A new Second Order (SO) adaptivecontrol approach, that accounts for both matched and unmatched unknown time-varying uncertainties, is proposed in this paper. It is developed based on the Predictor based model Referen...
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A new Second Order (SO) adaptivecontrol approach, that accounts for both matched and unmatched unknown time-varying uncertainties, is proposed in this paper. It is developed based on the Predictor based model reference adaptive control (PMRAC) structure. In this approach, the estimated parameter dynamics is composed of a sigma-modified PMRAC update law and a predicted parameter dynamics, with projection operator. The predicted parameter dynamics consists of a stable known eigendynamics, a feedback of an approximate parameter error, and the remaining part linearly parameterized by a set of known regressor functions combined with an adaptive estimate of unknown second-order parameters, which are updated by a Lyapunov design. Due to the estimation of time derivatives of the parameters, the proposed predictor based SO-MRAC is hence capable of capturing the rapid parameter variations in system dynamics. Furthermore, a generalized ultimate boundedness formulation based on the Lyapunov theory is presented for its stability analysis. This formulation provides separate bounds for each of the state vector partitions, mainly tracking and parameter estimation errors. Finally, this proposed approach is applied to control of lateral dynamics of an unmanned aircraft to illustrate the usefulness of the proposed approach. The simulation results are found to be quite satisfactory both in terms of tracking and adaptation performance. (C) 2019 European control Association. Published by Elsevier Ltd. All rights reserved.
The accuracy of the motion control for robotic mechanisms will have an effect on their overall performance. Under the condition where the robotic end-effector carries different loads, the motions for each joint of rob...
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The accuracy of the motion control for robotic mechanisms will have an effect on their overall performance. Under the condition where the robotic end-effector carries different loads, the motions for each joint of robotic mechanisms change depending on different payload masses. Conventional control systems possess the potential issue that they cannot compensate the load variation effect. adaptivecontrol, especially the model reference adaptive control (MRAC), has therefore been put forward to handle the above issue. adaptivecontrol is generally divided into three categories, modelreference, self-tuning and gain-scheduled. In this study, the authors only focus on the model-reference approach. To the best of the authors' knowledge, very few recent research articles can be found in the area of MRAC especially for robotic mechanisms since robotic system is a highly nonlinear system, and it is difficult to guarantee the stability of MRAC in such system. This study presents a review and discussion on the MRAC of robotic mechanisms and some issues of MRAC for robotic mechanisms are also demonstrated. This study can provide a guideline for upcoming research in the field of MRAC for robotic mechanisms. (C) 2017 Elsevier Ltd. All rights reserved.
This paper presents a direct model reference adaptive controller for single-input/single-output discrete-time (and thus sampled-data) systems that are possibly nonminimum phase. The adaptivecontrol algorithm requires...
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This paper presents a direct model reference adaptive controller for single-input/single-output discrete-time (and thus sampled-data) systems that are possibly nonminimum phase. The adaptivecontrol algorithm requires knowledge of the nonminimum-phase zeros of the transfer function from the control to the output. This controller uses a retrospective performance, which is a surrogate measure of the actual performance, and a cumulative retrospective cost function, which is minimized by a recursive-least-squares adaptation algorithm. This paper develops the retrospective cost model reference adaptive controller and analyzes its stability.
Owing to the imposed but undesired accelerations such as quadrature error and cross-axis perturbation, the micro-machined gyroscope would not be unconditionally retained at resonant mode. Once the preset resonance is ...
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Owing to the imposed but undesired accelerations such as quadrature error and cross-axis perturbation, the micro-machined gyroscope would not be unconditionally retained at resonant mode. Once the preset resonance is not sustained, the performance of the micro-gyroscope is accordingly degraded. In this article, a direct model reference adaptive control loop which is integrated with a modified disturbance estimating observer (MDEO) is proposed to guarantee the resonant oscillations at drive mode and counterbalance the undesired disturbance mainly caused by quadrature error and cross-axis perturbation. The parameters of controller are on-line innovated by the dynamic error between the MDEO output and expected response. In addition, Lyapunov stability theory is employed to examine the stability of the closed-loop control system. Finally, the efficacy of numerical evaluation on the exerted time-varying angular rate, which is to be detected and measured by the gyroscope, is verified by intensive simulations.
