Adaptive Feedback Linearization Control Research Articles

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

Projection operator‐based robust adaptive control of an aerial robot with a manipulator

AbstractThis paper describes a quadcopter manipulator system, an aerial robot with an extended workspace, its controller design, and experimental validation. The aerial robot is based on a quadcopter with a three degree of freedom robotic arm connected to the base of the vehicle. The work aims to create a stable airborne robot with a robotic arm that can work above and below the airframe, regardless of where the arm is attached. Integrating a robotic arm into an underactuated, unstable system like a quadcopter can enhance the vehicle's functionality while increasing instability. To execute a mission with accuracy and reliability during a real‐time task, the system must overcome the inter‐coupling effects and external disturbances. This work presents a novel design for a robust adaptive feedback linearization controller with a model reference adaptive controller and hardware implementation of the quadcopter manipulator system with plant uncertainties. The closed‐loop stability of the aerial robot and the tracking error convergence with the robust controller is analyzed using Lyapunov stability analysis. The quadcopter manipulator system is custom developed in the lab with an off‐the‐shelf quadcopter and a 3D‐printed robotic arm. The robotic system architecture is implemented using a Jetson Nano companion computer for autonomous onboard flight. Experiments were conducted on quadcopter manipulator system to evaluate the autonomous aerial robot's stability and trajectory tracking with the proposed controller.

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.

Function Approximation Technique (FAT)-Based Adaptive Feedback Linearization Control for Nonlinear Aeroelastic Wing Models Considering Different Actuation Scenarios

Function Approximation Technique (FAT)-Based Adaptive Feedback Linearization Control for Nonlinear Aeroelastic Wing Models Considering Different Actuation Scenarios

Control of Multiple-UAV Conveying Slung Load With Obstacle Avoidance

Quadrotor Unmanned Aerial Vehicles (UAVs) are generally underactuated systems and when load is attached for transportation purposes, the system complexity increases. Therefore, the need to appropriately control such systems becomes paramount as they usually navigate in cluttered environments. In this work, we conceptualize the problem of cooperative tracking control for a Multi-agent UAV load system (MUAVLs) whereby each UAV is divided into global position and local attitude subsystems. To ensure that formation is maintained in a desired path, Neural Network Graph-theoretic Distributed Adaptive Control (NNGDAC) is used for the position subsystem with a modified virtual force artificial potential field for obstacle avoidance. Another Adaptive Feedback Linearization (AFBL) controller is also designed for the attitude subsystem which is verified by simulation results.

Adaptive augmented torque control of a quadcopter with an aerial manipulator

A quadcopter manipulator system is an aerial robot consisting of a quadcopter with a robotic arm attached to it. The system has coupled nonlinear dynamics with uncertain time-varying parameters. The work in this paper focuses on designing an adaptive nonlinear controller to facilitate the uncertain system’s trajectory tracking and stability. The novelty of the proposed work is the design and implementation of an adaptive feedback linearization controller, called adaptive augmented torque (AAT) control, for the aerial robot. The control law is based on a feedback linearization controller with model reference adaptive controller and a tracking error-based augmented term. Using the input-to-state stability concept, a bound on the parameter estimation error is also developed. In the presented methodology, the controller uses estimated values of system parameters obtained from the adaptive mechanism and the tracking error to compute the control input using the AAT control law. An adaptive law for estimating unknown parameters is obtained using the strictly positive real-Lyapunov method. The asymptotic stability of the closed-loop system is analyzed via the Lyapunov theory. Simulations implemented in MATLAB and ROS/Gazebo and preliminary hardware experiments are presented to validate the theoretical results and to corroborate the performance of the AAT control law.

