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O trabalho visa apresentar uma interface que tem o objetivo de identificar a funcao de transferencia de diferentes sistemas lineares, com foco em diversas abordagens dos minimos quadrados recursivos. Para demonstrar a eficacia da interface, sao apresentadas as plantas Pendulo Amortecido e Motor Taco-Gerador, as quais foram projetadas no Instituto Federal de Educacao, Ciencia e Tecnologia do Para.

Este trabalho propoe o projeto de dois controladores preditivos, Generalized Minimum Variance Controller (GMV) e Generalized Predictive Controller (GPC) hibridizados para o controlador PID, estes sao aplicados ao conversor Boost presente em uma Nanorrede CC. O sistema e alimentado por uma String de paineis fotovoltaicos que operam em 3 formas diferentes e visam abastecer o barramento de 380 V. O objetivo e fazer o estudo comparativo para provar a eficiencia dos controladores nesta aplicacao e definir as vantagens de cada um, alem de apresentar a sintese com o controlador mais difundido no mercado, o PID. https://doi.org/10.53316/sepoc2021.055

Este artigo mostra o desenvolvimento de um carregador de celular portátil alimentado por placa solar (SUNLOAD), na forma de protótipo. Para a eficiência e validação do protótipo, são realizados estudos científicos sobre a utilização da energia solar e seu impacto no Brasil, a escolha de qual modelo de placa utilizar e o circuito elétrico mais adequado que atenda a proposta e necessidades do projeto. Os testes são realizados em simuladores e placa de ensaio, e adicionalmente um indicador de bateria é inserido no protótipo, sendo testado e montado de forma semiprofissional em uma placa de fenolite, juntamente com uma caixa personalizada impressa em uma impressora 3D.

Classical and advanced hybridization methods are employed in the control literature to calibrate the tuning parameters of internal model control (IMC) and proportional-integral-derivative (PID) controllers. This paper presents an alternative tuning design for the generalized predictive controller (GPC) that is based on both the positional process model and cost function. The proposed method involves the selection of an integral polynomial weighting filter for reference and output signals to deal with reference tracking, disturbance rejection, model-plant mismatch (MPM), minimum control energy and closed-loop robustness. The filter positional GPC (FP-GPC) obtained is transformed into a two-degree-of-freedom polynomial filtered RST structure and then into filtered internal model control (F-IMC) and PID controllers. Also, a multi-objective optimization based on genetic algorithm is applied to tune the filter parameters. Numerical and experimental essays show the effectiveness of the proposed control methodology.

The purpose of this paper is to present the linear filtered positional generalized predictive controller (GPC) synthesis using both a positional process model and cost function to ensure stability and offset-free behavior (reference tracking and disturbance rejection), which involves selecting an integral polynomial weighting filter for the setpoint and output of the process, thereby extending the applicability of the predictive controllers to different reference shapes and step disturbances for handling chemical processes. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, an implementation which involves the filter tuning parameters using a multi-objective optimization based on genetic algorithm. Numerical simulations are conducted featuring two nonlinear chemical processes models (CSTR and boiler level) to assess the efficiency, stability and robustness of different reference shapes and load disturbance rejection.

Modeling, PID controller tuning and advanced control design are necessary theoretical contents and of fundamental importance in the academic formation of electrical, chemical, automation and control engineers. Given the complexity and abstraction of control conceptions, several educational tools have been developed, with different characteristics and functionalities, trying to lessen the hardships in the teaching/learning relationship and offering students a larger set of options to understand these theoretical contents. This paper presents a new computational tool as an alternative for training, teaching and learning in First-Order Plus Dead-Time (FOPDT) plant identification and PI control tuning with Internal Model Controller (IMC), both using simplified models from the FOPDT representation. This educational software, called PIPIMC, has an interface which is pleasant, motivational and intuitive for the user, produces interesting dynamic results and was developed in Matlab® environment.

The generalized minimum variance (GMV) controller design to deal and eliminate offset can involve the selection of integral and weighting factors with respect to the controlled plant output. This introduces integral control action in a natural form and produces an implicit control design. The implementation of this idea has been explored in few nonlinear applications as well as in the GMV synthesis for the direct adaptive approach and also hybridized to tune a proportional-integral-derivative (PID) controller. The conventional way to ensure set point tracking and regulation is to use an integral control weighting or an incremental plant model. Besides these three linear GMV control designs, this paper brings numerical essays with nonlinear chemical plant models to show the efficiency, stability, and robustness to step set point changes and load disturbance rejection when moving the operating point of the plant around its nonlinear region.

