Input-output Linearization Control Research Articles

Nonlinear control algorithm for a self-excited induction generator for wind power applications

A non-linear state-feedback input-output linearizing control with tracking controllers for an isolated Induction Generator (IG) driven by a wind turbine and supplying an AC load is presented. For this purpose, an appropriate mathematical model of the IG based on the so-called stator-flux field oriented is detailed. Simulation results show that the input-output linearizing control provides an excellent control performances and a perfect tracking for both IG stator voltage magnitude and DC bus voltage. The proposed system can be directly explored for wind power generation purposes. Simulation results are curried out to demonstrate the feasibility of the proposed approach.

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.

The performance of the input-output linearization controller fo a doubly-fed induction machine

The performance of the input-output linearization controller fo a doubly-fed induction machine

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.

Fault Tolerant Control via Input-Output Linearization Method for LED-Driver Using a Boost Converter

This paper proposes a method based on an input-output linearization controller with a nonlinear adaptive observer, in order to achieve both an effective light-emitting diode (LED) current tracking and an actuator fault tolerant controller strategy for a LED-driver, using a boost converter. The partial fault is presented as a Loss of Effectiveness (LoE) in the embedded control target by considering that it generates a faulty Pulse-Width Modulation (PWM) signal. Also, faults of energy storage components in the power system are considered as actuator partial faults. An internal stability analysis is presented to ensure the feasibility of the nonlinear controller design. The nominal feedback controller is able to compensate for the nonlinearity of the system exactly, thus yielding a linear control loop. Furthermore, a nonlinear adaptive observer is considered for fault estimation. When the actuator fault is detected and estimated correctly, fault accommodation and reconfiguration strategies are performed to reduce the fault’s effect. The controller and observer gains are tuned using genetic algorithm techniques to have a desired closed-loop and fault estimation error response. Finally, simulations results are done in order to illustrate the effectiveness of the proposed methodology.

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.

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.

More Efficient Wind Energy Conversion System Using Shunt Active Power Filter

Wind energy takes first place in many countries among other renewable energy. However, the power quality problems such as nonlinear and unbalanced loads, unbalanced grid, can increase the mechanical vibrations as well as the risk of overheating in Wind Energy Conversion System (WECS). Therefore, the objective of this work is the use of robust flying capacitor converter in order to enhance the WECS efficiency by minimizing mechanical vibrations and reducing thermal stress with minimum voltage stress of power switches. To this end, this paper proposes a WECS based on doubly fed induction generator (DFIG) with the stator connected directly to non-ideal power grid, grid side converter (GSC) with flying capacitor topology and robust input output linearization control used to regulate the dc side capacitor voltage and also compensate the current harmonics in power grid in order to avoid excessive heating and mechanical vibrations in WECS. Simulation using MATLAB demonstrate that the proposed approach improves the power quality by reducing the total harmonic distortion (THD) of grid current to a value which satisfies the limits of IEEE standard, and allows the 8 KW DFIG to operate with reduced mechanical vibration and minimum heating stress in addition to the generation of power.

Nonlinear state and parameter estimation using derivative information: A Lie-Sobolev approach

The implementation of nonlinear control depends on the accuracy of the system model, which, however, is often restricted by parametric and structural uncertainty in the underlying dynamics. In this paper, we propose methods of estimating parameters and states that aim at matching the identified model and the true dynamics not only in the direct output measurements, i.e., in an L2-sense, but also in the higher-order time derivatives of the output signals, i.e., in a Sobolev sense. A Lie-Sobolev gradient descent-based observer-estimator and a Lie-Sobolev moving horizon estimator (MHE) are formulated, and their convergence properties and effects on input–output linearizing control and model predictive control (MPC) respectively are studied. Advantages of Lie-Sobolev state and parameter estimation in nonlinear processes are demonstrated by numerical examples and a reactor with complex dynamics.

A Robust Payload Control System Design for Offshore Cranes: Experimental Study

This paper presents a robust controller design of payload position control for an offshore crane facing disturbance and parametric uncertainties. The offshore operations with cranes while lifting and lowering a payload can be dangerous since safety and efficiency are affected by waves, wind and ocean currents. Such harsh sea conditions put the offshore crane and payload through unwanted disturbances and parametric uncertainties, which requires a robust control system to guarantee reliable performance of these systems. In this paper, we detail a controller designed based on uniformly ultimately bounded (UUB) theory, combined with the input-output linearization control technique (IOLC). The stability of the closed-loop system under the UUB conditions is analyzed using the energy-based Lyapunov function. To evaluate the control performance of the proposed controller, along with an IOLC and an integral sliding mode controller (ISMC), a comparison study is also conducted. The control performance and efficiency of the proposed controller are validated through experiments on an offshore crane model.

