26 research outputs found
Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey
Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area
Metaheuristic Parameter Identification of Motors Using Dynamic Response Relations
This article presents the use of the equations of the dynamic response to a step input in metaheuristic algorithm for the parametric estimation of a motor model. The model equations are analyzed, and the relations in steady-state and transient-state are used as delimiters in the search. These relations reduce the number of random parameters in algorithm search and reduce the iterations to find an acceptable result. The tests were implemented in two motors of known parameters to estimate the performance of the modifications in the algorithms. Tests were carried out with three algorithms (Gray Wolf Optimizer, Jaya Algorithm, and Cuckoo Search Algorithm) to prove that the benefits can be extended to various metaheuristics. The search parameters were also varied, and tests were developed with different iterations and populations. The results show an improvement for all the algorithms used, achieving the same error as the original method but with 10 to 50% fewer iterationsThis research received no external funding. Partial funding for open access charge: Universidad de Málag
Microprocessor based signal processing techniques for system identification and adaptive control of DC-DC converters
PhD ThesisMany industrial and consumer devices rely on switch mode power converters (SMPCs) to provide a reliable, well regulated, DC power supply. A poorly performing power supply can potentially compromise the characteristic behaviour, efficiency, and operating range of the device. To ensure accurate regulation of the SMPC, optimal control of the power converter output is required. However, SMPC uncertainties such as component variations and load changes will affect the performance of the controller. To compensate for these time varying problems, there is increasing interest in employing real-time adaptive control techniques in SMPC applications. It is important to note that many adaptive controllers constantly tune and adjust their parameters based upon on-line system identification. In the area of system identification and adaptive control, Recursive Least Square (RLS) method provide promising results in terms of fast convergence rate, small prediction error, accurate parametric estimation, and simple adaptive structure. Despite being popular, RLS methods often have limited application in low cost systems, such as SMPCs, due to the computationally heavy calculations demanding significant hardware resources which, in turn, may require a high specification microprocessor to successfully implement. For this reason, this thesis presents research into lower complexity adaptive signal processing and filtering techniques for on-line system identification and control of SMPCs systems.
The thesis presents the novel application of a Dichotomous Coordinate Descent (DCD) algorithm for the system identification of a dc-dc buck converter. Two unique applications of the DCD algorithm are proposed; system identification and self-compensation of a dc-dc SMPC. Firstly, specific attention is given to the parameter estimation of dc-dc buck SMPC. It is computationally efficient, and uses an infinite
impulse response (IIR) adaptive filter as a plant model. Importantly, the proposed method is able to identify the parameters quickly and accurately; thus offering an efficient hardware solution which is well suited to real-time applications. Secondly, new alternative adaptive schemes that do not depend entirely on estimating the plant parameters is embedded with DCD algorithm. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). Experimental and simulation results clearly show the DCD technique can be optimised to achieve comparable performance to classic RLS algorithms. However, it is computationally superior; thus making it an ideal candidate technique for low cost microprocessor based applications.Iraq Ministry of Higher Educatio
Microprocessor based signal processing techniques for system identification and adaptive control of DC-DC converters
Many industrial and consumer devices rely on switch mode power converters (SMPCs) to provide a reliable, well regulated, DC power supply. A poorly performing power supply can potentially compromise the characteristic behaviour, efficiency, and operating range of the device. To ensure accurate regulation of the SMPC, optimal control of the power converter output is required. However, SMPC uncertainties such as component variations and load changes will affect the performance of the controller. To compensate for these time varying problems, there is increasing interest in employing real-time adaptive control techniques in SMPC applications. It is important to note that many adaptive controllers constantly tune and adjust their parameters based upon on-line system identification. In the area of system identification and adaptive control, Recursive Least Square (RLS) method provide promising results in terms of fast convergence rate, small prediction error, accurate parametric estimation, and simple adaptive structure. Despite being popular, RLS methods often have limited application in low cost systems, such as SMPCs, due to the computationally heavy calculations demanding significant hardware resources which, in turn, may require a high specification microprocessor to successfully implement. For this reason, this thesis presents research into lower complexity adaptive signal processing and filtering techniques for on-line system identification and control of SMPCs systems. The thesis presents the novel application of a Dichotomous Coordinate Descent (DCD) algorithm for the system identification of a dc-dc buck converter. Two unique applications of the DCD algorithm are proposed; system identification and self-compensation of a dc-dc SMPC. Firstly, specific attention is given to the parameter estimation of dc-dc buck SMPC. It is computationally efficient, and uses an infinite impulse response (IIR) adaptive filter as a plant model. Importantly, the proposed method is able to identify the parameters quickly and accurately; thus offering an efficient hardware solution which is well suited to real-time applications. Secondly, new alternative adaptive schemes that do not depend entirely on estimating the plant parameters is embedded with DCD algorithm. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). Experimental and simulation results clearly show the DCD technique can be optimised to achieve comparable performance to classic RLS algorithms. However, it is computationally superior; thus making it an ideal candidate technique for low cost microprocessor based applications.EThOS - Electronic Theses Online ServiceIraq Ministry of Higher EducationGBUnited Kingdo
Intelligent methods for complex systems control engineering
This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems.
