Showing papers on "Feedback linearization published in 2007"
TL;DR: In this article, a control-oriented model in closed form is obtained by replacing complex force and moment functions with curve-fitted approximations, neglecting certain weak couplings, and neglecting slower portions of the system dynamics.
Abstract: Full simulation models for flexible air-breathing hypersonic vehicles include intricate couplings between the engine and flight dynamics, along with complex interplay between flexible and rigid modes, resulting in intractable systems for nonlinear control design. In this paper, starting from a high-fidelity model, a control-oriented model in closed form is obtained by replacing complex force and moment functions with curve-fitted approximations, neglecting certain weak couplings, and neglecting slower portions of the system dynamics. The process itself allows an understanding of the system-theoretic properties of the model, and enables the applicability of model-based nonlinear control techniques. Although the focus of this paper is on the development of the control-oriented model, an example of control design based on approximate feedback linearization is provided. Simulation results demonstrate that this technique achieves excellent tracking performance, even in the presence of moderate parameter variations. The fidelity of the truth model is then increased by including additional flexible effects, which render the original control design ineffective. A more elaborate model with an additional actuator is then employed to enhance the control authority of the vehicle, required to compensate for the new flexible effects, and a new design is provided.
696 citations
TL;DR: The design of a feedback and feedforward controller to compensate for creep, hysteresis, and vibration effects in an experimental piezoactuator system is studied and significant reduction of both the maximum and root-mean-square tracking error is shown.
Abstract: In this brief, we study the design of a feedback and feedforward controller to compensate for creep, hysteresis, and vibration effects in an experimental piezoactuator system. First, we linearize the nonlinear dynamics of the piezoactuator by accounting for the hysteresis (as well as creep) using high-gain feedback control. Next, we model the linear vibrational dynamics and then invert the model to find a feedforward input to account vibration - this process is significantly easier than considering the complete nonlinear dynamics (which combines hysteresis and vibration effects). Afterwards, the feedforward input is augmented to the feedback-linearized system to achieve high-precision highspeed positioning. We apply the method to a piezoscanner used in an experimental atomic force microscope to demonstrate the method's effectiveness and we show significant reduction of both the maximum and root-mean-square tracking error. For example, high-gain feedback control compensates for hysteresis and creep effects, and in our case, it reduces the maximum error (compared to the uncompensated case) by over 90%. Then, at relatively high scan rates, the performance of the feedback controlled system can be improved by over 75% (i.e., reduction of maximum error) when the inversion-based feedforward input is integrated with the high-gain feedback controlled system.
355 citations
TL;DR: In this article, a generalized framework for global output feedback stabilization of a class of uncertain, inherently nonlinear systems of a particularly complex nature is introduced, since their linearization around the equilibrium is not guaranteed to be either controllable or observable.
Abstract: In this paper, we introduce a generalized framework for global output feedback stabilization of a class of uncertain, inherently nonlinear systems of a particularly complex nature since their linearization around the equilibrium is not guaranteed to be either controllable or observable. Based on a new observer/controller construction and a homogeneous domination design, this framework not only unifies the existing output feedback stabilization results, but also leads to more general results which have been never achieved before, establishing this methodology as a universal tool for the global output feedback stabilization of inherently nonlinear systems. Copyright © 2006 John Wiley & Sons, Ltd.
232 citations
TL;DR: In this paper, a two-dimensional path following control system for autonomous marine surface vessels is presented, which is obtained through a way-point guidance scheme based on line-of-sight projection algorithm and the speed controller is achieved through state feedback linearization.
192 citations
27 Jun 2007
TL;DR: In this paper, the authors presented new and efficient methods for numerical differentiation, i.e., for estimating derivatives of a noisy time signal, via convincing numerical simulations, by the analysis of an academic signal and by the feedback control of a nonlinear system.
Abstract: We are presenting new and efficient methods for numerical differentiation, i.e., for estimating derivatives of a noisy time signal. They are illustrated, via convincing numerical simulations, by the analysis of an academic signal and by the feedback control of a nonlinear system.
174 citations
Book•
20 Dec 2007
TL;DR: In this paper, the authors present a moment expression for nonlinear Stochastic Dynamic Systems (NSDS) with stochastic Parametric Excitation (SPE) and linearization of dynamic systems under external excitation.