Adaptive Feedback Linearization Controller for Stabilization of Quadrotor UAV

Adaptive Feedback Linearization Controller for Stabilization of Quadrotor UAV

Nonlinear Robust Optimal Control via Adaptive Dynamic Programming of Permanent-Magnet Linear Synchronous Motor Drive for Uncertain Two-Axis Motion Control System

In this article, a nonlinear robust optimal control (NROC) scheme for uncertain two-axis motion control system via adaptive dynamic programming (ADP) and neural networks (NNs) is proposed. The two-axis motion control system is an X-Y table actuated by permanent-magnet linear synchronous motor servo drives. First, the motions of the tracking contour in X-axis and Y-axis of the X-Y table are stabilized through feedback linearization control (FLC) laws. However, the control performance may be destroyed due to parameter uncertainties and compounded disturbances. Therefore, to improve the robustness of the control system, an NROC is designed to achieve this purpose. The tracking control problem of the X-Y table with uncertainties is transformed to a regulation problem. Then, it is solved by an infinite horizon optimal control using a critic NN. Consequently, the NN is developed via ADP learning algorithm to facilitate the online solution of the Hamilton-Jacobi-Bellman equation corresponding to the nominal system for approximating the optimal control law. The uniform ultimate boundedness of the closed-loop system is proved utilizing the Lyapunov approach and the tracking error asymptotically converges to a residual set. The validation of the proposed control schemes are carried out through experimental analysis. The control algorithms have been implemented using a DSP control board. A comparison of control performances using FLC, adaptive FLC, and FLC-based NROC is investigated. From the experimental results, the dynamic behaviors of the two-axis control system using the proposed FLC-based NROC can achieve robust optimal control performance against parameter uncertainties and compounded disturbances.

A Position-Domain Adaptive Control Method for Underactuated Bipedal Walking on a Compliant Ground

Motivated by the potential use of humanoid-robot in real environment, a position-domain adaptive control strategy is developed to stabilize the underactuated bipedal walking on a compliant ground. First, the robot-ground system is modeled as a rigid kinematical chain coupled with a spring-damper system. Then by observing the simulation result of walking on compliant ground, we find the improvement of walking stability can be realized by controlling the initial velocity of robot’s center-of-mass (COM) of each walking cycle. In consideration of the highly-complicated impact of real road surface on direct velocity control, through the analysis on the relationship between the robot’s COM velocity and its foot vertical velocity, the robot’s state is parameterized by the normalized relative vertical position between both the feet, and the control of robot’s COM initial velocity of current cycle is realized by controlling the relative vertical velocity between the robot’s two feet during the previous impact phase. To realize it, an adaptive feedback linearization control strategy is developed in position-domain. Finally, the availability and adaptability of this method are validated through simulations: Specific to three initial gaits, on four compliant ground with different damping parameters, the underactuated bipedal walking is stabilized and the performance is improved.

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.

Adaptive and Nonlinear Control Techniques Applied to SEPIC Converter in DC-DC, PFC, CCM and DCM Modes Using HIL Simulation

In this paper, we propose adaptive nonlinear controllers for the Single-Ended Primary Inductance Converter (SEPIC). We also consider four distinct situations: AC-DC, DC-DC, Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). A comparative analysis between classic linear and nonlinear approaches to regulate the control loop is made. Three adaptive nonlinear control laws are designed: Feedback Linearization Control (FLC), Passivity-Based Control (PBC) and Interconnection and Damping Assignment Passivity-Based Control (IDAPBC). In order to compare the performance of these control techniques, numerical simulations were made in Software and Hardware in the Loop (HIL) for nominal conditions and operation disturbances. We recommend adaptive controllers for the two different situations: Adaptive Passivity-Based Feedback Linearization Control (APBFLC) for the PFC (Power Factor Correction) AC-DC system and IDAPBC-BB (IDAPBC Based on Boost converter) for the regulator DC-DC system.

A Distributed Control Paradigm for Smart Grid to Address Attacks on Data Integrity and Availability

In this paper, we propose an adaptive cyber-enabled parametric feedback linearization (PFL) control scheme for transient stability of smart grids. Based on feedback linearization control theory, the distributed PFL controller utilizes a distributed energy storage system to modify the dynamics of the power system during transients. We consider cyber attacks on data integrity and availability in the smart grid, and propose to adapt the PFL controller's parameter to the cyber state of the smart grid. Specifically, the PFL control scheme adapts its aggressiveness parameter to the level of noise, communication latency, and data injection attacks. Further, depending on the severity of the physical disturbance, the controller adjusts the value of its parameter to speed up the stabilization process. The performance of the proposed control scheme is validated on the IEEE 68-bus test power system, where the adaptive PFL controller is shown to efficiently stabilize the power system during physical and cyber disturbances.