Stable Shortest Horizon FCS-MPC Output Voltage Control in Non-Minimum Phase Boost-Type Converters Based on Input-State Linearization

Finite Control Set Model Predictive Control (FCS-MPC) is an alternative to conventional linear controllers in power converters due to its attractive properties such as its conceptual simplicity, flexibility, direct consideration of non linearities as well as constraints, and fast control response. This last feature imposes computational constraints and short prediction horizons are therefore needed. In fact, in many applications an horizon one FCS-MPC controller is used. However, using FCS-MPC with a short prediction horizon in topologies that exhibit non-minimum phase behavior such as active-front-end rectifiers, dc-dc boost converters, and Z-source converters, among others, may lead to control issues. This work studies the origin of these control issues using nonlinear control theory considering a dc-dc boost converter as a case study. It is shown that the difficulty to directly control the converter output voltage using short horizon FCS-MPC is the resulting unstable internal dynamic, as the short horizon FCS-MPC controller acts like an input-output linearizing controller. To solve the problem of the unstable internal dynamic, a short horizon FCS-MPC controller based on input-state linearization is proposed allowing correct operation of the system. Experimental results show the feasibility of the proposal.

Extended Capture Point and Optimization-based Control for Quadrupedal Robot Walking on Dynamic Rigid Surfaces

Stabilizing legged robot locomotion on a dynamic rigid surface (DRS) (i.e., rigid surface that moves in the inertial frame) is a complex planning and control problem. The complexity arises due to the hybrid nonlinear walking dynamics subject to explicitly time-varying holonomic constraints caused by the surface movement. The first main contribution of this study is the extension of the capture point from walking on a static surface to locomotion on a DRS as well as the use of the resulting capture point for online motion planning. The second main contribution is a quadratic-programming (QP) based feedback controller design that explicitly considers the DRS movement. The stability and robustness of the proposed control approach are validated through simulations of a quadrupedal robot walking on a DRS with a rocking motion. The simulation results also demonstrate the improved walking performance compared with our previous approach based on offline planning and input-output linearizing control that does not explicitly guarantee the feasibility of ground contact constraints.

Field Weakening Operation Control Strategies of PMSM Based on Feedback Linearization

Based on current research into the mathematical model of the permanent magnet synchronous motor (PMSM) and the feedback linearization theory, a control strategy established upon feedback linearization is proposed. The Lie differential operation is performed on the output variable to obtain the state feedback of the nonlinear system, and the dynamic characteristics of the original system are transformed into linear dynamic characteristics. A current controller based on the input–output feedback linearization algorithm is designed to realize the input–output linearization control of the PMSM. The current controller decouples the d–q axis current from the flux linkage information of the motor and outputs a control voltage. When the motor speed reaches above the base speed, the field-forward and straight-axis current components are newly distributed to achieve field weakening control, which can realize the smooth transition between the constant torque region and weak magnetic region. Simulation and experimental results show the feasibility and viability of the strategy.

Beyond Snap-In: A Hybrid Nonlinear Control Approach for Actuators Possessing a Square-Law Characteristic

The undesired square-law characteristics in micro-electrostatic actuators and magnetic solenoids results in a limited stable range, which reduces their application fields and performance. This research investigated the isomorphic dynamics in these actuators and observed that the nonlinear drive force and the uncertain time delay are the challenges for the full range position controller design. A hybrid nonlinear control scheme includes an input–output linearization controller and a feedback posicast compensator for targeting these problems with abundant stability margin. The experimental results show that the stable range has been extended from 33% to 80%. Furthermore, it is the first literature report of this type of actuator that can track sinusoidal motion beyond the conventional stable range with an amplitude of 70% of the total range. This contribution can significantly enhance the performance of micro-sensors or expand the usage of electrostatic/magnetic actuators in motion-control systems.

Model based extremum-seeking controller via modelling-error compensation approach

This paper is focused on the design of a robust model based extremum-seeking controller (ESC) aimed at the online optimization of a class of uncertain nonlinear systems. The ESC scheme is based on the modeling-error compensation approach using a robust input–output linearizing control law coupled with a first-order gradient estimator. The feedback control scheme is able to drive the closed-loop system into a vicinity of the optimal operating set-point. In contrast with perturbation-based ESC algorithms, the control scheme includes an observer-based uncertain estimator for computing unknown components related to model uncertainties and a continuous gradient estimator, while avoiding the use of a perturbation signal for achieving closed-loop convergence to the optimal neighborhood. The convergence of the closed-loop system to the unknown optimal set-point is analyzed. Numerical simulations illustrate the effectiveness of the proposed ESC scheme in two different nonlinear systems.

