Stable predictive control horizons

Estrada, R. Lopez Lena; Favela, Antonio; Raimondi, Angelo; Nevado, Antonio; Requena, Ricardo; Beltrán-Carbajal, Francisco

doi:10.1080/00207179.2011.652363

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Figure 1 shows a global scheme of the APC strategy. The Driver Block (DB) calculates the trajectory that guides the controlled variables to their references in an optimal way, based on the driver block equation [5,19]. The Predictive Model (PM) computes the control signals at every instant k; its objective is to lead the process output to meet the desired trajectory estimated by the DB.…”

Section: General Control Strategymentioning

confidence: 99%

“…Equation (19) shows the predicted output at instant k + j based on the process parameters, inputs and outputs at every instant k. The recursiveness of the parameters matrices are defined as follow:…”

Section: Multivariable Predictive Control Strategymentioning

confidence: 99%

“…) and considering the predictive control law (26), Equation (19) leads to the control law expression as follows:…”

Section: Control Lawmentioning

confidence: 99%

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Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model

et al. 2022

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Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This class of SISO-APC based on ARX models has been successfully and extensively used in many industrial applications. This approach aims to minimize the barriers between the theory of predictive adaptive control and its application in the industrial environment. The proposed MIMO-APC (MIMO adaptive predictive controller) performance is validated with two simulated processes: a quadrotor drone and the quadruple tank process. In the first experiment the proposed MIMO APC shows ISE-IAE-ITAE performance indices improvements of up to 25%, 25.4% and 38.9%, respectively. For the quadruple tank process the water levels in the lower tanks follow closely the set points, with the exception of a 13% overshoot in tank 1 for the minimum phase behavior response. The controller responses show significant performance improvements when compared with previously published MIMO control strategies.

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Section: General Control Strategymentioning

confidence: 99%

Section: Multivariable Predictive Control Strategymentioning

confidence: 99%

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Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model

et al. 2022

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“…Stability issue of MPC is studied in several papers in the literature, an exhaustive review of stability solutions for MPC is provided in [39]. In [40] and [41] closed-loop stability criterion is presented for extended predictive control by taking into account the prediction horizon value and the reference trajectory dynamics. Necessary and sufficient conditions of infinite horizon Generalized Predictive Control (GPC) GPC ∞ is presented in [42] with no need to solve a Lyapunov equation.…”

Section: Spc Stabilitymentioning

confidence: 99%

Subspace predictive control: stability and performance enhancement

Sedghizadeh¹

2021

Preprint

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In the absence of prior knowledge of a system, control design relies heavily on the system identifi- cation procedure. In real applications, there is an increasing demand to combine the usually time consuming system identification and modeling step with the control design procedure. Motivated by this demand, data-driven control approaches attempt to use the input-output data to design the controller directly. Subspace Predictive Control (SPC) is one popular example of these algorithms that combines Model Predictive Control (MPC) and Subspace Identification Methods (SIM). SPC instability and performance deterioration in closed-loop implementations are majorly caused by either poor tuning of SPC horizons or changes in the dynamics of the system. Stability and performance analysis of the SPC are the focus of this dissertation. We first provide the necessary and sufficient condition for SPC closed-loop stability. The results introduce SPC stability graphs that can provide the feasible prediction horizon range. Consequently, these stability constraints are included in SPC cost function optimization to provide a new method for determining the SPC horizons. The novel SPC horizon selection enhances the closed-loop performance effectively. Note that time-delay estimation and order selection in system modeling have been a challenging step in applications and industry. Here, we propose a new approach denoted by RE-based TDE that simultaneously and fficiently estimates the time-delay for the SIM framework. In addition, we use the recently developed MSEE approach for estimating the system order. Moreover, we propose an arti- ficial intelligence approach denoted by Particle Swarm Optimization Based Fuzzy Gain-Scheduled SPC (PSO-based FGS-SPC). The method overcomes the issue of on-line adaptation of SPC gains for systems with variable dynamics in the presence of the noisy data. The approach eliminates existing tuning problem of controller gain ranges in FGS and updates the SPC gains with no need to apply any external persistently excitation signals. As a result, PSO-based FGS-SPC provides a time efficient control strategy with fast and robust tracking performance compared to conventional and state of the art methods.

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Data-driven subspace predictive control: Stability and horizon tuning

Sedghizadeh

Beheshti

2018

Journal of the Franklin Institute

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Stable predictive control horizons

Cited by 7 publications

References 13 publications

Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model

Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model

Subspace predictive control: stability and performance enhancement

Data-driven subspace predictive control: Stability and horizon tuning

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