Assessment of predictive control performance using fractal measures

Domański, Paweł D.; Ławryńczuk, Maciej

doi:10.1007/s11071-017-3484-3

Cited by 20 publications

(6 citation statements)

References 45 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Esta cantidad invariante intrínseca del sistema tiene una relevancia singular para la caracterización de la previsibilidad. Los valores más altos de h implican valores más bajos de la predictibilidad 1/h, es decir, cuanto más caótico, menos predecible (Domański & Ławryńczuk, 2017).…”

Section: Exponentes De Lyapunovunclassified

Entrenamiento de redes neuronales recurrentes usando la dimensión de encaje en el espacio de las fases

2022

View full text Add to dashboard Cite

Se propone un método usando Redes Neuronales Recurrentes para la predicción de datos caóticos, aplicando la Teoría del Caos, para estudiar el comportamiento dinámico de los datos en el espacio multidimensional de las fases, establecer la correlación de los mismos y determinar la dimensión de encaje como base para el entrenamiento de las redes neuronales, así como determinar las características dinámicas del sistema calculando los coeficientes de Lyapunov y la entropía de Kolmorov-Siani, que nos indican el grado de desorden que tiene el sistema, para proyectar la precisión de la predicción. Se usan datos de contaminantes PM2.5 tomados en el centro Histórico de la ciudad de Quito, en intervalos de una hora, entre los años 2005 a 2019. Los resultados determinan que las series de datos corresponden a un sistema caótico (más de un coeficiente positivo de Lyapunov), por lo que se justifica la aplicación de la Teoría del Caos en el análisis de los mismos, dando buenos resultados en las predicciones aplicando los métodos de redes neuronales recurrentes de Elman y Jordan, al comparar las series predichas se demuestran que no presentan diferencias significativas entre ellas, ni con los datos medidos, usando el método de varianza con 0,05 de significancia, el error cuadrático porcentual respecto al rango de variación de los datos es aproximadamente del 5 % en ambos casos. Objetivos: Proponer un método que ayude al entrenamiento de las redes neuronales usando la Teoría del caos, mediante la implementación de la dimensión de encaje en el espacio de las fases.

show abstract

Section: Exponentes De Lyapunovunclassified

Entrenamiento de redes neuronales recurrentes usando la dimensión de encaje en el espacio de las fases

2022

View full text Add to dashboard Cite

show abstract

“…Non-Gaussian statistical [157] and fractal [158] methodologies have been investigated for the GPC predictive control algorithm. Linear [159] and nonlinear [160] DMC predictive control have been assessed using integral, statistical, information, and fractal measures.…”

Section: Data-driven Approachesmentioning

confidence: 99%

Performance Assessment of Predictive Control—A Survey

Domański

2020

Algorithms

Self Cite

View full text Add to dashboard Cite

Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology. More and more advanced predictive structures have been implemented. The first applications were in chemical engineering, and now Model Predictive Control can be found in almost all kinds of applications, from the process industry to embedded control systems or for autonomous objects. Currently, each implementation of a control system requires strict financial justification. Application engineers need tools to measure and quantify the quality of the control and the potential for improvement that may be achieved by retrofitting control systems. Furthermore, a successful implementation of predictive control must conform to prior estimations not only during commissioning, but also during regular daily operations. The system must sustain the quality of control performance. The assessment of Model Predictive Control requires a suitable, often specific, methodology and comparative indicators. These demands establish the rationale of this survey. Therefore, the paper collects and summarizes control performance assessment methods specifically designed for and utilized in predictive control. These observations present the picture of the assessment technology. Further generalization leads to the formulation of a control assessment procedure to support control application engineers.

show abstract

“…It has been shown that even simple linear MPC configuration requires alternative CPA approach, such as fractal [48] or non-Gaussian [6]. Nonlinear industrial control generates even more serious challenges for reliable MPC monitoring.…”

Section: Introductionmentioning

confidence: 99%

Control Quality Assessment for Processes With Asymmetric Properties and its Application to pH Reactor

Domański

Ławryńczuk

2020

IEEE Access

View full text Add to dashboard Cite

The majority of processes in chemical industry is nonlinear. However we often take advantage of linear approximation and analysis as the useful simplification. Nonetheless, one has to remember that the reality is often complex, nonlinear and full of unknown unknowns. One of the forgotten aspects in control engineering is connected with the symmetricity. Asymmetric properties appear, when the process or instrumentation introduces nonlinearities. Control systems are then exposed to the asymmetrical behavior and should properly react, while their performance measures have to take them into account. This paper proposes robust control performance indexes in form of the M-estimator using logistic ψ function denoted σ H and α-stable distribution scale factor γ. Additionally, their application procedure in industrial chemical engineering environment is proposed. The approach is illustrated with an example of the pH neutralization process. INDEX TERMS Asymmetry, control performance assessment, fat-tails, MPC, pH neutralization.

show abstract

Assessment of predictive control performance using fractal measures

Cited by 20 publications

References 45 publications

Entrenamiento de redes neuronales recurrentes usando la dimensión de encaje en el espacio de las fases

Entrenamiento de redes neuronales recurrentes usando la dimensión de encaje en el espacio de las fases

Performance Assessment of Predictive Control—A Survey

Control Quality Assessment for Processes With Asymmetric Properties and its Application to pH Reactor

Contact Info

Product

Resources

About