Abstract: Mathematical Preliminaries.- Moment Equations for Linear Stochastic Dynamic Systems (LSDS).- Moment Equations for Nonlinear Stochastic Dynamic Systems (NSDS).- Statistical Linearization of Stochastic Dynamic Systems Under External Excitations.- Equivalent Linearization of Stochastic Dynamic Systems Under External Excitation.- Nonlinearization Methods.- Linearization of Dynamic Systems with Stochastic Parametric Excitations.- Applications of Linearization Methods in Vibration Analysis of Stochastic Mechanical Structures.- Accuracy of Linearization Methods.
170 citations
TL;DR: The Tail-Equivalent Linearization Method (TELM) as discussed by the authors is a non-parametric linearization method for nonlinear random vibration analysis, which employs a discrete representation of the stochastic excitation and concepts from the first-order reliability method, FORM.
153 citations
Journal Article•
TL;DR: In this article, a nonlinear anti-sway controller for container cranes with load hoisting is investigated, where the control inputs are two trolley and hoisting forces, whereas the variables to be controlled are three (trolley position, hoisting rope length, and sway angle).
Abstract: In this paper, a nonlinear anti-sway controller for container cranes with load hoisting is investigated. The considered container crane involves a planar motion in conjunction with a hoisting motion. The control inputs are two (trolley and hoisting forces), whereas the variables to be controlled are three (trolley position, hoisting rope length, and sway angle). A novel feedback linearization control law provides a simultaneous trolley-position regulation, sway suppression, and load hoisting control. The performance of the closed loop system is shown to be satisfactory in the presence of disturbances at the payload and rope length variations. The advantage of the proposed control law lies in the full incorporation of the nonlinear dynamics by partial feedback linearization. The uniform asymptotic stability of the closed-loop system is assured irrespective of variations of the rope length. Simulation and experimental results are compared and discussed.
152 citations
TL;DR: By combining the feedback linearization and DPD linearization techniques, the performance of the predistorter is enhanced significantly compared to the conventional DPD, and results clearly show that the new method is a good linearization algorithm, better than a conventional D PD.
Abstract: We have developed a new adaptive digital predistortion (DPD) linearization technique based on analog feedback predistortion (FBPD). The lookup-table-based feedback input can remove the bandwidth limitation of the feedback circuit related to the loop delay, and suppress feedback oscillation by accurate digital control of the feedback signal. Moreover, the predistortion (PD) signal can be extracted very efficiently. By combining the feedback linearization and DPD linearization techniques, the performance of the predistorter is enhanced significantly compared to the conventional DPD. To clearly visualize the characteristics of digital FBPD (DFBPD), we have compared it to the conventional DPD based on the recursive least square algorithm using Matlab simulation. The results clearly show that the new method is a good linearization algorithm, better than a conventional DPD. For the demonstration, a Doherty power amplifier with 180-W peak envelope power is linearized using the proposed DFBPD. For a 2.14-GHz forward-link wideband code-division multiple-access signal, the adjacent channel leakage ratio at 2.5-MHz offset is -58 dBc, which is improved by 15 dB at an average output power of 43 dBm
128 citations
TL;DR: In this article, the authors present the results of a research effort focused on the modeling, identification, control design, simulation, and flight-testing of YF-22 research aircraft models in closed-loop formation.
91 citations
TL;DR: An linear matrix inequality (LMI)-based approach for designing linear static output feedback impulsive control laws to globally asymptotically synchronize Lur'e chaotic systems is derived.
Abstract: In this brief, we consider impulsive control for master-slave synchronization schemes that consist of identical chaotic Lur'e systems. Impulsive control laws are investigated which make use of linear static measurement feedback, instead of full state feedback. A less conservative sufficient condition than existing results for global asymptotic impulsive synchronization is presented, in which synchronization is proven for the error between the full state vectors. And then an linear matrix inequality (LMI)-based approach for designing linear static output feedback impulsive control laws to globally asymptotically synchronize Lur'e chaotic systems is derived. With the help of the LMI solvers, we can easily obtain the linear output feedback impulsive controller and the bound of the impulsive interval for global asymptotic synchronization. The method is illustrated on Chua's circuit.
TL;DR: In this article, feedback linearization is used to design controllers for displacement, velocity and differential pressure control of a rotational hydraulic drive, which take into account the square root nonlinearity in the system's dynamics.
TL;DR: Experimental results are presented to illustrate positioning stability, system linearity, dynamic response invariance, nano stepping, multiaxis contouring, and large rotational motion.
