Tuning of Multivariable Model Predictive Control for Industrial Tasks

Nebeluk, Robert; Ławryńczuk, Maciej

doi:10.3390/a14010010

Cited by 16 publications

(8 citation statements)

References 39 publications

(45 reference statements)

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“…Furthermore, continued improvements in modeling and estimation seem logical. Comparison of the results here to model predictive control, as articulated by Marusak in [38,39] for prediction and by Nebeluk and Ławry ńczuk for tuning in [40], seems to be very interesting for future research. In [41], Pappalardo proposes alternative uses of forward and reverse dynamics in the control, and comparison to the self-awareness feature of deterministic artificial intelligence seems to be a natural area for future improvements.…”

Section: Future Researchmentioning

confidence: 63%

Comparing Methods of DC Motor Control for UUVs

Shah

Sands

2021

Applied Sciences

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Adaptive and learning methods are proposed and compared to control DC motors actuating control surfaces of unmanned underwater vehicles. One type of adaption method referred to as model-following is based on algebraic design, and it is analyzed in conjunction with parameter estimation methods such as recursive least squares, extended least squares, and batch least squares. Another approach referred to as deterministic artificial intelligence uses the process dynamics defined by physics to control output to track a necessarily specified autonomous trajectory (sinusoidal versions implemented here). In addition, one instantiation of deterministic artificial intelligence uses 2-norm optimal feedback learning of parameters to modify the control signal, while another instantiation is presented with proportional plus derivative adaption. Model-following and deterministic artificial intelligence are simulated, and respective performance metrics for transient response and input tracking are evaluated and compared. Deterministic artificial intelligence outperformed the model-following approach in minimal peak transient value by a percent range of approximately 2–70%, but model-following achieved at least 29% less error in input tracking than deterministic artificial intelligence. This result is surprising and not in accordance with the recently published literature, and the explanation of the difference is theorized to be efficacy with discretized implementations.

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Section: Future Researchmentioning

confidence: 63%

Comparing Methods of DC Motor Control for UUVs

Shah

Sands

2021

Applied Sciences

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“…Table 2 shows the ISE calculated for different prediction horizons p G (the control horizon in all cases was equal s G = 1), together with different values of the turbine's MPC control horizon p T (wit the control horizon s T = 1). To find the minimum of the ISE, taking into account the behavior of the turbine (connected with the generator by a common shaft) and the generator, an extensive search, including 112 simulations, was performed (without any additional tuning optimizations procedures [56]). 3 and 4).…”

Section: Simulation Test Resultsmentioning

confidence: 99%

Power System Stabilizer as a Part of a Generator MPC Adaptive Predictive Control System

et al. 2021

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In this paper, a model predictive controller based on a generator model for prediction purposes is proposed to replace a standard generator controller with a stabilizer of a power system. Such a local controller utilizes an input-output model of the system taking into consideration not only a generator voltage Ug but also an additional, auxiliary signal (e.g., α, Pg, or ωg). This additional piece of information allows for taking oscillations into account that occur in the system and minimizing their impact on the overall system performance. Parameters of models used by the controller are obtained on the basis of the introduced black-box models both for a turbine and a synchronous generator, parameters of which are estimated in an on line fashion using a RLS method. The aim of this paper is to compare the behavior of the classical generator control system with a power system stabilizer and a model predictive control with an additional feedback signal. The novelty of the paper is related to the use of the predictive controller instead of the classical controller/stabilizer system and its possibility of stabilizing the power system. Contrary to the solutions found in the literature, which are commonly-based on a fuzzy logic approach, the authors propose the use of an adaptive model predictive controller, which takes advantage of the knowledge concerning the plant in the form of a model and adapts itself to the operating point of the system using the model parameters estimation mechanism. Moreover, the adaptive predictive controller, unlike other solutions, automatically adjusts signal levels to changes in the plant. The proposed solution is able to calculate the best control signal regardless of whether these changes of the plant are caused by a change in the operating point, or resulting from operation, e.g., wear of mechanical parts.

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“…One significant advantage is its ability to handle difficult-to-control, multivariable systems having to cope with different types of constraints [20]. MPC uses a time-dependent mathematical model to determine the future behavior of the system and computes the best control actions over a specified prediction horizon, also considering the operating constraints [21]. This predictive capability enables MPC to account for process dynamics, disturbances, and constraints in real time, resulting in improved control efficiency and stability in contrast to traditional control methods.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Advanced Control Strategy for Post-Combustion Carbon Capture Plant by Integrating PI and Model-Based Approaches

Ilea,

Cormos,

Cristea

et al. 2024

Energies

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Even though the energy penalties and solvent regeneration costs associated with amine-based absorption/stripping systems are important challenges, this technology remains highly recommended for post-combustion decarbonization systems given its proven capture efficacy and technical maturity. This study introduces a novel centralized and decentralized hybrid control strategy for the post-combustion carbon capture plant, aimed at mitigating main disturbances and sustaining high system performance. The strategy is rooted in a comprehensive mathematical model encompassing absorption and desorption columns, heat exchangers and a buffer tank, ensuring smooth operation and energy efficiency. The buffer tank is equipped with three control loops to finely regulate absorber inlet solvent solution parameters, preventing disturbance recirculation from the desorber. Additionally, a model-based controller, utilizing the model predictive control (MPC) algorithm, maintains a carbon capture yield of 90% and stabilizes the reboiler liquid temperature at 394.5 K by manipulating the influent flue gas to the lean solvent flowrates ratio and the heat duty of the reboiler. The hybrid MPC approach reveals efficiency in simultaneously managing targeted variables and handling complex input–output interactions. It consistently maintains the controlled variables at desired setpoints despite CO2 flue gas flow disturbances, achieving reduced settling time and low overshoot results. The hybrid control strategy, benefitting from the constraint handling ability of MPC, succeeds in keeping the carbon capture yield above the preset minimum value of 86% at all times, while the energy performance index remains below the favorable value of 3.1 MJ/kgCO2.

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Tuning of Multivariable Model Predictive Control for Industrial Tasks

Cited by 16 publications

References 39 publications

Comparing Methods of DC Motor Control for UUVs

Comparing Methods of DC Motor Control for UUVs

Power System Stabilizer as a Part of a Generator MPC Adaptive Predictive Control System

Hybrid Advanced Control Strategy for Post-Combustion Carbon Capture Plant by Integrating PI and Model-Based Approaches

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