Robust adaptive optimal control modification with large adaptive gain

Nguyen, Nhan T.; Ishihara, Abraham K.

doi:10.1109/acc.2009.5159904

Cited by 13 publications

(9 citation statements)

References 15 publications

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“…Note that parameter set INIT was chosen such that each scalar parameter was within an order of magnitude of the value to be identified. The choice of optimal adaptation gains is a long-standing open problem in adaptive systems theory [16], [17]. From simulations of the adaptive identification algorithm, we empirically chose angular velocity, inertia tensor, quadratic drag and buoyancy torque adaption gains of a = 10, γ 1 = 5000, γ 2 = 10000, and γ 3 = 1000 respectively.…”

Section: B Experimental Testing Methodologymentioning

confidence: 99%

A new adaptive identifier for second-order rotational plants with applications to underwater vehicles

McFarland

Whitcomb

2012

2012 Oceans

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This paper reports a novel adaptive identifier for the estimation of the plant parameters of three degree-offreedom second-order rigid-body rotational plants under the influence of externally applied torques. A second adaptive identifier is also reported which, in addition to estimating the rigid body parameters, identifies quadratic drag, and buoyancy torque parameters that arise in the rotational dynamic models of rigid-body underwater vehicles. Although a rich collection of literature exists on the problem of model-based adaptive trajectory-tracking control, these approaches are not applicable when the plant is either uncontrolled, under open-loop control, or using any control law other than a specific adaptive tracking controller. In contrast, the adaptive identifiers reported herein provide an approach to plant parameter estimation applicable to the commonly occurring cases of uncontrolled plants, plants under open-loop control, and plants using control methods prescribed to meet other considerations for an application. Local stability proofs of the new adaptive identifiers are reported. The stability analysis show that the angular velocity estimate of the adaptive identifiers converges asymptotically to the angular velocity of the actual plant, that the parameter estimates are stable, and that the parameter estimates converge asymptotically to values that provide input/output model behavior identical to that of the actual plant. A comparative experimental evaluation of the second adaptive identifier is reported using the Johns Hopkins University Remotely Controlled Underwater Vehicle. The experimental results corroborate the analytical stability analysis, showing that the angular velocity estimate of the adaptive identifier converges asymptotically to the angular velocity of the actual plant, and the adaptively estimated plant parameters asymptotically converge to values that provide plant-model input/output behavior closely approximating the input/output behavior of the actual experimental underwater vehicle. An experimental comparison of adaptive identification and conventional least squares parameter identification is also reported. The adaptively identified model was shown to be similar to the least squares identified model in its ability to match the actual vehicle's input/output characteristics. The common formulation of least squares identifiers require angular acceleration, a measurement which is not required for the adaptive identification algorithm presented herein. Since underwater vehicles are often not instrumented to measure angular acceleration, this method may provide model parameter estimates that could not be obtained by other standard methods.

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Section: B Experimental Testing Methodologymentioning

confidence: 99%

A new adaptive identifier for second-order rotational plants with applications to underwater vehicles

McFarland

Whitcomb

2012

2012 Oceans

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“…Many of the results in adaptive control are derived from Lyapunov stability theory, such as parameter and indirect adaptive control schemes [6], [7]. The sensitivity of some adaptive schemes to disturbances and unmodeled dynamics 978-1-4244-6588-0/10/$25.00 ©201 0 IEEE prompted many researchers to investigate robust adaptive nonlinear control [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Neural networks for helicopter azimuth and elevation angles control obtained by cloning processes

Uzunović

Velagić

Osmić

et al. 2010

2010 IEEE International Conference on Systems, Man and Cybernetics

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Neural networks have been applied very successfully in the identification and control of nonlinear dynamic systems. The paper presents a design of neural network based control system for 2DOF nonlinear laboratory helicopter model (Humusoft CE 150). The main objective of this paper is to develop artificial neural networks to control helicopter's motors, or consequently elevation and azimuth angles. Neural networks are obtained by cloning various type of controllers designed in our previous papers. Those procedures included a cloning linear PID controller, gain scheduling controller and fuzzy controller. K�words-helicopter model, neural network, cloning process, fuzzy controller, adaptive gain scheduling control

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“…However, updating laws with high learning rates may yield signals of high-frequency, which can, for example, excite unmodeled dynamics, and even result in instability for real-world applications. There have been great efforts on modifications of control architectures or updating laws for improving the transient performance of adaptive control systems [52][53][54]. However, most works are practiced within the model reference adaptive control framework.…”

Section: Introductionmentioning

confidence: 99%

Predictor-based neural dynamic surface control for distributed formation tracking of multiple marine surface vehicles with improved transient performance

Peng

Wang

2016

Sci. China Inf. Sci.

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In this paper, we investigate the distributed formation tracking problem of multiple marine surface vehicles with model uncertainty and time-varying ocean disturbances induced by wind, waves, and ocean currents. The objective is to achieve a collective tracking with a time-varying trajectory, which can only be accessed by a fraction of follower vehicles. A novel predictor-based neural dynamic surface control design approach is proposed to develop the distributed adaptive formation controllers. We use prediction errors, rather than tracking errors, to construct the neural adaptive laws, which enable the fast identification of the vehicle dynamics without incurring high-frequency oscillations in control signals. We establish the stability properties of the closed-loop network via Lyapunov analysis, and quantify the transient performance by deriving the truncated L 2 norms of the derivatives of neural weights, which we demonstrate to be smaller than the classical neural dynamic surface control design approach. We also extend the above result to the distributed formation tracking using the relative position information of vehicles, and the advantage is that the velocity information of neighbors and leader are required. Finally, we give the comparative studies to illustrate the performance improvement of the proposed method.

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Robust adaptive optimal control modification with large adaptive gain

Cited by 13 publications

References 15 publications

A new adaptive identifier for second-order rotational plants with applications to underwater vehicles

A new adaptive identifier for second-order rotational plants with applications to underwater vehicles

Neural networks for helicopter azimuth and elevation angles control obtained by cloning processes

Predictor-based neural dynamic surface control for distributed formation tracking of multiple marine surface vehicles with improved transient performance

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