The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning).
Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions
Nonlinear Systems
Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems
Modelação e controlo de sistemas com incertezas baseados em lógica difusa de tipo-2
Doutoramento em Engenharia EletrotécnicaA última fronteira da Inteligência Artificial será o desenvolvimento de
um sistema computacional autónomo capaz de "rivalizar" com a capacidade
de aprendizagem e de entendimento humana. Ainda que tal
objetivo não tenha sido até hoje atingido, da sua demanda resultam
importantes contribuições para o estado-da-arte tecnológico atual. A
Lógica Difusa é uma delas que, influenciada pelos princípios fundamentais
da lógica proposicional do raciocínio humano, está na base
de alguns dos sistemas computacionais "inteligentes" mais usados da
atualidade.
A teoria da Lógica Difusa é uma ferramenta fundamental na suplantação
de algumas das limitações inerentes à representação de informação
incerta em sistemas computacionais. No entanto esta apresenta
ainda algumas lacunas, pelo que diversos melhoramentos à teoria
original têm sido introduzidos ao longo dos anos, sendo a Lógica
Difusa de Tipo-2 uma das mais recentes propostas. Os novos graus de
liberdade introduzidos por esta teoria têm-se demonstrado vantajosos,
particularmente em aplicações de modelação de sistemas não-lineares
complexos. Uma das principais vantagens prende-se com o aumento
da robustez dos modelos assim desenvolvidos comparativamente àqueles
baseados nos princípios da Lógica Difusa de Tipo-1 sem implicar
necessariamente um aumento da sua dimensão. Tal propriedade é particularmente
vantajosa considerando que muitas vezes estes modelos
são utilizados como suporte ao desenvolvimento de sistemas de controlo
que deverão ser capazes de assegurar o comportamento ótimo
de um processo em condições de operação variáveis. No entanto, o
estado-da-arte da teoria de controlo de sistemas baseada em modelos
não tem integrado todos os melhoramentos proporcionados pelo desenvolvimento
de modelos baseados nos princípios da Lógica Difusa de
Tipo-2.
Por essa razão, a presente tese propõe-se a abordar este tópico desenvolvendo
uma metodologia de síntese de Controladores Preditivos
baseados em modelos Takagi-Sugeno seguindo os princípios da Lógica
Difusa de Tipo-2. De modo a cumprir este objetivo, quatro linhas de
investigação serão debatidas neste trabalho.Primeiramente proceder-se-á ao desenvolvimento de uma metodologia
de treino de Modelos Difusos de Tipo-2 simplificada, focada em dois
paradigmas: manter a clareza dos intervalos de incerteza introduzidos
sobre um Modelo Difuso de Tipo-1; assegurar a validade dos diversos
modelos localmente lineares que constituem a estrutura Takagi-
Sugeno, de modo a torná-los adequados a métodos de síntese de controladores
baseados em modelos.
O modelo desenvolvido é tipicamente utilizado para extrapolar o comportamento
do sistema numa janela temporal futura. No entanto,
quando usados em aproximações de sistemas não lineares, os modelos
do tipo Takagi-Sugeno estabelecem um compromisso entre exatidão e
complexidade computacional. Assim, é proposta a utilização dos princípios
da Lógica Difusa de Tipo-2 para reduzir a influência dos erros de
modelação nas estimações obtidas através do ajuste dos intervalos de
incerteza dos parâmetros do modelo.
Com base na estrutura Takagi-Sugeno, um método de linearização local
de modelos não-lineares será utilizado em cada ponto de funcionamento
do sistema de modo a obter os parâmetros necessários para a
síntese de um controlador otimizado numa janela temporal futura de
acordo com os princípios da teoria de Controlo Preditivo Generalizado -
um dos algoritmos de Controlo Preditivo mais utilizado na indústria. A
qualidade da resposta do sistema em malha fechada e a sua robustez a
perturbações serão então comparadas com implementações do mesmo
algoritmo baseadas em métodos de modelação mais simples.
