Linear Feedback 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.

Fuzzy fractional-order adaptive robust feedback linearization control optimized by the multi-objective artificial hummingbird algorithm for a nonlinear ball–wheel system

Fuzzy fractional-order adaptive robust feedback linearization control optimized by the multi-objective artificial hummingbird algorithm for a nonlinear ball–wheel system

Stabilizing unstable periodic orbit of unknown fractional-order systems via adaptive delayed feedback control

This article presents an adaptive nonlinear delayed feedback control scheme for stabilizing the unstable periodic orbit of unknown fractional-order chaotic systems. The proposed control framework uses the Lyapunov approach and sliding mode control technique to guarantee that the closed-loop system is asymptotically stable on a periodic trajectory sufficiently close to the unstable periodic orbit of the system. The proposed method has two significant advantages. First, it employs a direct adaptive control method, making it easy to implement this method on systems with unknown parameters. Second, the framework requires only the period of the unstable periodic orbit. The robustness of the closed-loop system against system uncertainties and external disturbances with unknown bounds is guaranteed. Simulations on fractional-order duffing and gyro systems are used to illustrate the effectiveness of the theoretical results. The simulation results demonstrate that our approach outperforms the previously developed linear feedback control method for stabilizing unstable periodic orbits in fractional-order chaotic systems, particularly in reducing steady-state error and achieving faster convergence of tracking error.

An Objective Holographic Feedback Linearization Based on a Sliding Mode Control for a Buck Converter with a Constant Power Load

As a typical load, the constant power load (CPL) has negative impedance characteristics. The stability of the buck converter system with a mixed load of CPL and resistive load is affected by the size of the CPL. When the resistive load is larger than the CPL, the buck converter with the output voltage as an output function is a non-minimum phase nonlinear system, because its linear approximation has a right-half-plane pole. The non-minimum phase characteristic limits the application of many control techniques, but the objective holographic feedback linearization control (OHFLC) method is a good control strategy that can bypass the non-minimum phase system and make the system stable. However, the traditional OHFLC method, in designing the controller, generally uses a linear optimal quadratic design method to obtain a linear feedback control law. It requires a state quantity component with a one-order relative degree to the system. But it is not easy to find such a suitable state quantity with a one-order relative degree to the system. In this paper, an improved OHFLC method is proposed for Buck converters with a mixed loads of CPL and resistive loads, using the sliding mode control (SMC) theory to design the controller, so that the output state quantity components with different relative degrees to the system can be used in the holographic feedback linearization method. Finally, the simulation and experimental results also demonstrate that this method has the same, or even better, dynamic response performance and robustness than the traditional OHFLC method.

Adaptive controller based on barrier Lyapunov function for a composite Cartesian-delta robotic device for precise time-varying position tracking

This study presents the design of an adaptive event-driven controller for solving the trajectory tracking problem of a composite robotic device made up of a three-dimensional Cartesian and a parallel Delta robot. The proposed composite device has a mathematical model satisfying a standard Lagrangian structure affected by modeling uncertainties and external perturbations. The adaptive gain of the controller is considered to enforce the convergence of the tracking error while the state bounds are satisfied. The barrier Lyapunov function addresses the preconceived state constraints for both robotic devices by designing a time-varying gain that guarantees the ultimate boundedness of the tracking error under the effect of external perturbations. The event-driven approach considers that the Cartesian robot is moving into a predefined invariant zone near to the origin. In contrast, the delta robot can complete the tracking problem once the end-effector is inside the given zone. The suggested controller was evaluated using a virtual representation of the composite robotic device showing better tracking performance (while the restrictions are satisfied) than the performances obtained with the traditional linear state feedback controllers. Analyzing the mean square error and its integral led to confirming the benefits of using the adaptive barrier control.

Nonlinear Feedback Linearized Controller Design for Islanded AC Microgrids

A new way has been proposed in this paper to design a nonlinear feedback linearized controller for a single-input-two-output control problem of a multi-input multi-output system. An islanded AC microgrid linearized by the feedback linearization control scheme. The output voltages and currents are the control objectives to improve the voltage regulation and the power-sharing accuracy among parallel-connected distributed generation systems, respectively. The stability of the proposed feedback linearized control scheme has been investigated using a Lyapunov function for the islanded AC microgrid. The performance of the designed nonlinear controller has been evaluated under different case studies for the islanded AC microgrid.

