles by the exploitation of YALMIP software. Furthermore, this gain have complete independence from the PMSM states. As a proof of the efficacy of the proposed approach, one applies an illustrative simulation to the sensorless Field Oriented Control (FOC) of the PMSM drive by utilising the MATLAB/SIMULINK environment.

Adaptive Input-Output Feedback Linearization Control of Doubly-Fed Induction Machine in Wind Power Generation

The present paper aims to present an adaptive, nonlinear control of wind energy conversion system using doubly fed induction generator (DFIG). The aim is to track the optimal torque-speed characteristic of the wind turbine in order to extract the maximum power, as well as to control the reactive power transmitted to the electrical grid. The adaptive nonlinear controller is designed on the basis of input-output feedback linearization control (FLC) with the full order model of the DFIG in the fixed stator d, q axis reference frame. In order to ensure the system stability under uncertain parameters such as rotor and stator windings resistances and the aerodynamic torque which comes to compromise the robustness and the efficiency of the controller, update laws in real time of the uncertain parameters are established. The global stability of the system is proven using Lyapunov's stability theory. Simulation results have been carried out in Matlab/Simulink in presence of uncertain parameters also, the comparison between the control with and without adaptation confirm the validity and robustness of proposed controller.

Adaptive Torque and Flux Control of Sensorless IPMSM Drive in the Stator Flux Field Oriented Reference Frame

In this paper, direct torque and flux control of interior permanent magnet synchronous motor has been described in the stator flux field-oriented reference frame based on adaptive input-output feedback linearization (AIOFL) control method. The proposed control method does not need to know the motor two-axis inductances, the rotor permanent magnetic flux linkages, and the rotor position. However, the knowledge of the stator resistance is mandatory and that is estimated by AIOFL method. In practice, the rotor speed is approximately estimated by using the derivation of the stator flux vector angle in the stator stationary reference frame. The experimental and simulation results presented in this paper show the effectiveness and capability of the proposed control approach.

Robust Control for High Performance Induction Motor Drives Based on Partial State-Feedback Linearization

This paper deals with a robust input–output feedback linearization control technique for induction motors. Indeed, classic feedback linearization presents two main disadvantages: 1) the accuracy of the dynamic model; and 2) the corresponding correct knowledge of the model parameters. To address this issue, the linear controller has been substituted with a suitably controller designed to be robust to the variations of the main parameters of the induction motor, like stator and rotor resistances, and the three-phase magnetizing inductance. The proposed controller has been tested both in numerical simulation and experimentally on a suitably designed test setup. Moreover, it has been compared with the classical feedback linearization based on linear controllers, highlighting the improvements in terms of dynamic performance, when parameter variations occur. Results confirm a significant increase of the robustness of the controller against parameter variations.