Adaptive feedback linearization control for twin rotor multiple-input multiple-output system

This paper deals with the tracking problem of a twin rotor multiple-input multiple-output system (TRMS) described by an Euler-Lagrange forced model with constant uncertain parameters and input disturbances. The control of TRMS system is the benchmark problem for control of helicopter which has been a challenging problem due to flexible dynamics, nonlinearities and cross coupling. The control objective is to make this systems track to the given trajectories accurately in spite of the influences of uncertainties and input disturbances. We propose the adaptive feedback linearization controller by using 03 adaptive parameters that stand for the unknown mass parameters of parts in the TRMS. By choosing appropriately controller parameters, the effects of the input disturbances that are friction, cable, gyro moments and the effects caused by the main propeller speed and the tail propeller speed on the vertical and horizontal movements to the yaw and pitch angles will be attenuated. Two functions, including unknown mass parameter adaption and the disturbances attenuation to guarantee the errors of the pitch and yaw angles, are implemented simultaneously by the adaptive feedback linearization controller. The simulation and experimental results show that the good robustness of the TRMS controlled by the proposed controller can be obtained arbitrarily.

Torque ripple reduction of brushless DC motor based on adaptive input-output feedback linearization

Torque ripple reduction of Brushless DC Motors (BLDCs) is an interesting subject in variable speed AC drives. In this paper at first, a mathematical expression for torque ripple harmonics is obtained. Then for a non-ideal BLDC motor with known harmonic contents of back-EMF, calculation of desired reference current amplitudes, which are required to eliminate some selected harmonics of torque ripple, are reviewed. In order to inject the reference harmonic currents to the motor windings, an Adaptive Input-Output Feedback Linearization (AIOFBL) control is proposed, which generates the reference voltages for three phases voltage source inverter in stationary reference frame. Experimental results are presented to show the capability and validity of the proposed control method and are compared with the vector control in Multi-Reference Frame (MRF) and Pseudo-Vector Control (P-VC) method results.

Implementation of sensorless IPMSM drive using the stator current space vector orientation reference frame

Purpose In this paper an adaptive nonlinear control scheme has been proposed for direct torque and flux control of interior permanent magnet synchronous motor (IPMSM) without using mechanical sensor. Design/methodology/approach In this control method the stator resistance is online estimated by adaptive input-output state feedback linearization (AIOFL) controller. Based on proposed control scheme, the strategies of the maximum torque per ampere (MTPA) and maximum power factor (MPF) have been tested using a so-called stator flux search method. Findings The motor equations are transferred from the stationary reference frame to the (SX-SY) two-axis frame of the current by the stator current angle. In this frame, the Y component of the motor current is set to zero and the implementation of the nonlinear controller is independent of the two-axis inductances, the rotor magnetic flux, and position. Practical implications The effectiveness and capability of the proposed control method has been verified by simulation and experimental results. The proposed control scheme can be used for the sensorless control of IPMSM drive in industrial applications such as electric vehicle, traction system, and air conditioner. Originality/value The proposed controller is developed in a special axis (X-Y) rotating reference frame with the X-axis in coincide with the machine current space vector so that there is no need to use and know the machine inductances and rotor position.

ABOUT THE ROBUSTNESS OF ADAPTIVE FEEDBACK LINEARIZATION CONTROLLER FOR INPUT PERTURBED UNCERTAIN FULLY-ACTUATED SYSTEMS

The paper proposes a theorem to assert the arbitrarily good robustness of the fully actuated mechanical system controlled by the adaptive feedback linearization controller. The fully actuated system to be controlled is considerately perturbed by input disturbances and contains constant uncertain parameters in its Euler-Lagrange forced model. It is shown in this paper that independent of input disturbances the adaptive feedback linearization controller with appropriately chosen parameters will drive the output of controlled systems to the desired trajectory for any arbitrary precision. The adaptive controller is applied to the two-link planar elbow arm robot with unknown mass of the end-effector of second link and input torque noises caused by the viscous friction forces and Coulomb friction terms. Simulation results show that the arbitrary precision of the tracking errors always are guaranteed.

