Singular linear parameter‐varying observer for composition estimation in a binary distillation column

Gonzalez, Adriana Aguilera; Astorga‐Zaragoza, C.M.; Medina, Manuel Adam; Theilliol, Didier; Reyes-Reyes, J.; García-Beltrán, Carlos-Daniel

doi:10.1049/iet-cta.2011.0469

Cited by 19 publications

(9 citation statements)

References 36 publications

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“…MPC can be directly used to control a great variety of simple or complex processes, with long delay times, non-minimum phase or unstable process. It can also be applied for multivariable processes with dead times [22,23]. Knowing the future reference signals, constraints on input, state and output variable are part of the controller, and optimal control actions are calculated, for example quadratic programming (QP) [24].…”

Section: Mpc Strategymentioning

confidence: 99%

Reduced order modelling and predictive control of multivariable nonlinear process

et al. 2018

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In this paper, an efficient model-predictive control strategy that can be applied to complex multivariable process is presented. A reduced order generalized predictive algorithm is proposed for online applications with reduction in complexity and time elapsed. The complex multivariable process considered in this work is a binary distillation column. The reduced order model is developed with a recently proposed hybrid algorithm known as Clustering Dominant Pole Algorithm and is able to compute the full set of dominant poles and their cluster centre efficiently. The controller calculates the optimal control action based on the future reference signals, current state and constraints on manipulated and controlled variables for a high-order dynamic simulated model of nonlinear multivariable binary distillation column process. The predictive control algorithm uses controlled auto-regressive integrated moving average model. The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE.

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Section: Mpc Strategymentioning

confidence: 99%

Reduced order modelling and predictive control of multivariable nonlinear process

et al. 2018

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“…In addition, a binary distillation column can be controlled by using a linear proportional-integral observer in [22]. In [23], a polynomial controller was proposed for a quad-rotor unmanned aerial vehicle (UAV) combined with a linear observer to deal with bounded uncertainties and disturbances.…”

Section: Introductionmentioning

confidence: 99%

Comparative investigations of nonlinear and linear observers for a highly manoeuvrable target in sliding mode guidance

Wang¹,

Sun²,

Du³

et al. 2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. Target manoeuvre is one of the key factors affecting guidance accuracy. To intercept highly maneuverable targets, a second-order sliding-mode guidance law, which is based on the super-twisting algorithm, is designed without depending on any information about the target motion. In the designed guidance system, the target estimator plays an essential role. Besides the existing higher-order sliding-mode observer (HOSMO), a first-order linear observer (FOLO) is also proposed to estimate the target manoeuvre, and this is the major contribution of this paper. The closed-loop guidance system can be guaranteed to be uniformly ultimately bounded (UUB) in the presence of the FOLO. The comparative simulations are carried out to investigate the overall performance resulting from these two categories of observers. The results show that the guidance law with the proposed linear observer can achieve better comprehensive criteria for the amplitude of normalised acceleration and elevator deflection requirements. The reasons for the different levels of performance of these two observer-based methods are thoroughly investigated.Key words: guidance law, second-order sliding mode, super-twisting algorithm, linear observer, target manoeuvre. matched disturbances. However, the chattering phenomenon is the major obstacle for the implementation of SMC in practice, and a number of methods have been proposed to reduce the chattering. Levant first proposed the higher-order sliding mode (HOSM) control method to attenuate the chattering [9][10][11][12]. The second-order sliding mode control is the most widely used HOSM method in which the super-twisting algorithm is utilised to attenuate the differentiable disturbance and ensure the finite-time stability [13,14]. To intercept highly manoeuvrable targets, the second-order sliding mode methods were utilised in [13] and [14], based on the nonlinear observers, which are used to check the uncertainty caused by target manoeuvres. However, in many simulations, it is readily observed that the guidance commands (acceleration) generated by the proposed guidance laws of [13] and [14] exhibit the properties of oscillation with a large amplitude. Comparing the fundamental guidance component with the observer component, the latter contributes to this phenomenon much more. Hence, an effective estimation approach is urgently needed to deal with highly manoeuvrable targets.Han proposed a novel philosophy, namely the active disturbance rejection control (ADRC) algorithm [15,16], which has attracted wide attention in the past decade. The core of ADRC is an extended state observer (ESO) that can accurately observe the total disturbance. Han originally presented the ESO in a nonlinear form [15], which is too complicated to tune, and is difficult to be implemented. In [16], a linear ESO (LESO) was proposed in terms of frequency bandwidth, which provides a useful design approach in practical applications. Although this

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“…Most of the papers deal with FDI for state-space LPV systems (Yoneyama, 2009;Theilliol and Aberkane, 2011;Ichalal et al, 2010;Chadli et al, 2013a;Blesa et al, 2014). Some of them are related to fault detection for D-LPVsystems with measurable scheduling functions (Hamdi et al, 2012b;Astorga-Zaragoza et al, 2011;Aguilera-González et al, 2013) and only several are related to D-LPV systems. However, we can mention Nagy- Kiss et al (2011a), who propose a state observer by transforming the D-LPV system with unmeasurable scheduling functions into an equivalent uncertain system.…”

Section: Introductionmentioning

confidence: 99%

Robust sensor fault estimation for descriptor-LPV systems with unmeasurable gain scheduling functions: Application to an anaerobic bioreactor

López‐Estrada

Ponsart

Astorga‐Zaragoza

et al. 2015

International Journal of Applied Mathematics and Computer Science

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This paper addresses the design of a state estimation and sensor fault detection, isolation and fault estimation observer for descriptor-linear parameter varying (D-LPV) systems. In contrast to where the scheduling functions depend on some measurable time varying state, the proposed method considers the scheduling function depending on an unmeasurable state vector. In order to isolate, detect and estimate sensor faults, an augmented system is constructed by considering faults to be auxiliary state vectors. An unknown input LPV observer is designed to estimate simultaneously system states and faults. Sufficient conditions to guarantee stability and robustness against the uncertainty provided by the unmeasurable scheduling functions and the influence of disturbances are synthesized via a linear matrix inequality (LMI) formulation by considering H∞ and Lyapunov approaches. The performances of the proposed method are illustrated through the application to an anaerobic bioreactor model.

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Singular linear parameter‐varying observer for composition estimation in a binary distillation column

Cited by 19 publications

References 36 publications

Reduced order modelling and predictive control of multivariable nonlinear process

Reduced order modelling and predictive control of multivariable nonlinear process

Comparative investigations of nonlinear and linear observers for a highly manoeuvrable target in sliding mode guidance

Robust sensor fault estimation for descriptor-LPV systems with unmeasurable gain scheduling functions: Application to an anaerobic bioreactor

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