Shortest-prediction-horizon non-linear model-predictive control with guaranteed asymptotic stability

Panjapornpon, Chanin; Soroush, Masoud

doi:10.1080/00207170601186200

“…This issue is addressed in [38] through the use of Lyapunov inequality constraints. All the above mentioned methods are restricted to special cases and cannot be generalize to obtain optimum horizon.…”

Section: Spc Tuning Parametersmentioning

confidence: 99%

Subspace predictive control: stability and performance enhancement

Sedghizadeh¹

2021

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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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“…Some features of SPC, such as no pre-assumptions about system model, calculation of predictor matrices without iteration and no need to solving Diophantine equation are some of the advantages of SPC in practical applications [21,22]. With increasing popularity of MPC and SPC in industrial applications [23] such as, chemical engineering [24][25][26], power systems [27][28][29], smart grids and buildings [30,31], network control systems [32,33], vehicle control [34], their closed-loop stability and performance have become significant and controversial issues in predictive control [35][36][37][38].…”

Section: Dissertation Objectivesmentioning

confidence: 99%

Subspace predictive control: stability and performance enhancement

Sedghizadeh¹

2021

Preprint

0

View full text Add to dashboard Cite

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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“…Aided by the extensive research on control accounting for the presence of constraints, nonlinearity, and uncertainty (see, e.g., Lin and Sontag, 1991;Muske and Rawlings, 1993;Valluri and Soroush, 1998;Kapoor and Daoutidis, 2000;Mayne et al, 2000;Dubljevic and Kazantzis, 2002;Mhaskar et al, 2005Huynh and (1998), Kapoor and Daoutidis (2000), Mayne et al (2000), Dubljevic and Kazantzis (2002), Mhaskar et al (2005, Huynh and Kazantzis (2005), Karafyllis and Kravaris (2005), Christofides and El-Farra (2005), and Panjapornpon and Soroush (2007) have been utilized within reconfiguration-based fault-tolerant control structures focusing on closed-loop stability and performance, while accounting for process nonlinearity and constraints (see, e.g., Mhaskar et al, , 2008Mhaskar, 2006). However, all the reconfigurationbased fault-tolerant control designs of Mhaskar (2006) and Mhaskar et al ( , 2008 assume the existence of a backup, redundant control configuration.…”

Section: Introductionmentioning

confidence: 99%