Abstract: This paper presents the development of a compact six-axis magnetic levitation stage, including the design and implementation of magnetic actuators, laser interferometer motion sensors, and motion controllers. The designed travel volume of the stage is 2times2times2 mm in translation and 4degtimes4degtimes4deg in rotation. A two-axis linear actuator, based on the magnetic Lorentz force law, was designed, and three two-axis actuators, equivalent to six single -axis actuators, were implemented to achieve six-axis actuation. A high-resolution laser interferometer measurement system was implemented and employed to measure the six-axis motion of the stage, facilitating real-time feedback control. Feedback linearization, based on rigid-body dynamics of the levitated stage, and force distribution were implemented in a computer-controlled architecture so as to establish a decoupled dynamics between the six computed inputs and the resulting six-axis motions. Constant gain controllers were then designed and implemented, according to the concept of loop shaping, for each of the six axes, and high positioning stability, 1.1 nm root mean square (RMS) for x and 0.74 nm RMS for y, has been achieved. Experimental results are presented to illustrate positioning stability, system linearity, dynamic response invariance, nano stepping, multiaxis contouring, and large rotational motion
TL;DR: In this paper, a state-dependent Riccati equation is used for control of a helicopter with a six-degree-of-freedom nonlinear dynamic model, which is then solved numerically at each step of a 50 Hz control loop to design the nonlinear state feedback control law.
Abstract: DOI: 10.2514/1.21910 This paper presents a flight control approach based on a state-dependent Riccati equation and its application to autonomous helicopters. For our experiments, we used two different platforms: an XCell-90 small hobby helicopter and a larger vehicle based on the Yamaha R-Max. The control design uses a six-degree-of-freedom nonlinear dynamicmodelthatismanipulated into apseudolinearformwheresystemmatricesare givenexplicitly asafunction of the current state. A standard Riccati equation is then solved numerically at each step of a 50 Hz control loop to design the nonlinear state feedback control law online. In addition, the state-dependent Riccati equation control is augmented with a nonlinear compensator that addresses issues with the mismatch between the original nonlinear dynamics and its pseudolinear transformation.
TL;DR: A robust Petri-fuzzy-neural-network (PFNN) control strategy applied to a linear induction motor (LIM) drive for periodic motion based on the model-free control design to retain the decoupled control characteristic of the FLC system is investigated.
Abstract: This study focuses on the development of a robust Petri-fuzzy-neural-network (PFNN) control strategy applied to a linear induction motor (LIM) drive for periodic motion. Based on the concept of the nonlinear state feedback theory, a feedback linearization control (FLC) system is first adopted in order to decouple the thrust force and the flux amplitude of the LIM. However, particular system information is required in the FLC system so that the corresponding control performance is influenced seriously by system uncertainties. Hence, to increase the robustness of the LIM drive for high-performance applications, a robust PFNN control system is investigated based on the model-free control design to retain the decoupled control characteristic of the FLC system. The adaptive tuning algorithms for network parameters are derived in the sense of the Lyapunov stability theorem, such that the stability of the control system can be guaranteed under the occurrence of system uncertainties. The effectiveness of the proposed control scheme is verified by both numerical simulations and experimental results, and the salient merits are indicated in comparison with the FLC system
TL;DR: The proposed methodology is an approach included in the control areas of non-linear feedback linearization, model-based and uncertainties consideration, making use of a pioneering algorithm in underwater vehicles, based on the fusion of a sliding mode controller and an adaptive fuzzy system.
Abstract: This paper address the kinematic variables control problem for the low-speed manoeuvring of a low cost and underactuated underwater vehicle Control of underwater vehicles is not simple, mainly due to the non-linear and coupled character of system equations, the lack of a precise model of vehicle dynamics and parameters, as well as the appearance of internal and external perturbations The proposed methodology is an approach included in the control areas of non-linear feedback linearization, model-based and uncertainties consideration, making use of a pioneering algorithm in underwater vehicles It is based on the fusion of a sliding mode controller and an adaptive fuzzy system, including the advantages of both systems The main advantage of this methodology is that it relaxes the required knowledge of vehicle model, reducing the cost of its design The described controller is part of a modular and simple 2D guidance and control architecture The controller makes use of a semi-decoupled non-linear plant model of the Snorkel vehicle and it is compounded by three independent controllers, each one for the three controllable DOFs of the vehicle The experimental results demonstrate the good performance of the proposed controller, within the constraints of the sensorial system and the uncertainty of vehicle theoretical models
TL;DR: A Modified TJ (MTJ) algorithm is presented which employs stored data of the control command in the previous time step, as a learning tool to yield improved performance, and the noise rejection characteristics of the algorithm are improved.
