2,587 research outputs found

    Robust output stabilization: improving performance via supervisory control

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    We analyze robust stability, in an input-output sense, of switched stable systems. The primary goal (and contribution) of this paper is to design switching strategies to guarantee that input-output stable systems remain so under switching. We propose two types of {\em supervisors}: dwell-time and hysteresis based. While our results are stated as tools of analysis they serve a clear purpose in design: to improve performance. In that respect, we illustrate the utility of our findings by concisely addressing a problem of observer design for Lur'e-type systems; in particular, we design a hybrid observer that ensures ``fast'' convergence with ``low'' overshoots. As a second application of our main results we use hybrid control in the context of synchronization of chaotic oscillators with the goal of reducing control effort; an originality of the hybrid control in this context with respect to other contributions in the area is that it exploits the structure and chaotic behavior (boundedness of solutions) of Lorenz oscillators.Comment: Short version submitted to IEEE TA

    Some tracking problems for aerospace models with input constraints

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    We study tracking controller design problems for key models of planar vertical takeoff and landing (PVTOL) aircraft and unmanned air vehicles (UAVs). The novelty of our PVTOL work is the global boundedness of our controllers in the decoupled coordinates, the positive uniform lower bound on the thrust controller, the applicability of our work to cases where the velocity measurements may not be available, the uniform global asymptotic stability and uniform local exponential stability of our closed loop tracking dynamics, the generality of our class of trackable reference trajectories, and the input-to-state stability of the controller performance under actuator errors of arbitrarily large amplitude. The significance of our UAV results is the generality of the trackable trajectories, the input-to-state stability properties of the tracking dynamics with respect to additive uncertainty on the controllers, and our ability to satisfy command amplitude and command rate constraints as well as state dependent command constraints and a state constraint on the velocity. Our work is based on a Matrosov approach for converting a nonstrict Lyapunov function for the UAV tracking dynamics into a strict one, in conjunction with asymptotic strict Lyapunov function methods and bounded backstepping

    Multi-Parametric Extremum Seeking-based Auto-Tuning for Robust Input-Output Linearization Control

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    We study in this paper the problem of iterative feedback gains tuning for a class of nonlinear systems. We consider Input-Output linearizable nonlinear systems with additive uncertainties. We first design a nominal Input-Output linearization-based controller that ensures global uniform boundedness of the output tracking error dynamics. Then, we complement the robust controller with a model-free multi-parametric extremum seeking (MES) control to iteratively auto-tune the feedback gains. We analyze the stability of the whole controller, i.e. robust nonlinear controller plus model-free learning algorithm. We use numerical tests to demonstrate the performance of this method on a mechatronics example.Comment: To appear at the IEEE CDC 201

    Nonlinear adaptive estimation with application to sinusoidal identification

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    Parameter estimation of a sinusoidal signal in real-time is encountered in applications in numerous areas of engineering. Parameters of interest are usually amplitude, frequency and phase wherein frequency tracking is the fundamental task in sinusoidal estimation. This thesis deals with the problem of identifying a signal that comprises n (n ≥ 1) harmonics from a measurement possibly affected by structured and unstructured disturbances. The structured perturbations are modeled as a time-polynomial so as to represent, for example, bias and drift phenomena typically present in applications, whereas the unstructured disturbances are characterized as bounded perturbation. Several approaches upon different theoretical tools are presented in this thesis, and classified into two main categories: asymptotic and non-asymptotic methodologies, depending on the qualitative characteristics of the convergence behavior over time. The first part of the thesis is devoted to the asymptotic estimators, which typically consist in a pre-filtering module for generating a number of auxiliary signals, independent of the structured perturbations. These auxiliary signals can be used either directly or indirectly to estimate—in an adaptive way—the frequency, the amplitude and the phase of the sinusoidal signals. More specifically, the direct approach is based on a simple gradient method, which ensures Input-to-State Stability of the estimation error with respect to the bounded-unstructured disturbances. The indirect method exploits a specific adaptive observer scheme equipped with a switching criterion allowing to properly address in a stable way the poor excitation scenarios. It is shown that the adaptive observer method can be applied for estimating multi-frequencies through an augmented but unified framework, which is a crucial advantage with respect to direct approaches. The estimators’ stability properties are also analyzed by Input-to-State-Stability (ISS) arguments. In the second part we present a non-asymptotic estimation methodology characterized by a distinctive feature that permits finite-time convergence of the estimates. Resorting to the Volterra integral operators with suitably designed kernels, the measured signal is processed, yielding a set of auxiliary signals, in which the influence of the unknown initial conditions is annihilated. A sliding mode-based adaptation law, fed by the aforementioned auxiliary signals, is proposed for deadbeat estimation of the frequency and amplitude, which are dealt with in a step-by-step manner. The worst case behavior of the proposed algorithm in the presence of bounded perturbation is studied by ISS tools. The practical characteristics of all estimation techniques are evaluated and compared with other existing techniques by extensive simulations and experimental trials.Open Acces

    Direct Adaptive Control for a Trajectory Tracking UAV

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    This research focuses on the theoretical development and analysis of a direct adaptive control algorithm to enable a fixed-wing UAV to track reference trajectories while in the presence of persistent external disturbances. A typical application of this work is autonomous flight through urban environments, where reference trajectories would be provided by a path planning algorithm and the vehicle would be subjected to significant wind gust disturbances. Full 6-DOF nonlinear and linear UAV simulation models are developed and used to study the performance of the direct adaptive control system for various scenarios. A stability proof is developed to prove convergence of the direct adaptive control system under certain conditions. Specific adaptive controller implementation details are provided, including the use of a sensor blending algorithm to address the non-minimum phase properties of the UAV models. The robustness of the adaptive system pertaining to the amount of modeling error that can be accommodated by the controller is studied, and the disturbance rejection capabilities and limitations of the controllers are also analyzed. The overall results of this research demonstrate that the direct adaptive control algorithm can enable trajectory tracking in cases where there are both significant uncertainties in the external disturbances and considerable error in the UAV model

    Bounded attitude control of rigid bodies: Real-time experimentation to a quadrotor mini-helicopter

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    International audienceA quaternion-based feedback is developed for the attitude stabilization of rigid bodies. The control design takes into account a priori input bounds and is based on nested saturation approach. It results in a very simple controller suitable for an embedded use with low computational resources available. The proposed method is generic not restricted to symmetric rigid bodies and does not require the knowledge of the inertia matrix of the body. The control law can be tuned to force closed-loop trajectories to enter in some a priori fixed neighborhood of the origin in a finite time and remain thereafter. The global stability is guaranteed in the case where angular velocities sensors have limited measurement range. The control law is experimentally applied to the attitude stabilization of a quadrotor mini-helicopter
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