This paper describes a new approach that has been applied since 2011 with the students of the Feedback Systems course to increase not only the control knowledge and approval rate but also to improve their hands-on practical skills and to prepare them for the industry. Alongside the theoretical lessons and laboratory activities, the students complete their activities with the design and implementation of a physical homemade plant where they make case studies in modeling, calibration, sensor and actuator electronic circuits, simulation, PID tuning and real-time control code under a step by step collection schedule. As result of this laboratory activity, significant progress is observed in the quality, motivation and learning (students feels so good in understanding control topics). The forth experiment of the final practical laboratory task is discussed and it is based on a PID tuning when applied to the fan-and plate, process to be built. Student evaluation is based on a questionnaire to obtain a useful measurement linked to the gain of the final performance.

In this paper we present two linear filtered positional GMV control designs using the indirect and repetitive control approaches to cope with setpoint changes and step or periodic disturbance rejection. Also, an optimization algorithm is applied to tune the filter parameters. Numerical and practical essays with nonlinear models are shown to verify the efficiency, stability and robustness when moving the operating condition of the processes.

The purpose of this paper is to present an alternative synthesis for the Generalized Predictive Controller (GPC) applied to a multiple-input multiple-output (MIMO) model, using both a positional plant model and cost function to deal with reference tracking, disturbance rejection and model-plant mismatch (MPM) with minimum control energy and closed-loop robustness. This new control design called Multivariable Filtered Positional GPC (MFP-GPC) uses an integral polynomial weighting filter for the setpoint and output of the plant, thereby extending the applicability of the predictive controllers. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, and this implementation involves the filter tuning parameters using a multi-objective optimization algorithm. Numerical simulations for a hydroelectric power and a methanol/water distillation plants show the effectiveness of the proposed control methodology.

The Model Free Learning Adaptive Control (MFLAC) is based on the pseudo-partial-derivative (PPD) calculated from the input and output signals of the system to be controlled and also using a fixed penalty factor that weights the control energy to provide a good behavior to the feedback process. This paper presents two new adaptation algorithms to adjust the PPD, based on the Sigmoid Function (SF) and another on the Newton Method (NM). With these proposals, alternative control designs are obtained for the on-line tuning of the PPD, ensuring good stability and robustness for the closed-loop system. Simulations on a nonlinear coupled liquid-tank plant and a laboratory apparatus on a damped pendulum show the efficiency and superiority of the proposed adaptive controllers when using estimation mechanisms to adjust these parameter over the standard MFLAC. Performance indices are used to show the improvement of the behavior of the proposal control algorithms.

In this paper novel tuning settings for Proportional-Integral-Derivative (PID) controllers are proposed. These tunings are based on approximations of the generalized predictive controller (GPC) resulting in pseudo predictive PID controllers. It is possible in this way to embed industrial PID controllers as predictive controllers without hardware changes being necessary. In order to obtain this pseudo predictive tuning the PID structure is matched to the GPC structure and approximations are taken in the cases where the structures are not equivalent. A first order model implies in a PI while a second order model in a PID controller designs, respectively. Depending on which tuning is used, either set-point tracking or closed-loop characteristics is favored. Future work will include results and comparisons of the proposed tuning with other methods from literature.

The purpose of this paper is first to review the standard Generalized Predictive Controller (GPC) design, second, to establish a comparative study between the GPC with a prefilter T f (q −1 ), the GPC with a parameter of the future reference trajectory and the GPC that includes a positional model but with a integral polynomial weighing factor for the error. Simulation results are shown and discussed.

The purpose of this paper is to derive an alternative tuning design from Generalized Predictive Controller (GPC) that is based on both positional plant model and cost function, involving the selection of an integral polynomial weighting filter. The obtained Filter Positional GPC can be transformed into RST structure and next to Filtered Internal Model and PID controllers. Numerical and experimental essays show the effectiveness of the control methodologies.

The paper presents a discrete design method for tuning of proportional-integral-derivative control loops by using the Smith predictor with feedback filter in order to guarantee better dynamic and target loop specifications for setpoint tracking and disturbance rejection acting on the process with long dead-time. Simulation and experimental essays show that the effectiveness of the dead-time compensating PID tuning over standard PID controller tuning methods is improved to meet the desired performance and to decrease the impact of the dead-time over the closed-loop stability.

Neste trabalho e apresentado um metodo de auto-sintonia ( self-tuning ) com caracteristicas conservativas para o GPC adaptativo, utilizando minimos quadrados recursivo para estimacao do modelo. O erro de estimacao do processo ´e utilizado para criar um fator de confianca que ajusta os parâmetros do GPC para tornar a a¸cao de controle mais conservativa frente a incertezas de processo. Para validacao deste metodo foram utilizados dois estudos de caso, o primeiro uma simulacao duma valvula de controle nao-linear e o segundo uma aplicacao real num pendulo nao-linear sub-amortecido. O metodo de auto-sintonia proposto e comparado com o GPC classico, demonstrando resultados superiores quanto a suavidade da acao de controle especialmente quanto ao tratamento de ringing.