The governance of common-pool resources has vital importance for sustainability. However, in the realistic management systems of common-pool resources, the institutions do not necessarily execute the management polici...
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The governance of common-pool resources has vital importance for sustainability. However, in the realistic management systems of common-pool resources, the institutions do not necessarily execute the management policies completely, which will induce that the real implementation intensity is uncertain. In this paper, we consider a feedback-evolving game model with the inspection for investigating the management of renewable resource and assume that there exists the implementation uncertainty of inspection. Furthermore, we use the nonlinear model reference adaptive control approach to handle this uncertainty. We accordingly design a protocol, which is an update law of adjusting the institutional inspection intensity. We obtain a sufficient condition under which the update law can drive the actual system to reach the expected outcome. In addition, we provide several numerical examples, which can confirm our theoretical results. Our work presents a novel approach to address the implementation uncertainty in the feedback-evolving games and thus our results can be helpful for effectively managing the common-pool resources in the human social systems.
Robotic exoskeletons are expected to show high compliance and low impedance for human-robot interactions (HRIs). Our study introduces a novel method based on nonlinear model reference adaptive control (MRAC) to reduce...
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Robotic exoskeletons are expected to show high compliance and low impedance for human-robot interactions (HRIs). Our study introduces a novel method based on nonlinear model reference adaptive control (MRAC) to reduce the inherent impedance and replace the traditional impedance controller in HRIs. The control law and adaptive law are designed according to a candidate Lyapunov function. A simple system identification and initialization method for the nonlinear MRAC is put forward, which provides a set of better initial values for the controller. From the results of simulation and experiment, our controller can reduce the mechanical impedance and achieve high compliance for HRI. The adaptivecontrol and compliance control can be both achieved by the proposed nonlinear MRAC framework.
The paper presents a modelreferenceadaptivecontrol (MRAC) of first and second order to control the nonlinear dynamics of an atomic force microscope (AFM) cantilever, which is operated in contact mode. The AFM is a ...
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ISBN:
(纸本)9781509011476
The paper presents a modelreferenceadaptivecontrol (MRAC) of first and second order to control the nonlinear dynamics of an atomic force microscope (AFM) cantilever, which is operated in contact mode. The AFM is a powerful tool to measure the topography of a sample at the scale of a few nanometers, where a small sharp tip supported in a micro cantilever scans the surface. In the contact mode the sample ' s topography is obtained by using the closed-loop control that holds the tip sample force constant. The nonlinear dynamics of the tip-sample system is very complex with different kinds of nonlinear forces that act between the tip and the sample. Here the dominated force depends on the distance tip-sample. In the present work we use a modified Hertz model to describe the nonlinear force when the distance tip-sample is less than 1 nm. First the complex nonlinear tip-sample system is controlled with a nonlinear MRAC of 1st order and after with a nonlinear MRAC of 2nd order. The results of both control strategies were compared in order to see which one gives a better control perfomance. Here a stability proof for both MRAC methods is present. A variety of simulation results are presented to demonstrate the efficacy of the proposed methods. The procedure is general and can be applied to any nonlinear system.
In this paper, we present a new adaptive predictive controller solving the problem of ill-defined relative degree of certain class of nonlinear systems. This control law takes into account the unknown and time varying...
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ISBN:
(纸本)0780386620
In this paper, we present a new adaptive predictive controller solving the problem of ill-defined relative degree of certain class of nonlinear systems. This control law takes into account the unknown and time varying character of the system parameters. The convergence analysis of the new proposed adaptivecontrol algorithm is also proved leading the tracking error asymptotically converges towards zero. Simulations results are given showing the effectiveness of the control algorithm.
The Convergence analysis of a nonlinear predictive adaptivecontroller is studied for a Single input Single output nonlinear system. The controller is based on a predictive referenceadaptivecontrol algorithm where t...
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ISBN:
(纸本)0780383796
The Convergence analysis of a nonlinear predictive adaptivecontroller is studied for a Single input Single output nonlinear system. The controller is based on a predictive referenceadaptivecontrol algorithm where the plant is fed by a control input constructed from a feedback linearization control technique. The convergence proof is handled using Lyapunov stability theory.
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