Criteria for Stability and Stabilization of Variable Fractional-Order Uncertain Neutral Systems With Time-Varying Delay: Delay-Dependent Analysis

This work focuses on the stability and stabilization of variable fractional-order neutral systems (VFONS) with time-varying delays and time-varying structured uncertainties. Via the FO Lyapunov function, a new set of delay-and order-dependent criterion is proposed for both nominal and uncertain VFONS. The resultant criteria are formulated by designing a linear matrix inequality-based static output feedback control strategy in a set of linear matrix inequalities. The numerical simulations verify the theoretical formulation.

The stabilization of uncertain dynamic systems involving the generalized Riemann-Liouville fractional derivative via linear state feedback control

The main objective of this paper is to investigate the stabilization problem of uncertain fractional-order dynamic systems (UFDSs) that involve the generalized Riemann-Liouville fractional derivative using a linear feedback controller. We establish several sufficient conditions for achieving Mittag-Leffler stabilization and asymptotic stabilization of UFDSs. The primary methodology employed in our study is Lyapunov's direct method (LDM), along with other inequality techniques, which have demonstrated significant efficacy in studying the stability theory of nonlinear dynamic systems. To demonstrate the effectiveness of the proposed approach, examples and corresponding simulations are provided.

Design of a Reference Governor in a Zonotopic Framework Applied to a Quadrotor under Feedback Linearization Control Strategies

Design of a Reference Governor in a Zonotopic Framework Applied to a Quadrotor under Feedback Linearization Control Strategies

Feedback Linearization and Robust Control for Whirl Mode With Operating Point Deviation in Active Magnetic Bearings-Rotor System

The whirl mode, seriously affecting the stability of active magnetic bearings (AMBs), is a hot issue in the AMBs-rotor system. This article stresses a whirl mode problem accompanied by the operating point deviation when the magnetically suspended control moment gyro outputs torque. A feedback linearization controller is proposed to decouple the whirl mode effects from the system state-space representation. A robust controller for uncertain systems is also used to solve the operating point deviation problem. First, the AMBs-rotor system's state-space representation containing the whirl mode terms is described. Next, a system uncertainty caused by the operating point deviation is discussed. Then, a feedback linearization controller combined with a robust controller for uncertain systems is designed. Finally, simulations and experiments for validity verification are presented, and comparative experiments with a PID controller and a robust controller are shown and compared in this article.

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.

Predictive Feedback Control for Inventory Systems Subject to Delays and Constraints

In this paper, we discuss the inventory regulation problem of a logistic system composed of a production unit and inventory unit. The production system is characterized by a constant lead time, and the storage unit presents fixed losses due to perishable products. In this study, the system is subject to finite production resources, limited storage capacities, and the customer demands are uncertain in a given range. We address a control strategy to the input time delay system that is subject to positive and saturation constraints and to bounded disturbances. We apply a linear prediction feedback control strategy, in order to stabilize the closed-loop system and to fulfill the system constraints, so that necessary and sufficient conditions for the existence of an admissible control law are formulated.

Polyhedral estimation of L1 and L∞ incremental gains of nonlinear systems

We provide novel dissipativity conditions for bounding the incremental L1 gain of systems. Moreover, we adapt existing results on the L∞ gain to the incremental setting and relate the incremental L1 and L∞ gain bounds through transposed systems. Building on work on optimization based approaches to constructing polyhedral Lyapunov functions, we make use of these conditions to obtain a Linear Programming based algorithm that can provide increasingly sharp bounds on the gains as a function of a given candidate polyhedral storage function or polyhedral set. The algorithm is also extended to allow for the design of constrained linear feedback controllers for performance, as measured by the bounds on the incremental gains. We apply the algorithm to a couple of numerical examples to illustrate the power, as well as some limitations, of this approach.

Coordinated control of rotor side converter and grid side converter on 15 kW DFIG using cascaded feedback linearization controllers

Coordinated control of rotor side converter and grid side converter on 15 kW DFIG using cascaded feedback linearization controllers

Decentralized robust control of building structures based on sliding mode theory

SummaryIn this paper, a decentralized control algorithm is proposed for actively controlling the response of the flexible tall building structures under earthquake excitations. In the proposed approach, tall building structure was divided into some substructures in the form of state equation. The interaction of the subsystems and external excitations is conducted as bounded generalized force acting on the subsystems. A decentralized control algorithm of tall building structures is established based on the sliding model control theory. The control structure is described based on unit vector control. The control law consists of two parts: a linear control law uL and a nonlinear law uN. The linear control is merely a linear state feedback controller, whereas the nonlinear feedback controller incorporates the discontinuous or continuous nonlinear elements of the control law. Using the advantage of match conditions of sliding mode theory and the bounded feature of generalized force, the overall stability of decentralized control is also investigated. The actuator arrangement and matching conditions are discussed. The effectiveness of the proposed method is demonstrated by the numerical simulation of the decentralized control of a 20‐story benchmark structure under seismic excitations.

