Disturbance estimation and compensation in linear systems

Profeta, J.A.; Vogt, William G.; Mickle, Marlin H.

doi:10.1109/7.53455

Cited by 61 publications

(25 citation statements)

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“…Though the traditional DAC approach only considers disturbance functions which exhibit waveform patterns over short intervals of time (Johnson and Kelly 1981), a more general formulation of DAC can accommodate the simultaneous presence of both 'noise' type disturbances and 'waveform structured' disturbances (Johnson 1984(Johnson , 1985. The disturbance accommodating observer approach has been shown to be extremely effective for disturbance attenuation (Profeta, Vogt, and Mickle 1990;Kim and Oh 1998;Biglari and Mobasher 2000); however, the performance of the observer can significantly vary for different types of exogenous disturbances, which is due to observer gain sensitivity.…”

Section: Introductionmentioning

confidence: 98%

Adaptive stochastic disturbance accommodating control

George

Singla

2011

International Journal of Control

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This article presents a Kalman filter based adaptive disturbance accommodating stochastic control scheme for linear uncertain systems to minimise the adverse effects of both model uncertainties and external disturbances. Instead of dealing with system uncertainties and external disturbances separately, the disturbance accommodating control scheme lumps the overall effects of these errors in a to-be-determined model-error vector and then utilises a Kalman filter in the feedback loop for simultaneously estimating the system states and the model-error vector from noisy measurements. Since the model-error dynamics is unknown, the process noise covariance associated with the model-error dynamics is used to empirically tune the Kalman filter to yield accurate estimates. A rigorous stochastic stability analysis reveals a lower bound requirement on the assumed system process noise covariance to ensure the stability of the controlled system when the nominal control action on the true plant is unstable. An adaptive law is synthesised for the selection of stabilising system process noise covariance. Simulation results are presented where the proposed control scheme is implemented on a two degree-of-freedom helicopter.

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Section: Introductionmentioning

confidence: 98%

Adaptive stochastic disturbance accommodating control

George

Singla

2011

International Journal of Control

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“…Potentially, the SSIE can be applied to a wide range of problems such as fault diagnosis (see, e.g. Gao & Ding, 2007;Patton, Clark, & Frank, 1989), fault-tolerant control (Jiang & Fahmida, 2005), disturbance rejection control (Profeta, Joseph, William, & Marin, 1990). In the field of fault detection and fault-tolerant control, for example, actuator, sensor and/or structure faults are usually modelled as inputs to the system with unknown dynamics.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous state and input estimation with partial information on the inputs

2015

Systems Science & Control Engineering

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This paper investigates the problem of simultaneous state and input estimation (SSIE) for discrete-time linear stochastic systems when the information on the inputs is partially available. To incorporate the partial information on the inputs, matrix manipulation is used to obtain an equivalent system with reduced-order inputs. Then Bayesian inference is drawn to obtain a recursive filter for both state and input variables. The proposed filter is an extension of the recently developed state filter with partially observed inputs to the case where the input filter is also of interest, and an extension of the SSIE to the case where the information on the inputs is partially available. A numerical example is given to illustrate the proposed method. It is shown that, due to the additional information on the inputs being incorporated in the filter design, the performances of both state and input estimation are substantially improved in comparison with the conventional SSIE without partial input information.

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“…These techniques include Disturbance Observer [3], Unknown Input Observer [4], and Perturbation Observer [5]. The above methods are relatively simple to implement but are mostly dedicated to linear, time-invariant cases with an additional assumption that the precise mathematical description of the plant is somehow available.…”

Section: State Of the Artmentioning

confidence: 99%

Active Disturbance Rejection Control of a 2DOF manipulator with significant modeling uncertainty

Przybyla¹,

Kordasz²,

Madoński³

et al. 2012

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. This paper presents a practical verification of an Active Disturbance Rejection Control (ADRC) method in governing a multidimensional system. The experiments were conducted on a two degrees of a freedom planar manipulator with only partial knowledge about the mathematical model of the plant. This multi input multi output system was controlled with a set of two, independent, single input single output ADRC controllers, each regulating one of the manipulator degree of freedom. Modeling uncertainty (nonlinearities, cross-coupling effects, etc.) and external disturbances were assumed to be a part of the disturbance, to be estimated with an observer and cancelled on-line in the control loop. The ADRC robustness was experimentally compared with the results obtained from using two decentralized, classic PID controllers. Both control methods were tested under various conditions, e.g. changing the inertial parameters of the plant. Significantly better results, in terms of parametric robustness, have been reported for the ADRC approach.

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Disturbance estimation and compensation in linear systems

Cited by 61 publications

References 1 publication

Adaptive stochastic disturbance accommodating control

Adaptive stochastic disturbance accommodating control

Simultaneous state and input estimation with partial information on the inputs

Active Disturbance Rejection Control of a 2DOF manipulator with significant modeling uncertainty

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