Neural self-tuning adaptive control of non-minimum phase system

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response

Adaptive control strategies for flexible robotic arm

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity if not unstable closed-loop behavior. Therefore a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response

NIPUNA: A Novel Optimizer Activation Function for Deep Neural Networks

In recent years, various deep neural networks with different learning paradigms have been widely employed in various applications, including medical diagnosis, image analysis, self-driving vehicles and others. The activation functions employed in deep neural networks have a huge impact on the training model and the reliability of the model. The Rectified Linear Unit (ReLU) has recently emerged as the most popular and extensively utilized activation function. ReLU has some flaws, such as the fact that it is only active when the units are positive during back-propagation and zero otherwise. This causes neurons to die (dying ReLU) and a shift in bias. However, unlike ReLU activation functions, Swish activation functions do not remain stable or move in a single direction. This research proposes a new activation function named NIPUNA for deep neural networks. We test this activation by training on customized convolutional neural networks (CCNN). On benchmark datasets (Fashion MNIST images of clothes, MNIST dataset of handwritten digits), the contributions are examined and compared to various activation functions. The proposed activation function can outperform traditional activation functions

A survey on modern trainable activation functions

In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has been a renovated interest of the scientific community in investigating activation functions which can be trained during the learning process, usually referred to as "trainable", "learnable" or "adaptable" activation functions. They appear to lead to better network performance. Diverse and heterogeneous models of trainable activation function have been proposed in the literature. In this paper, we present a survey of these models. Starting from a discussion on the use of the term "activation function" in literature, we propose a taxonomy of trainable activation functions, highlight common and distinctive proprieties of recent and past models, and discuss main advantages and limitations of this type of approach. We show that many of the proposed approaches are equivalent to adding neuron layers which use fixed (non-trainable) activation functions and some simple local rule that constraints the corresponding weight layers.Comment: Published in "Neural Networks" journal (Elsevier

An application of Gaussian radial based function neural networks for the control of a nonlinear multi link robotic manipulator

The theory of Gaussian radial based function neural networks is developed along with a stable adaptive weight training law founded upon Lyapunov stability theory. This is applied to the control of a nonlinear multi-linked robotic manipulator for the general case of N links. Simulations of a two link system are performed and demonstrate the derived principles

Some aspects of traffic control and performance evaluation of ATM networks

The emerging high-speed Asynchronous Transfer Mode (ATM) networks are expected to integrate through statistical multiplexing large numbers of traffic sources having a broad range of statistical characteristics and different Quality of Service (QOS) requirements. To achieve high utilisation of network resources while maintaining the QOS, efficient traffic management strategies have to be developed. This thesis considers the problem of traffic control for ATM networks. The thesis studies the application of neural networks to various ATM traffic control issues such as feedback congestion control, traffic characterization, bandwidth estimation, and Call Admission Control (CAC). A novel adaptive congestion control approach based on a neural network that uses reinforcement learning is developed. It is shown that the neural controller is very effective in providing general QOS control. A Finite Impulse Response (FIR) neural network is proposed to adaptively predict the traffic arrival process by learning the relationship between the past and future traffic variations. On the basis of this prediction, a feedback flow control scheme at input access nodes of the network is presented. Simulation results demonstrate significant performance improvement over conventional control mechanisms. In addition, an accurate yet computationally efficient approach to effective bandwidth estimation for multiplexed connections is investigated. In this method, a feed forward neural network is employed to model the nonlinear relationship between the effective bandwidth and the traffic situations and a QOS measure. Applications of this approach to admission control, bandwidth allocation and dynamic routing are also discussed. A detailed investigation has indicated that CAC schemes based on effective bandwidth approximation can be very conservative and prevent optimal use of network resources. A modified effective bandwidth CAC approach is therefore proposed to overcome the drawback of conventional methods. Considering statistical multiplexing between traffic sources, we directly calculate the effective bandwidth of the aggregate traffic which is modelled by a two-state Markov modulated Poisson process via matching four important statistics. We use the theory of large deviations to provide a unified description of effective bandwidths for various traffic sources and the associated ATM multiplexer queueing performance approximations, illustrating their strengths and limitations. In addition, a more accurate estimation method for ATM QOS parameters based on the Bahadur-Rao theorem is proposed, which is a refinement of the original effective bandwidth approximation and can lead to higher link utilisation

