Gustavo H. C. Oliveira

Brief Constrained robust predictive controller for uncertain processes modeled by orthonormal series functions

The present work focuses on robust predictive control (RPC) of uncertain processes and proposes a new approach based on orthonormal series function modeling. In such unstructured modeling, the output signal is described as a weighted sum of orthonormal functions that uses approximative information about the time constant of the process. Due to an efficient uncertainty representation, this kind of modeling is advantageous in the RPC context, even for constrained systems and processes with integral action. The stability of the closed-loop system is guaranteed by the setting of sufficient conditions for the selection of the controller prediction horizon. Simulation results are presented to illustrate the performance of this new RPC algorithm.

Fuzzy models within orthonormal basis function framework

Presents a framework for fuzzy modeling of dynamic systems using orthonormal basis functions in the representation of the model input signals. The main objective of using orthonormal bases is to overcome the task of estimating the order and time delay of the process. The result is a nonlinear moving average fuzzy model which, consequently, has no feedback of prediction errors. Although any technique of fuzzy modeling can be used in the proposed framework, a relational approach is considered. The performance of fuzzy models with orthonormal basis functions is illustrated by examples and the results are compared with those provided by conventional fuzzy models and Volterra models.

PMV-Based Predictive Algorithms for Controlling Thermal Comfort in Building Plants

The present paper is focused on thermal comfort control for building occupants. Thermal comfort is addressed here by the use of PMV index for such measurement. Based on PMV, two predictive strategies characterized by having terminal constraints are proposed and compared. The first is based on generating a temperature set-point signal that optimizes the building (single zone) internal PMV value. The second includes the PMV model in the controller prediction computations, generating a non-linear PMV model having Wiener structure. In both cases, the linear part of the model is built by using Laguerre basis. Simulation results, conducted with actual climate data, illustrate the performance of the thermal comfort control algorithms.

K-Bug, A New Bug Approach for Mobile Robot's Path Planning

This work presents the K-Bug algorithm, a new method for path planning of mobile robots belonging to the Bug family. The main idea of the algorithm may be used to improve the performance of existing methods of path planning that use local information, or as an entirely new method, if global information is available. Its also presented a short comparison of the methods found in literature, proving its efficiency, low computational cost and high robustness, even in complex environments.

An introduction to models based on Laguerre, Kautz and other related orthonormal functions – part I: linear and uncertain models

This paper provides an overview of system identification using orthonormal basis function models, such as those based on Laguerre, Kautz, and generalised orthonormal basis functions. The paper is separated in two parts. In this first part, the mathematical foundations of these models as well as their advantages and limitations are discussed within the context of linear and robust system identification. The second part approaches the issues related with non-linear models. The discussions comprise a broad bibliographical survey of the subjects involving linear models within the orthonormal basis functions framework. Theoretical and practical issues regarding the identification of these models are presented and illustrated by means of a case study involving a polymerisation process.

THERMAL COMFORT BASED PREDICTIVE CONTROLLERS FOR BUILDING HEATING SYSTEMS

Abstract This work is focused on indoor thermal comfort control problem in buildings equipped with HVAC (Heating Ventilation and Air Conditioning) systems. The occupants thermal comfort is addressed here by a comfort zone in the psychometric chart and the PMV (Predict Mean Vote) index. In this context, three control algorithms are proposed by using only-one-actuator system associated to a heating equipment. The methods are based on the model predictive control scheme and on the improvement of indices related to occupants thermal comfort sensation. Simulation results – obtained by using the weather data file for the city of Curitiba, Brazil – are presented to validate the proposed methodology in terms of room air temperature, relative humidity and PMV control.

CRHPC using Volterra models and orthonormal basis functions: an application to CSTR plants

This work focuses on predictive control of nonlinear systems modelled by Volterra series with orthonormal basis functions expansion (Volterra-OBF). By using Volterra series, any nonlinear analytical operator with finite memory can be approximated with an arbitrary precision. A Laguerre basis expansion is then used in the model parameterization to reduce the number of coefficients of the Volterra model. In this context, a predictive control with terminal constraints window based on cited model is proposed. Finally, simulated results using a CSTR unit illustrates the control scheme performance.

An introduction to models based on Laguerre, Kautz and other related orthonormal functions - Part II: non-linear models

This paper provides an overview of system identification using orthonormal basis function models, such as those based on Laguerre, Kautz, and generalised orthonormal basis functions. The paper is separated in two parts. The first part of the paper approached issues related with linear models and models with uncertain parameters. Now, the mathematical foundations as well as their advantages and limitations are discussed within the contexts of non-linear system identification. The discussions comprise a broad bibliographical survey of the subject and a comparative analysis involving some specific model realisations, namely, Volterra, fuzzy, and neural models within the orthonormal basis functions framework. Theoretical and practical issues regarding the identification of these non-linear models are presented and illustrated by means of two case studies.

Model Predictive Control based on LMIs Applied to an Omni-Directional Mobile Robot

Abstract The paper presents a methodology for state feedback MPC synthesis applied to the trajectory tracking control problem of a three wheeled omnidirectional mobile robot. The MPC design used here is based on a cost function developed over finite horizon and LMI framework. It is shown that MPC concepts well established for robot applications, for instance, the use of open loop predictions, receding horizon control, constraints manipulation, are preserved in this new formulation. The stability of the closed loop system is guaranteed by LMI conditions related with the cost function monotonicity. Simulation results of navigation are provided to demonstrate the performance of the proposed control strategy.

Simulation and Analysis of a Secondary HVAC System Using MATLAB/SIMULINK Platform

In this paper, the mathematical model of a secondary system (fan-coil) of a HVAC equipment is described. This system was inserted into a computer code developed in Matlab/Simulink platform devoted to the analysis of buildings hygrothermal behavior and performance of closed-loop control systems. The model is presented in terms of state-space variables that represent the energy and mass balance for each component of the fan-coil. Results are presented in terms of a comparative analysis of the cooling coil temperature and external air ventilation rate effects on the room air psychrometrics state. Finally, the control system performance is presented for both temperature and relative humidity.Copyright