Adaptive Model Predictive Control for Wiener Nonlinear Systems

Aliskan, Ibrahim

doi:10.1007/s40998-018-0159-0

Cited by 13 publications

(8 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, and due to the dominant nonlinear dynamics in the behavior of TGWHs, the classic design of MPC controllers is more complicated and can lead to performance drops (Aliskan, 2018). Therefore, an adaptive predictive control strategy was also taken into consideration in this study, due to the adaptive MPC provides a new linear model at each time interval, under dynamic operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Predictive control strategies for optimizing temperature stability in instantaneous hot water systems

Ehtiwesh

Quintã

Ferreira

2021

Science and Technology for the Built Environment

View full text Add to dashboard Cite

Domestic hot water production is responsible for a significant part of domestic energy consumption; instantaneous gas heating devices are widely used because they don't require reservoirs, therefore have a competitive use/consumption ratio compared to other technologies. However, users' perception of comfort is severely affected by sudden changes in temperature outside the desired temperature. The instability of the water temperature with overshoots and undershoots is the most common disadvantage, which occurs mainly due to sudden changes in the water flow requested by users and the response delays inherent to the heating system. Traditional heat cell power controllers have difficulties in responding to these problems in a timely manner, as they don't have the capacity to anticipate the effects of sudden variations in water flowrate.In this work, predictive control strategies were developed which, due to its predictive nature, allows anticipating and correcting the negative effects of sudden variations of water flowrate in the temperature. A comparative analysis of model based predictive controllers (MPCs), with and without adaptive function, with traditional controllers used in the tankless gas water heaters (TGWHs) was carried out. Tests in a simulated environment demonstrated better performances in the stabilization of temperature during sudden changes in water flowrates.

show abstract

Section: Introductionmentioning

confidence: 99%

Predictive control strategies for optimizing temperature stability in instantaneous hot water systems

Ehtiwesh

Quintã

Ferreira

2021

Science and Technology for the Built Environment

View full text Add to dashboard Cite

show abstract

“…For instance, when monitored parameters exceed corresponding thresholds, or they display some behaviors that might be considered as problematic which may hint at some issues associated with the plant in question, which at that point some other control action might be needed. These types of schemes are implemented in Model Predictive Control (MPC) algorithms [12].…”

Section: Introductionmentioning

confidence: 99%

GLSDC Based Parameter Estimation Algorithm for a PMSM Model

Sel

Kasnakoğlu

2021

Energies

View full text Add to dashboard Cite

In this study, a GLSDC (Gaussian Least Squares Differential Correction) based parameter estimation algorithm is used to identify a PMSM (Permanent Magnet Synchronous Motor) model. In this method, a nonlinear model is assumed to be the correct representation of the underlying state dynamics and the output signals are assumed to be measured in a noisy environment. Using noisy input and output signals, parameters that constitute the coefficients of the nonlinear state and input signal terms are to be estimated using the state transition matrix which is computed by the numerical means that are detailed. Since a GLSDC algorithm requires correct initial state value, this term is also estimated in addition to the unknown coefficients whose bounds are assumed to be known, which is mostly the case in the industrial applications. The batch input and output signals are used to iteratively estimate the parameter set before and after the convergence, and to recover the filtered state trajectories. A couple of different scenarios are tested by means of numerical simulations and the results are addressed. Different methods are discussed to compute better initial estimate values, to shorten the convergence time.

show abstract

“…For linear cases, since there is a structure to be exploited that results in less computational load, MPC is especially preferred. However, depending on the availability of computation power and the importance of the application, MPC is also deployed in many nonlinear systems [15,16]. Using MPC-based methods, it is also possible to address some specific controllability and observability problems for LTI or nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of an EKF-Based Parameter Estimation and a Nonlinear Optimization-Based Estimation on PMSM System Identification

Sel

Coskun

et al. 2021

Energies

View full text Add to dashboard Cite

In this study, two different parameter estimation algorithms are studied and compared. Iterated EKF and a nonlinear optimization algorithm based on on-line search methods are implemented to estimate parameters of a given permanent magnet synchronous motor whose dynamics are assumed to be known and nonlinear. In addition to parameters, initial conditions of the dynamical system are also considered to be unknown, and that comprises one of the differences of those two algorithms. The implementation of those algorithms for the problem and adaptations of the methods are detailed for some other variations of the problem that are reported in the literature. As for the computational aspect of the study, a convexity study is conducted to obtain the spherical neighborhood of the unknown terms around their correct values in the space. To obtain such a range is important to determine convexity properties of the optimization problem given in the estimation problem. In this study, an EKF-based parameter estimation algorithm and an optimization-based method are designed for a given nonlinear dynamical system. The design steps are detailed, and the efficacies and shortcomings of both algorithms are discussed regarding the numerical simulations.

show abstract

Adaptive Model Predictive Control for Wiener Nonlinear Systems

Cited by 13 publications

References 55 publications

Predictive control strategies for optimizing temperature stability in instantaneous hot water systems

Predictive control strategies for optimizing temperature stability in instantaneous hot water systems

GLSDC Based Parameter Estimation Algorithm for a PMSM Model

Comparative Study of an EKF-Based Parameter Estimation and a Nonlinear Optimization-Based Estimation on PMSM System Identification

Contact Info

Product

Resources

About