FCC : controle preditivo e identificação via redes neurais

Orientadores: Ana Maria Frattini Fileti, Florival Rodrigues de CarvalhoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuimicaResumo: A unidade de Craqueamento Catalítico em Leito Fluido - FCC, modelo Kellogg Orthoflow F., representa um processo de refino de petróleo apresentando característica altamente não linear, possuindo fortes interaçães entre as variáveis de produção, e condições de operação extremamente severas. Essas unidades são constituídas basicamente de duas seções: uma de reação catalítica na qual ocorrem as reações de quebra de cadeia hidrocarbônica e também há formação de coque, desativando o catalisador; e outra seção onde ocorre a regeneração do catalisador desativado. O objetivo dessa unidade é transformar produtos de elevado peso molecu1ar, que apresentam baixo valor agregado, em compostos de elevado valor comercial. As unidades FCC, devido às condições severas de operação, necessitam de um controle rigoroso de determinadas variáveis operacionais. Apesar de existirem instalados controladores avançados baseados em modelos de convolução, fteqüentemente essas unidades são reguladas por meio de controladores PID padrões e também através de controle manual baseado no conhecimento de operadores das refinarias. O presente estudo tem como objetivo desenvolver um controlador preditivo multivariável (Multivariable Predictive Control - MPC) para ser implementado na unidade FCC, utilizando Redes Neurais Artificiais (RNA) como modelo interno do controlador. Inicialmente é previsto realizar a identificação do processo da FCC em RNA, obedecendo a seguinte estratégia: usando um modelo fenomenológico que representa a unidade industrial, e partindo de um estado inicial são aplicados diversos degraus nas variáveis manipuladas analisando as respostas nas variáveis controladas do processo. A partir destas simulações são gerados diversos conjuntos de dados divididos em grupos de treinamento, validação e teste. Diversas redes neurais do tipo multicamada feedforward são então criadas para representar o modelo fenomenológico, sendo selecionada aquela que apresenta melhor desempenho, quando comparada com o modelo. A configuração da RNA escolhida como modelo interno foi 8x15x4 (camadas de entrada, escondida e de saída, respectivamente) apresentando um erro relativo máximo de 1% quando comparado com os resultados do modelo rigoroso. Posteriormente, foi previsto desenvolver um controlador preditivo multivariável usando como modelo interno esta rede selecionada. Este controlador foi implementado dentro da rotina do modelo fenomenológico, sendo então realizados testes para verificar seu desempenho, comparando o resultado com o sistema aberto e também com o controlador DMC (Dynamic Matrix Contro!) existente. Diversos horizontes de predição e controle foram analisados, sendo selecionados aqueles que apresentaram melhor desempenho. Foi introduzido um ruído nos sinais do modelo fenomenológico para testar a robustez do controlador proposto. O controlador apresentou bom desempenho mesmo na presença de ruídos de 1,5%, levando sempre as variáveis controladas para seus valores de referência, o que comprova sua robustez. Baseados nestes resultados, conclui-se que um controlador preditivo multivariável baseado em RNA é perfeitamente capaz de controlar um sistema não linear de porte do FCC, onde elevada interação entre suas variáveis operacionais e fortes restrições estão presentes. Isto nos permite extrapolar que são boas as expectativas para uma futura utilização na unidade industrial, principalmente devido à sua simplicidade, robustez e facilidade de implementação, a despeito da dificuldade de sintonia do controladorAbstract: The Fluid Cracking Catalytic unit - FCC, Kellogg Orthotlow F. model, represents a very strong nonlinear process, with severe interactions among the process variables, and extremely severe operation conditions. The unit is composed of two sections: one is the catalytic reaction, where the hydrocarbon breaks chain reactions and coke deposition take place becoming the catalyst inactive, and the other where the catalyst regeneration happens. The objective is to transform products derived ITom petroleum, with high molecular weight and low added value, into products with higher profit. Due to the severe operation conditions, rigorous control of some variable is needed. In spite of the existence of advanced control based on a convolution model, in practice, FCC units are ftequently regulated by standard PID controllers, and also through manual control actions based on the knowledge of the refinery operators. The objective of this study is to develop a Multivariable Predictive Control (MPC) to be implemented in the FCC unit, using the Artificial Neural Networks (ANN) as internal model. Initially, the process identification in ANN of the FCC was done by the following strategy: an initial state was fust achieved using numerical simulations based on the phenomenological mo deI. Then, several steps changes were applied to the manipulated variables and the response in the controlled variables were monitored and recorded. From these simulations, several groups of data were generated for training, validation and testing. The Neural Network of multilayer feedforward type were created to represent the phenomenological model, being selected the one that better represents the phenomenological model. The ANN configuration chosen to be the internal model was 8x15x4 (Input x Hidden x Output) architecture, with a maximum relative error below 1 % when comparing the results with the phenomenological model results. Later on, it was developed a multivariable predictive control based on this internal model. This control was implemented inside the routine of the phenomenological model. The performance tests were evaluated comparing the results with the open system and with the Dynamics Matrix Control (DMC). Several prediction and control horizons were analyzed. The ANN control presented good performance even in the presence of noise of 1,5% of intensity, taking back the controlled variables to its setpoints, proving its robustness. Based on these results, a multivariable predictive control based on ANN showed be perfectly able to control a nonlinear system like a FCC unit, where high interactions among process variables, and strong restriction conditions exists. This allows us to have good expectations for a future use in the industrial unit, mainly due to its simplicity, robustness and facility ofuse, in spite ofthe difliculty oftune controlDoutoradoSistemas de Processos Quimicos e InformaticaDoutor em Engenharia Químic