This paper presents a novel formulation of the Lyapunov-based ultimate boundedness theorem with explicit specification of transient and ultimate bounds, time to the ultimate bound, and the set of admissible initial co...
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This paper presents a novel formulation of the Lyapunov-based ultimate boundedness theorem with explicit specification of transient and ultimate bounds, time to the ultimate bound, and the set of admissible initial conditions. The formulation is tailored to the structure of model reference adaptive control systems accounting for a partitioning of the state vector into a tracking error and a parameter estimation error. It allows for a computation of separate bounds for the single state vector partitions in favor of a single bound on the compound state, consisting of both partitions. This is of great practical importance because tracking error and parameter estimation error are of different magnitudes in general. Regarding model reference adaptive control systems, it is the state of the technology so far that the ultimate boundedness theorem is tailored to the structure of the considered system each time, again and again. It is hence manifest to formulate once a generic version of the theorem, tailored to model reference adaptive control systems, but not to reinvent the wheel for each case.
In this paper, a modified model reference adaptive control (MRAC) strategy is developed for use on plants with noise or unmodelled high-frequency dynamics. MRAC consists of two parts, an adaptivecontrol part and a fi...
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In this paper, a modified model reference adaptive control (MRAC) strategy is developed for use on plants with noise or unmodelled high-frequency dynamics. MRAC consists of two parts, an adaptivecontrol part and a fixed gain control part. The adaptive algorithm uses a combination of low- and high-pass filters such that the frequency range for the adaptive part of the strategy is limited. The mechanism for noise-induced gain windup is demonstrated analytically, and it is shown how MRAC can be modified to eliminate this windup. Further to this, an additional filter is proposed to improve MRAC robustness to high-frequency unmodelled dynamics. Two test plants, one with added noise and the other with unmodelled high-frequency dynamics, are considered. Both plants exhibit unstable behaviour when controlled using standard MRAC, but, with the modified strategy, robustness is significantly improved.
An adaptivecontroller with improved performance characteristics is introduced. The proposed controller extends recent results in this area since it achieves performance improvement of the zero-state output error in t...
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An adaptivecontroller with improved performance characteristics is introduced. The proposed controller extends recent results in this area since it achieves performance improvement of the zero-state output error in the presence of some uncertainty on the high-frequency gain of the plant.
A new method is developed for tracking in non-linear delay-differential systems when the exact knowledge of the system parameters are not known. The method is based on securing the stability in the state spaces of pla...
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A new method is developed for tracking in non-linear delay-differential systems when the exact knowledge of the system parameters are not known. The method is based on securing the stability in the state spaces of plant and referencemodels. The results obtained are quite general in nature and can be applied to many physical systems.
An averaging theorem for integro-differential equations is applied to the convergence analysis of controller parameters of a modelreference adapative control (MRAC) algorithm for a class of parabolic partial differen...
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An averaging theorem for integro-differential equations is applied to the convergence analysis of controller parameters of a modelreference adapative control (MRAC) algorithm for a class of parabolic partial differential equations (PDEs) with constant coefficients. The stability of an adaptivecontrol algorithm is proven as well.
In structural control, appropriate control parameters and a stable closed-loop mechanism are required for a controller to achieve optimal performance, particularly in the presence of uncertain structural parameters un...
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In structural control, appropriate control parameters and a stable closed-loop mechanism are required for a controller to achieve optimal performance, particularly in the presence of uncertain structural parameters under external excitation. In this study, an optimum model reference adaptive control (OMRAC) algorithm combining the Linear quadratic regulator method and model reference adaptive control based on Lyapunov stability is proposed. The OMRAC algorithm is implemented and applied to the response control of a base-isolated structure equipped with magneto-rheological dampers under earthquake excitations. The results from a series of numerical simulations that consider the effects of uncertainty within structural parameters are reported. The performance of the OMRAC algorithm is compared with that of other control algorithms in terms of effectiveness and stability. The results suggest that the proposed OMRAC method can successfully compensate for uncertainties in structural parameters, leading the controlled structure to adaptively track the optimal response of the referencemodel.
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