Relative Degrees and Adaptive Feedback Linearization Control of T–S Fuzzy Systems

This paper presents a new study on the relative degrees of single-input and single-output T–S fuzzy systems in general noncanonical forms, and proposes a feedback linearization-based control design method for such systems. The study extends the system relative degree concepts, commonly used for the control of nonlinear systems, to general T–S fuzzy systems, derives various relative degree conditions for general T–S fuzzy systems, and establishes the relative degree dependent normal forms. A feedback linearization-based control design framework is developed for general T–S fuzzy systems using its normal form, to achieve closed-loop stability and asymptotic output tracking under relaxed design conditions. A new adaptive feedback linearization-based control scheme for T–S fuzzy systems in general noncanonical forms with parameter uncertainties is designed and analyzed. Some extensions of relative degrees and their possible application to robust adaptive control for noncanonical form T–S fuzzy systems are also demonstrated. An illustrative example is presented with simulation results to demonstrate the control system design procedure and to show the effectiveness of the proposed control scheme.

2B35 Quaternion-based adaptive attitude control for a winged rocket(The 12th International Conference on Motion and Vibration Control)

Reusable Launch Vehicles (RLVs) are expected to become important transportation systems in future space exploration. RLVs are vehicles which have the ability to be launched into space, carry a payload to orbit and finally landing back on earth without using any expendable rockets. Due to this reason, RLVs are capable to reduce rocket launch operation expenses for space exploration. However, RLVs travel from earth's surface to space in uncertain environments causing the values of the parameters for the RLVs dynamic equations to be inconsistent. Therefore, various researchers have proposed several adaptive control methods to counter this problem. Quaternion is often being used in attitude control system of space vehicles such as rockets and satellites because quaternion has no singular point when the pitch angle approaches ±90[deg], which is a problem for Euler Angles. In this paper, we propose a digital adaptive feedback linearization control method using Quaternion for controlling the attitude of a winged rocket, a type of RLV. The proposed control method is implemented into the control system of a propulsion engine model. The effectiveness of the control system is demonstrated through computer simulations under the condition that the rocket's pitch angle during lift-off is in singularity of Euler angles. Simulation results show that the winged rocket is able to be launched from a vertically upward launch phase, flying into ascending phase and subsequently making a transition into a gliding phase successfully. The simulation results demonstrated the effectiveness of the proposed method in controlling a winged rocket.

NARMA-L2 Control of a Nonlinear Half-Car Servo-Hydraulic Vehicle Suspension System

In this paper, the performance of a nonlinear, 4 degrees-of-freedom, servo- hydraulic half-car active vehicle suspension system is compared with that of a passive vehicle suspension system with similar model parameters. The active vehicle suspension system is controlled by an indirect adaptive Neural Network-based Feedback Linearization controller (NARMA-L2). Hydraulic actuator force tracking is guaranteed by an inner Proportional+Integral+Derivative-based force feedback control loop. The output responses of the vehicles are presented and analyzed in the frequency and time domains, in the presence of model uncertainties in the form of variation in vehicle sprung mass loading. The results show that the NARMA-L2-based active vehicle suspension system performed better than the passive vehicle suspension system within the constraints.

Damping of Low Frequency Oscillations of Multi-Machine Multi-UPFC Power Systems, Based on Adaptive Input-Output Feedback Linearization Control

In this paper, damping of the low frequency oscillations of multi-machine multi-UPFC power systems is investigated based on adaptive input-output feedback linearization control (AIFLC) approach. Considering a three-phase symmetrical fault, ignoring the subtransient states of the synchronous machines, the nonlinear state equations of the system are derived in order to obtain the UPFC reference control signals as well as the system parameters estimation laws. The stability of the system controller is proved by Lyapunov theory. Moreover using the six reduced order model of synchronous machine, some simulation results are presented in order to verify the validity and effectiveness of the proposed control approach.