Para concluir, o controlador proposto será implementado num
System-on-Chip baseado no core ARM Cortex-M4. Com o propósito
de facilitar a realização de testes de implementação de algoritmos
de controlo em sistemas embutidos, será apresentada também uma
plataforma baseada numa arquitetura Processor-In-the-Loop, que permitirá
avaliar a execução do algoritmo proposto em sistemas computacionais
com recursos limitados, aferindo a existência de possíveis
limitações antes da sua aplicação em cenários reais.
A validade do novo método proposto é avaliada em dois cenários de
simulação comummente utilizados em testes de sistemas de controlo
não-lineares: no Controlo da Temperatura de uma Cuba de Fermentação
e no Controlo do Nível de Líquidos num Sistema de Tanques
Acoplados. É demonstrado que o algoritmo de controlo desenvolvido
permite uma melhoria da performance dos processos supramencionados,
particularmente em casos de mudança rápida dos regimes de funcionamento
e na presença de perturbações ao processo não medidas.The development of an autonomous system capable of matching
human knowledge and learning capabilities embedded in a compact
yet transparent way has been one of the most sought milestones of
Artificial Intelligence since the invention of the first mechanical general
purpose computers. Such accomplishment is yet to come but, in its
pursuit, important contributions to the state-of-the-art of current technology
have been made. Fuzzy Logic is one of such, supporting some
of the most used frameworks for embedding human-like knowledge in
computational systems.
The theory of Fuzzy Logic overcame some of the difficulties that the
inherent uncertainty in information representations poses to the development
of computational systems. However, it does present some
limitations so, aiming to further extend its capabilities, several improvements
over its original formalization have been proposed over the
years such as Type-2 Fuzzy Logic - one of its most recent advances.
The additional degrees of freedom of Type-2 Fuzzy Logic are showing
greater potential to supplant its original counterpart, especially in
complex non-linear modeling tasks. One of its main outcomes is its
capability of improving the developed model’s robustness without necessarily
increasing its dimensionality comparatively to a Type-1 Fuzzy
Model counterpart. Such feature is particularly advantageous if one
considers these model as a support for developing control systems capable
of maintaining a process’s optimal performance over changing
operating conditions. However, state-of-the art model-based control
theory does not seem to be taking full advantage of the improvements
achieved with the development of Type-2 Fuzzy Logic based models.
Therefore, this thesis proposes to address this problem by developing a
Model Predictive Control system supported by Interval Type-2 Takagi-
Sugeno Fuzzy Models. To accomplish this goal, four main research
directions are covered in this work.Firstly, a simpler method for training a Type-2 Takagi-Sugeno Fuzzy
Model focused on two main paradigms is proposed: maintaining a
meaningful interpretation of the uncertainty intervals embedded over
an estimated Type-1 Fuzzy Model; ensuring the validity of several locally
linear models that constitute the Takagi-Sugeno structure in order
to make them suitable for model-based control approaches.
Based on the developed model, a multi-step ahead estimation of the
process behavior is extrapolated. However, as Takagi-Sugeno Fuzzy
Models establish a trade-off between accuracy and computational complexity
when used as a non-linear process approximation, it is proposed
to apply the principles of Type-2 Fuzzy Logic to reduce the influence
of modeling uncertainties on the obtained estimations by adjusting the
model parameters’ uncertainty intervals.
Supported by the developed Type-2 Takagi-Sugeno Fuzzy Model, a
locally linear approximation of each current operation point is used to
obtain the optimal control law over a prediction horizon according to
the principles of Generalized Predictive Control - one of the most used
Model Predictive Control algorithms in Industry. The improvements in
terms of closed loop tracking performance and robustness to unmodeled
operation conditions are then assessed comparatively to Generalized
Predictive Control implementations based on simpler modeling
approaches.
Ultimately, the proposed control system is implemented in a general
purpose System-on-a-Chip based on a ARM Cortex-M4 core. A
Processor-In-the-Loop testing framework, developed to support the implementation
of control loops in embedded systems, is used to evaluate
the algorithm’s turnaround time when executed in such computationally
constrained platform, assessing its possible limitations before deployment
in real application scenarios.
The applicability of the new methods introduced in this thesis is illustrated
in two simulated processes commonly used in non-linear control
benchmarking: the Temperature Control of a Fermentation Reactor
and the Liquid Level Control of a Coupled Tanks System. It is shown
that the developed control system achieves an improved closed loop
performance of the above mentioned processes, particularly in the cases
of quick changes in the operation regime and in presence of unmeasured
external disturbances
Intelligent methods for complex systems control engineering
This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.EThOS - Electronic Theses Online ServiceBiruni Remote Sensing Centre, LibyaGBUnited Kingdo
Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures
In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools