Input-output Feedback Linearization Approach Research Articles

Robust Output Feedback Stabilization and Tracking for an Uncertain Nonholonomic Systems with Application to a Mobile Robot.

This paper presents a novel robust output feedback control that simultaneously performs both stabilization and trajectory tracking for a class of underactuated nonholonomic systems despite model uncertainties, external disturbance, and the absence of velocity measurement. To solve this challenging problem, a generalized normal form has been successfully created by employing an input-output feedback linearization approach and a change in coordinates (diffeomorphism). This research mainly focuses on the stabilization problem of nonholonomic systems that can be transformed to a normal form and pose several challenges, including (i) a nontriangular normal form, (ii) the internal dynamics of the system are non-affine in control, and (iii) the zero dynamics of the system are not in minimum phase. The proposed scheme utilizes combined backstepping and sliding mode control (SMC) techniques. Furthermore, the full-order high gain observer (HGO) has been developed to estimate the derivative of output functions and internal dynamics. Then, full-order HGO and the backstepping SMC have been integrated to synthesize a robust output feedback controller. A differential-drive type (2,0) the wheeled mobile robot has been considered as an example to support the theoretical results. The simulation results demonstrate that the backstepping SMC exhibits robustness against bounded uncertainties.

End-effector trajectory tracking of flexible link parallel robots using servo constraints

We apply the concept of servo constraints to end-effector trajectory tracking control of parallel robots with structural link flexibilities. Such servo constraints deliver the inverse robot model where solution approaches via projections are proposed, which transform the resulting differential-algebraic equations to ordinary differential equations. The applicable solution process depends on the existence and stability of the internal dynamics. When using the exact end-effector of flexible link robots as output, this internal dynamics is usually unstable. Then a two-point boundary value problem is considered in the framework of stable inversion to obtain the noncausal solution offline. This solution is used as a feedforward control, which is initially combined only with actuator feedback control. To also account for errors within the link flexibility, the well-known linear–quadratic regulator is adapted to end-effector trajectory tracking based on differential-algebraic equations. Finally, we propose a systematic input–output feedback linearization approach, which uses servo constraints for flexible link parallel robots. Here a minimum phase system is obtained by tracking a redefined end-effector output, which is an approximation of the exact end-effector position. All control concepts are validated experimentally with a parallel robot having a highly flexible link. The results allow us to compare different control approaches and show the superior performance of controllers that rely on a flexible multibody model in contrast to classical rigid multibody modeling.

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.

Robust LQR-Based Neural-Fuzzy Tracking Control for a Lower Limb Exoskeleton System with Parametric Uncertainties and External Disturbances.

The design of an accurate control scheme for a lower limb exoskeleton system has few challenges due to the uncertain dynamics and the unintended subject's reflexes during gait rehabilitation. In this work, a robust linear quadratic regulator- (LQR-) based neural-fuzzy (NF) control scheme is proposed to address the effect of payload uncertainties and external disturbances during passive-assist gait training. Initially, the Euler-Lagrange principle-based nonlinear dynamic relations are established for the coupled system. The input-output feedback linearization approach is used to transform the nonlinear relations into a linearized state-space form. The architecture of the adaptive neuro-fuzzy inference system (ANFIS) and used membership function are briefly explained. While varying mass parameters up to 20%, three robust neural-fuzzy datasets are formulated offline with the joint error vector and LQR control input. Thereafter, to deal with external interferences, an error dynamics with a disturbance estimator is presented using an online adaptation of the firing strength matrix. The Lyapunov theory is carried out to ensure the asymptotic stability of the coupled human-exoskeleton system in view of the proposed controller. The gait tracking results for the proposed control scheme (RLQR-NF) are presented and compared with the exponential reaching law-based sliding mode (ERL-SM) controller. Furthermore, to investigate the robustness of the proposed control over LQR control, a comparative performance analysis is presented for two cases of parametric uncertainties and external disturbances. The first case considers the 20% raise in mass values with a trigonometric form of disturbances, and the second case includes the effect of the 30% increment in mass values with a random form of disturbances. The simulation runs have shown the promising gait tracking aspects of the designed controller for passive-assist gait training.

An Adaptive Feedback Linearization Approach to Inertial Frequency Response of Wind Turbines

Synthetic inertial frequency response has attracted increasing attention due to the paradigm shift in power generation from synchronous generators toward wind turbines. This paper presents a novel nonlinear controller to enable inertial response of a variable speed wind turbine (VSWT). The VSWT has a nonlinear model which cannot efficiently be dealt with by linear control techniques as it may experience large deviations from its operating point during the frequency support period. To design the controller, a nonlinear model for the wind turbine together with power system is utilized. The output power of the wind turbine is expressed in terms of the state variables, and its exact Taylor series expansion is used in control design procedure. A regular controller is first designed based on the input–output feedback linearization approach. Then, the regular controller is replaced by an adaptive one to accommodate parameter variations of VSWT. The proposed controller is then embedded in the detailed model of the VSWT in MATLAB/Simulink environment. Extensive investigations, including the effects of different controller parameters, are carried out. Impacts of the most important practical operational constraints in the VSWT inertia response are also analyzed. Results verify the superiority and appropriateness of the proposed controller.