Modeling and Diagnosis of the Inter-Turn Short Circuit Fault for the Sensorless Input-Output Linearization Control of the PMSM

The purpose of this paper is the inter-turn short circuit fault Modeling and detection for the sensorless input-output linearization control of the permanent magnet synchronous motor (PMSM) based on the Extended Kalman Filter observer (EKF). The fault detection procedures are based through the estimation of the stator resistance variation by the Extended Kalman Filter observer and the Fast Fourier Transformer (FFT) for the stationary state, and the Discrete Wavelet Transform (DWT) analysis of the electrical characteristics of the PMSM, for the non-stationary state. However, the FFT spectral analysis and the DWT is a useful solution to ensure that the variation of the stator resistance estimation is caused by the inter-turn short circuit fault. The effectiveness of the sensorless control and the fault detection techniques are presented in a simulation in MATLAB/Simulink environment.

Antiwindup Input–Output Linearization Strategy for the Control of a Multistage Continuous Fermenter With Input Constraints

This paper deals with the control of a multistage continuous fermenter (MSCF) used for the study of wine fermentation. The control design is facing three main difficulties: 1) system nonlinearity; 2) lack of online measurement of the controlled variable (sugar concentration); and 3) positive constraints on the control inputs (inlet flow rates of each tank) coming from the cascade structure of the system. A control strategy has been proposed, which accounts for these specificities. It is based on an input-output linearization control law coupled with an antiwindup technique and a state observer (the Kalman filter). The strategy has been tested and validated first on numerical simulations and then applied to the real process. The experiments gave satisfactory results that open up new perspectives on the use of the MSCF.

Comparison and implementation of different control strategies for improving production of rHSA using Pichia pastoris

Recombinant protein production by various host organisms is strictly regulated, which is determined mainly by host cell genetics and maintenance of process variables within desirable limits in a reactor. Since the process dynamics typically exhibit nonlinear interactions amongst the critical process variables, the commonly employed controllers are suboptimal for control of most bioprocesses. In this article, we present the implementation of an augmented controller for improving the production of recombinant human serum albumin (rHSA) using Pichia pastoris. The augmented controller consists of a decoupled adaptive control (DAC) and a decoupled input-output linearizing control (DIOLC) in a combined architecture that permits simultaneous control of substrate concentration and dissolved oxygen concentration, sampled at different rates during fed-batch fermentation. The performance of DAC-DIOLC controller was compared to the performance of DAC and DIOLC implemented individually on the process. Our results show that the augmented controller better regulates the substrate and dissolved oxygen (DO), along the desired control trajectory compared to the individual DAC and DIOLC controllers. The performance of the augmented DAC-DIOLC controller showed deviations of only 0.021 g/L from substrate set point compared to 0.09 g/L in PID and 0.031 g/L in DIOLC, and a 1.12% deviation from DO set point compared to 11.86% in PID and 2.58% in DAC. Overall, stricter control achieved using the augmented controller resulted in a 1.5 fold increase in rHSA production.

Adaptive Type-2 Fuzzy Logic Control Applied to the Inverted Pendulum on a Cart Problem

This paper introduces the design of an adaptive interval type-2 fuzzy logic controller and its implementation on a pendulum-cart system. It includes the theoretical foundations of: type-2 fuzzy logic system, input-output linearization control, fuzzy systems for functions approximation and adaptive systems. Type-2 fuzzy controllers provide an approach for using wide variety of opinions in control design. The above allows minimizing the effect of uncertainties in the rule base of fuzzy logic systems. These types of controllers provide greater flexibility in the application of human knowledge during the design of control systems in contrast to traditional controllers employing type-1 fuzzy logic. We propose to compare an adaptive interval type-2 fuzzy logic controller with its corresponding adaptive type-1 fuzzy logic controller. These controllers are tested in real-time on an inverted pendulum on a cart experimental prototype under the presence of parametric uncertainties in the mass of the pendulum. Five performance indexes are computed to compare both controllers, showing that type-2 fuzzy systems can help to improve the response of type-1 fuzzy systems. Copyright © 2018 Praise Worthy Prize - All rights reserved.