TL;DR: A novel adaptive H"~ tracking controller design for a class of nonlinear systems with uncertain system and gain function, which are unstructured (or non-repeatable) and state-dependent unknown nonlinear functions.
TL;DR: In this article, a feedback linearization control is applied to control a chaotic pendulum system to track desired periodic orbits such as period-one, period-two, and period-four orbits.
Abstract: In present paper, a feedback linearization control is applied to control a chaotic pendulum system. Tracking the desired periodic orbits such as period-one, period-two, and period-four orbits is efficiently achieved. Due to the presence of saturation in real world control signals, the stability of controller is investigated in presence of saturation and sufficient stability conditions are obtained. At first feedback linearization control law is designed, then to avoid the singularity condition, a saturating constraint is applied to the control signal. The stability conditions are obtained analytically. These conditions must be investigated for each specific case numerically. Simulation results show the effectiveness and robustness of proposed controller. A major advantage of this method is its shorter chaotic transient time in compare to other methods such as OGY and Pyragas controllers.
10 Apr 2007
TL;DR: The dynamic model of a robot with antagonistic actuated joints is presented, and the problem of full linearization via static state feedback is analyzed, and a scheme for simultaneous stiffness-position control of the linearized system is presented.
Abstract: In this paper, the dynamic model of a robot with antagonistic actuated joints is presented, and the problem of full linearization via static state feedback is analyzed. The use of transmission elements with nonlinear relation between the displacement and the actuated force allows to control both the position and the stiffness of each joint. The main advantage of this actuation modality is that the achieved stiffness becomes a mechanical characteristic of the system and it is not the result of an immediate control action as in the classical impedance control scheme (Davison, 2003). Different examples of implementation of this kind of devices are known in literature, even if limited to one single joint (Kjita et al., 2003; Laumond and Kineocam, 2006; Mansard and Chaumette, 2004 and 2006) and the application of antagonistic actuated kinematic chains in the field of robotic hand design is under investigation (Stasse et al., 2006). After a brief review of the dependence of the properties of antagonistic actuation on the transmission elements characteristics, a scheme for simultaneous stiffness-position control of the linearized system is presented. Finally, simulation results of a two-link antagonistic actuated arm are reported and discussed.
TL;DR: This paper presents chaos synchronization between two different chaotic systems by using a nonlinear controller, in which the nonlinear functions of the system are used as a non linear feedback term.
Abstract: This paper presents chaos synchronization between two different chaotic systems by using a nonlinear controller, in which the nonlinear functions of the system are used as a nonlinear feedback term. The feedback controller is designed on the basis of stability theory, and the area of feedback gain is determined. The artificial simulation results show that this control method is commendably effective and feasible.
TL;DR: In this article, a leader-follower robot formation is formulated based on the relative motion states between the robots and the local motion of the follower robot, and a formation controller, consisting of a feedback linearization part and a sliding mode compensator, is designed to stabilize the overall system including the internal dynamics.
TL;DR: A nonlinear feedback linearization control scheme and an adaptive control strategy are designed to synchronization two neurons under external electrical stimulation via the nonlinear control of FitzHugh–Nagumo neural system.
Abstract: Synchronization of FitzHugh–Nagumo neural system under external electrical stimulation via the nonlinear control is investigated in this paper. Firstly, the different dynamical behavior of the nonlinear cable model based on the FitzHugh–Nagumo model responding to various external electrical stimulations is studied. Next, using the result of the analysis, a nonlinear feedback linearization control scheme and an adaptive control strategy are designed to synchronization two neurons. Computer simulations are provided to verify the efficiency of the designed synchronization schemes.
TL;DR: In this paper, an indirect adaptive control algorithm is proposed to stabilize the fixed points of discrete chaotic systems, which is assumed that the functionality of the chaotic dynamics is known but the system parameters are unknown.