Robust performance comparison of PMSM for flight control applications in more electric aircraft.

This paper describes a robust performance comparison of flight control actuation controllers based on a permanent magnet synchronous motor (PMSM) in more electric aircraft (MEA). Recently, the PMSM has become a favorite for the flight control applications of more electric aircraft (MEA) due to their improved efficiency, higher torque, less noise, and higher reliability as compared to their counterparts. Thus, advanced nonlinear control techniques offer even better performance for the control of PMSM as noticed in this research. In this paper, three nonlinear approaches i.e. Feedback Linearization Control (FBL) through the cancellation of the non-linearity of the system, the stabilization of the system via Backstepping Control (BSC) using the Lyapunov candidate function as well as the robust performance with chattering minimization by applying the continuous approximation based Sliding Mode Control (SMC) are compared with generalized Field-Oriented Controller (FOC). The comparison of FOC, FBL, BSC and SMC shows that the nonlinear controllers perform well under varying aerodynamic loads during flight. However, the performance of the sliding mode control is found superior as compared to the other three controllers in terms of better performance characteristics e.g. response time, steady-state error etc. as well as the control robustness in the presence of the uncertain parameters of the PMSM model and variable load torque acting as a disturbance. In essence, the peak of the tolerance band is less than 20% for all nonlinear and FOC controller, while it is less than 5% for SMC. Steady state error for the SMC is least (0.01%) as compared to other three controllers. Moreover, the SMC controller is able to withstand 50% parameter variation and loading torque of 10 N.m without significant changes in performance. Six simulation scenarios are used to analyze the performance and robustness which depict that the sliding mode controller performs well in terms of the desired performance for MEA application.

OPTIMIZATION OF THE DC MOTOR MODEL COMPLEXITY LEVEL SELECTION FOR THE SYSTEM USING A NEURAL NETWORK

A mathematical and computer model of the torque control system of a DC motor with independent excitation was built using the NARMA 2 neurocontroller. NARMA - a nonlinear moving average autoregressive model is one of the basic structures of a discrete and nonlinear model. The task was to conduct a comparative analysis of the operation of the neurocontroller-based control system using plant model of varying complexity. For the experiment, three models of the object were built with different degrees of detail, and therefore the reliability of the object. The full model includes an independently excited DC motor model that takes into account all the main parameters of the DC machine, internal friction parameters, excitation circuits, etc. The simplified model is a linear approximation of the DC motor by a second-order system that takes into account both mechanical and electrical time constants. The simplest model is a linear approximation of a DC motor by a first-order aperiodic link that takes into account only the mechanical time constant. The neurocontroller was trained using the above object models and an experiment was conducted to process the reference torque signal by the system. For each of the three training cases, the neurocontroller was set to 1000 epochs, since further training performance improvement is unnecessary due to the huge loss of computation time. The NARMA-L2 neurocontroller, which is also called feedback linearization control, was used. This controller can be implemented using a pre-identified NARMA-L2 object model. Neural networks that are trained on simplified object models do not take into account most of the electrical processes in a DC motor, especially the excitation winding is not modeled at all. The obtained results were compared with the use of a neurocontroller as a torque regulator. Further research in this direction involves the study of the necessary computing power for the microprocessor implementation of the neurocontroller.

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.

Design of optimal-robust control of step motors with partially unknown parameters and disturbances

The paper describes the design of a novel control law for step motor under participially unknown parameters and bounded external disturbances. The unknown parameters and disturbances are related to the motor torque, the rotor moment of inertia, the viscous friction, the resistance and the load torque. The design of a novel control law is based on feedback linearization and linear robust control. The gain of a linear control law is calculated by solving the linear matrix inequality and some optimization problems. The simulations illustrate the efficiency of the proposed scheme in comparison with classical PID-controller and adaptive control law.

Offshore wind power generation system control using robust economic MPC scheme

With the increasing penetration of wind power in the modern power system, the economic model predictive control (EMPC) has been widely employed in the control of the offshore wind power generation system for promoting the dynamic economy. A robust EMPC strategy, aiming to minimizing damage to the turbine while maximizing the electric power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A linear model is utilized to approximate the original nonlinear behavior of the wind power generation system, while the consequent linearization error is taken as the external disturbance of the control system. A linear feedback controller with a robust control invariant set is designed to restrict the deviation between the nominal linear system and the actual nonlinear wind power generation system. Plant simulations, as well as NREL FAST code testing, are given to demonstrate the proposed controller's effectiveness throughout the overall operating region.