A Switching Control Strategy for Energy Efficient Walking on Uneven Surfaces

This paper studies the problem of achieving an energy efficient walking on an uneven surface for a compass-like biped robot with flat feet. To this end, reference walking gaits, which consist of two phases including the static foot phase and the foot rotation phase, are designed on several typical slopes via minimizing the dimensionless specific mechanical cost of transport. Moreover, for other slopes, a simple transformation method is proposed to generate interim reference walking gaits from the reference ones on those typical slopes. Then an input–output feedback linearization approach is considered to design a tracking control law in the static foot phase and a time-scaling approach is adopted to construct a tracking control law in the foot rotation phase as the dynamics in this phase is underactuated. By switching the control law at different phases and switching the reference walking gaits on different slopes, a compass-like robot can achieve energy efficient walking on uneven surfaces. The validity of our method is illustrated by numerical simulations.

Electronic Throttle Control System: Modeling, Identification and Model-Based Control Designs

Electronic throttle control (ETC) system has worked its way to becoming a standard subsystem in most of the current automobiles as it has contributed much to the improvement of fuel economy, emissions, drivability and safety. Precision control of the subsystem, which consists of a dc motor driving a throttle plate, a pre-loaded return spring and a set of gear train to regulate airflow into the engine, seems rather straightforward and yet complex. The difficulties lie in the unknown system parameters, hard nonlinearity of the pre-loaded spring that pulls the throttle plate to its default position, and friction, among others. In this paper, we extend our previous results obtained for the modeling, unknown system parameters identification and control of a commercially available Bosch’s DV-E5 ETC system. Details of modeling and parameters identification based on laboratory experiments, data analysis, and knowledge of the system are provided. The parameters identification results were verified and validated by a real-time PID control implemented with an xPC Target. A nonlinear control design was then proposed utilizing the input-output feedback linearization approach and technique. In view of a recent massive auto recalls due to the controversial uncontrollable engine accelerations, the results of this paper may inspire further research interest on the drive-by-wire technology.

Visual Servoing Tracking Control of a Ball and Plate System: Design, Implementation and Experimental Validation

This paper presents the design, implementation and validation of real-time visual servoing tracking control for a ball and plate system. The position of the ball is measured with a machine vision system. The image processing algorithms of the machine vision system are pipelined and implemented on a field programmable gate array (FPGA) device to meet real-time constraints. A detailed dynamic model of the system is derived for the simulation study. By neglecting the high-order coupling terms, the ball and plate system model is simplified into two decoupled ball and beam systems, and an approximate input-output feedback linearization approach is then used to design the controller for trajectory tracking. The designed control law is implemented on a digital signal processor (DSP). The validity of the performance of the developed control system is investigated through simulation and experimental studies. Experimental results show that the designed system functions well with reasonable agreement with simulations.

Set point stabilization of a 2DOF underactuated manipulator

Controlling an underactuated manipulator with less actuators than degrees of freedom is a challenging problem, specifically when it is to force the underactuated manipulator to track a given trajectory or to be configurated at a specific position in the work space. This paper presents two controllers for the set point regulation of 2-DOF underactuated manipulators. The first one is a cascade sliding mode tracking controller while the second one uses an inputoutput feedback linearization approach. The first algorithm builds on an observation that an underactuated manipulator can be treated as two subsystems. Consequently, a cascade sliding mode tracking controller has been designed. Firstly, a sliding mode surface is designed for both subsystems, these two sliding surfaces represent a first layer in the design architecture. A second layer sliding mode surface is then constructed based on the first layer sliding surface. The cascaded sliding mode controller is therefore deduced in terms of Lyapunov stability theorem. Robustness issues to bounded disturbances are then investigated. In a second stage of the paper, the input output feedback linearization (IOFL) control is presented. The latter, is then mixed to the sliding mode control scheme for robustness issues. Simulation results on 2-DOF whirling pendulum are presented to demonstrate the effectiveness of the proposed approach.

A nonlinear adaptive backstepping approach applied to a three phase PWM AC–DC converter feeding induction heating

This paper presents a new control strategy for a three phase PWM converter, which consists of applying an adaptive nonlinear control. The input–output feedback linearization approach is based on the exact cancellation of the nonlinearity, for this reason, this technique is not efficient, because system parameters can vary. First a nonlinear system modelling is derived with state variables of the input current and the output voltage by using power balance of the input and output, the nonlinear adaptive backstepping control can compensate the nonlinearities in the nominal system and the uncertainties. Simulation results are obtained using Matlab/Simulink. These results show how the adaptive backstepping law updates the system parameters and provide an efficient control design both for tracking and regulation in order to improve the power factor.