Abstract: In this paper an indirect adaptive control algorithm is proposed to stabilize the fixed points of discrete chaotic systems. It is assumed that the functionality of the chaotic dynamics is known but the system parameters are unknown. This assumption is usually applicable to many chaotic systems, such as the Henon map, logistic and many other nonlinear maps. Using the recursive-least squares technique, the system parameters are identified and based on the feedback linearization method an adaptive controller is designed for stabilizing the fixed points, or unstable periodic orbits of the chaotic maps. The stability of the proposed scheme has been shown and the effectiveness of the control algorithm has been demonstrated through computer simulations.
TL;DR: It is shown that a well-known linearization technique initially proposed for quadratic assignment problems can be generalized to a broader class of quadratics 0–1 mixed-integer problems subject to assignment constraints, and the resulting linearized formulation is more compact and tighter than that obtained with a more usuallinearization technique.
Abstract: We show that a well-known linearization technique initially proposed for quadratic assignment problems can be generalized to a broader class of quadratic 0–1 mixed-integer problems subject to assignment constraints. The resulting linearized formulation is more compact and tighter than that obtained with a more usual linearization technique. We discuss the application of the compact linearization to three classes of problems in the literature, among which the graph partitioning problem.
09 Jul 2007
TL;DR: A nonlinear control technique based on the property of flatness is proposed to control the diesel engine air system to achieve tracking of suitable references for the air-fuel ratio and the fraction of the recirculated exhaust gas.
Abstract: In this paper, a nonlinear control technique based on the property of flatness is proposed to control the diesel engine air system. More precisely, to achieve tracking of suitable references (corresponding to low emissions) for the air-fuel ratio and the fraction of the recirculated exhaust gas. The simulated diesel engine is a six cylinders medium duty Caterpillar 3126 B equipped with a variable geometry turbocharger and an exhaust gas recirculation valve. The proposed controller is designed on the reduced third order mean value model and implemented by endogenous linearizing dynamic feedback on the full order model. The controller is assessed through simulations with a SIL architecture using dSpace simulator. It exhibits good control performance without zero dynamics and ensures global stability and tracking of flat output references.
27 Jun 2007
TL;DR: In this article, a subclass of NN-ANARX structure is proposed to simplify the calculation of controls by ANARX-based control technique and the problem of the inverse function calculation in the control algorithm is solved.
Abstract: An application of Neural Networks based Additive Nonlinear Autoregressive Exogenous (NN-ANARX) structure for modeling and control of nonlinear MIMO systems is presented in the paper. A subclass of NN-ANARX structure is proposed to simplify the calculation of controls by ANARX-based control technique. The problem of the inverse function calculation in the control algorithm is solved. After that the ANARX-based dynamic output feedback linearization control algorithm is applied for control of nonlinear MIMO systems. The effectiveness of the approach proposed in the paper is demonstrated on examples.
TL;DR: The method combines ingredients from process networks, thermodynamics and systems theory to derive robust decentralized controllers that will ensure complete plant stability and is illustrated on a non-isothermal chemical reaction network.
26 Dec 2007
TL;DR: This paper proposes a behavior-based decentralized approach for UAV formation flight that is considered to promptly react on various situations because its control input is decided based on the relative weight of each agent's desired behavior.
Abstract: This paper proposes a behavior-based decentralized approach for UAV formation flight. The objective of UAV formation flight is that UAVs have to fly to a specified region while maintaining the distances between UAVs. To design the controller, the coupled dynamics of multiple UAVs is considered with assumption that each UAV can share the state-information with one another. A feedback linearization rule with a diffeomorphic transfer map is derived for three degree-of-freedom point mass model. The behavior-based approach is considered to promptly react on various situations because its control input is decided based on the relative weight of each agent's desired behavior. The optimization technique is used to provide the better performance of formation flying. To verify the performance of the proposed controller, numerical simulation is performed for a waypoint-passing mission of multiple UAVs.
TL;DR: A MIMO decoupling feedback linearization algorithm and an adaptive strategy are introduced respectively to design the synchronization controllers for the two coupled neurons.
Abstract: In this paper, synchronization dynamics of two FitzHugh–Nagumo neurons electrically coupled with gap junction in external electrical stimulation is studied. In order to synchronize all state variables in the neuron system, we regard the nonlinear feedback system as a multi-input multi-output (MIMO) system. Then, a MIMO decoupling feedback linearization algorithm and an adaptive strategy are introduced respectively to design the synchronization controllers for the two coupled neurons. Numerical simulations demonstrate the effectiveness of the developed methods.