A robust control algorithm for AUV: based on a high order sliding mode

Salgado-Jiménez, Tomás; Spiewak, J.-M.; Fraisse, Philippe; Jouvencel, B.

doi:10.1109/oceans.2004.1402929

Cited by 27 publications

(16 citation statements)

References 6 publications

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“…x r ] ∈ R r , and u ∈ R are the state vector, and the scalar input variable, respectively. Moreover, it is assumed that f is smooth and sufficiently differentiable [18]. The sliding mode order is the number of continuous successive derivatives of the differentiable sliding variable s ∈ R, and it is a measure of the degree of smoothness of the sliding variable in the vicinity of the sliding manifold.…”

Section: Second Order Discrete Sliding Mode Controlmentioning

confidence: 99%

Adaptive Discrete Second-Order Sliding Mode Control With Application to Nonlinear Automotive Systems

Amini

Shahbakhti

Pan

2018

Journal of Dynamic Systems, Measurement, and Control

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Sliding mode control (SMC) is a robust and computationally efficient model-based controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. However, the implementation of the conventional continuous-time SMC on digital computers is limited, due to the imprecisions caused by data sampling and quantization, and the chattering phenomena, which results in high frequency oscillations. One effective solution to minimize the effects of data sampling and quantization imprecisions is the use of higher order sliding modes. To this end, in this paper, a new formulation of an adaptive second order discrete sliding mode control (DSMC) is presented for a general class of multi-input multi-output (MIMO) uncertain nonlinear systems. Based on a Lyapunov stability argument and by invoking the new Invariance Principle, not only the asymptotic stability of the controller is guaranteed, but also the adaptation law is derived to remove the uncertainties within the nonlinear plant dynamics. The proposed adaptive tracking controller is designed and tested in real-time for a highly nonlinear control problem in spark ignition combustion engine during transient operating conditions. The simulation and real-time processor-in-the-loop (PIL) test results show that the second order single-input single-output (SISO) DSMC can improve the tracking performances up to 90%, compared to a first order SISO DSMC under sampling and quantization imprecisions, in the presence of modeling uncertainties. Moreover, it is observed that by converting the engine SISO controllers to a MIMO structure, the overall controller performance can be enhanced by 25%, compared to the SISO second order DSMC, because of the dynamics coupling consideration within the MIMO DSMC formulation. arXiv:1805.06800v1 [math.OC]

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Section: Second Order Discrete Sliding Mode Controlmentioning

confidence: 99%

Adaptive Discrete Second-Order Sliding Mode Control With Application to Nonlinear Automotive Systems

Amini

Shahbakhti

Pan

2018

Journal of Dynamic Systems, Measurement, and Control

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“…The better performance of the second order DSMC can be traced in driving the second order derivative (difference function) of the system states to the sliding manifold, in addition to the zero convergence of the sliding variable itself. The second order sliding mode for a continuous-time system is determined by the following equalities [11,21]:…”

Section: Second Order Adaptive Dsmcmentioning

confidence: 99%

MIMO First and Second Order Discrete Sliding Mode Controls of Uncertain Linear Systems Under Implementation Imprecisions

Amini

Shahbakhti

Pan

2017

Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Cont

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The performance of a conventional model-based controller significantly depends on the accuracy of the modeled dynamics. The model of a plant's dynamics is subjected to errors in estimating the numerical values of the physical parameters, and variations over operating environment conditions and time. These errors and variations in the parameters of a model are the major sources of uncertainty within the controller structure. Digital implementation of controller software on an actual electronic control unit (ECU) introduces another layer of uncertainty at the controller inputs/outputs. The implementation uncertainties are mostly due to data sampling and quantization via the analog-todigital conversion (ADC) unit. The failure to address the model and ADC uncertainties during the early stages of a controller design cycle results in a costly and time consuming verification and validation (V&V) process. In this paper, new formulations of the first and second order discrete sliding mode controllers (DSMC) are presented for a general class of uncertain linear systems. The knowledge of the ADC imprecisions is incorporated into the proposed DSMCs via an online ADC uncertainty prediction mechanism to improve the controller robustness characteristics. Moreover, the DSMCs are equipped with adaptation laws to remove two different types of modeling uncertainties (multiplicative and additive) from the parameters of the linear system model. The proposed adaptive DSMCs are evaluated on a DC motor speed control problem in real-time using a processor-in-the-loop (PIL) setup with an actual ECU. The results show that the proposed SISO and MIMO second order DSMCs improve the conventional SISO first order DSMC tracking performance by 69% and 84%, respectively. Moreover, the proposed adaptation mechanism is able to remove the uncertainties in the model by up to 90%. NOMENCLATURE INTRODUCTIONThere are two major sources of uncertainties which make the completion of traditional verification and validation (V&V) cycle of a model-based controller challenging and costly. These uncertainties are mostly due to: (i) errors in estimating the model parameters and physical changes in the plant or fluctuations in the environment in which the system operates, and (ii) imprecisions often arise during digital implementation of the controller software on an actual electronic control unit (ECU) via analogto-digital converter (ADC) unit. Neglecting the modeling and implementation uncertainties during the early stages of the controller design cycle leads to substantial deviation in the controller performance once it is implemented in the real ECU [1,2].It has been shown in the literature [3][4][5] that the performance of a conventional model-based controller is considerably sensitive to any errors in the modeled plant dynamics, and ignoring the uncertainties in the model results in the controller failure. Moreover, digital implementation of a controller software on an actual ECU introduces sampling and quantization imprecision on the controller in...

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“…It is easy to learn, high stability, and is most common in the field of visual simulation software [7,8]. Labview Simulation Interface Toolkit [9,10] (SIT) happens to combine the advantages of both, to provide seamless integration between Matlab / Simulink and Labview [11][12][13]. It can automatically generate Labview code and interact with Simulink models to provide users with a flexible and easy to operate interface [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Gas Engine Hardware-in-Loop Simulation System Design Based on Virtual Instrument

Gan

2014

2014 Sixth International Conference on Measuring Technology and Mechatronics Automation

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The method of engine software simulation based on Matlab/Simulink and LabView is investigated, and the principle and the realization of engine software simulation with LabView SIT are illustrated including principle of engine simulation, engine model configuration and engine simulation running. Experimental results show that man-machine interface is good. At the same time operation is convenient, and the simulation results are obvious. This method is applied wildely in engine in-loop simulation.

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A robust control algorithm for AUV: based on a high order sliding mode

Cited by 27 publications

References 6 publications

Adaptive Discrete Second-Order Sliding Mode Control With Application to Nonlinear Automotive Systems

Adaptive Discrete Second-Order Sliding Mode Control With Application to Nonlinear Automotive Systems

MIMO First and Second Order Discrete Sliding Mode Controls of Uncertain Linear Systems Under Implementation Imprecisions

Gas Engine Hardware-in-Loop Simulation System Design Based on Virtual Instrument

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