Adhemar de Barros Fontes

A new bilinear generalized predictive control approach: Algorithm and results

Abstract In this paper a new approach of bilinear predictive control is presented. The approach is based in the Bilinear Generalized Predictive Control (BGPC), strategy that uses a time-step quasi-linearised NARIMAX model. In that approach, due to the used model, a prediction error exist, which increases with the prediction horizon, degrading the performance of that controller. Thus, in the present approach, a compensated model is used, whose compensation term depends of the prediction horizon. The algorithm and the results obtained in a example simulation are shown, evidencing that a new approach presents a better performance than the controller based on the quasi-linear model.

An iterative algorithm for constrained MPC with stability of bilinear systems

This paper presents a new algorithm for model predictive control (MPC) of constrained bilinear systems using iterative compensation of the prediction error and invariant sets for constraints satisfaction and stability guarantee. In order to improve the performance of the controller, which holds prediction as its essence, an iterative process is proposed with the objective of reducing the prediction errors due to the use of a quasi-linear approximation of the bilinear model. A study of the conditions under which the prediction error converges to zero is also provided. An important outcome of this property is that feasibility and effective state constraints satisfaction along the state trajectory can be achieved. For stability guarantee, a controlled-invariant set is computed and used as terminal constraint. Then, if the initial state is admissible, the state trajectory is assured to converge to this terminal set without violating the constraints. Once inside this region, a local controller can be used to drive the state to the operation point. Numerical examples illustrate the effectiveness of the proposed algorithm regarding convergence, constraints satisfaction and stability.

Sigma-Delta Modulator with Thermoresistive Sensor Frequency Response

In our previous paper we proposed feedback architecture with thermo-resistive sensor, based on thermal sigma-delta principle to realize digital measurement of physical quantities that interacts with the sensor: temperature, thermal radiation, fluid velocity. This architecture uses 1-bit sigma-delta modulator in which considerable part of conversion functions is performed by a thermoresistive sensor. The sensor is modelled using electrical equivalence principle and the applied measure method is constant temperature. Now we present frequency response analysis and signal to noise ratio dependence on oversampling ratio for this architecture applied to thermal radiation measurement

A phase margin metric for multi-model multivariable MPC

This paper deals of a new metric for multi-model approach, based in phase margin. A quasilinear generalized predictive controller (QGPC) is designed for each chosen operating regime and the metric calculates a weighting factor for each controller. A case study with a simulated debutanizer distillation column is showed.

New Method of Closed-Loop Identification for FOPDT and SOPDT Models Considering the Padé Zero Influence

Abstract This paper presents a new closed-loop identification method for first-order and second-order plus-dead-time models, using first-order Pade approximation. In this method, the influence of the“ zero “ relative to the Pade approximation is considered in the temporal parameters transient behavior. Simulation results are presented, showing the method performance for some kinds of systems. An experimental platform with a heat sink bar was used to apply this identification method.

Bilinear Compensated Predictive Controller: A state variables approach

Nonlinear Predictive Control has been subject of many researches in recent decades. Bilinear models have been an alternative to represent process nonlinearities because they are simpler than the nonlinear models in general and satisfactorily represent many types of nonlinearities. This paper presents a state variables approach of bilinear predictive control. This approach uses a compensated state variables model obtained from the compensated polynomial model, which uses a quasilinear model with the addition of compensations terms to the predictor model. These terms are different for each prediction horizon. A simulated example shows an improvement in the control performance by using this method, when it is compared with the “time-step quasilinear predictive controller”.

A new algorithm for bilinear predictive control

This paper presents a new algorithm for bilinear predictive control based on state variables. This algorithm uses a time-step quasilinear model and adds compensations terms to the predictor model, which are different for each prediction horizon. A simulated example shows an improvement in the control performance by using this method, when it is compared with the “time-step quasilinear controller”.

An Adaptive PI Nonlinear Controller Applied to Slug Control Problem in Procedures for Petroleum Exploration and Production

Abstract This paper presents a new technique for the regulatory nonlinear control applied to the slug control, which consists of abrupt variation on the flow of gas and liquid in vessels used on the extraction and production units of the oil & gas industry. The slugs are frequent phenomenon in these processes and are caused due to the characteristics of the flow in the risers between the well and the production platforms. Depending on the magnitude of the phenomenon, serious damage may occur, as flood in the separators, instability in the forward process or even non-programmed stop in the production of the platform. A PI nonlinear controller was developed with adaptive gain depending on the data level and its dynamics, regardless of the size of the slug, since it is not known. The purpose is to reject the slugs without violating the process constraints, stabilize the system as a whole and increase safety and operational reliability, optimizing the production. The developed technique have the purpose of absorbing as much as possible the slugs by manipulating the output flow with the minimum floating on the process variable in the vessel, keeping it inside the operational limits.

Multi-Model Approaches for Bilinear Predictive Control

It is well known that linear controllers can exhibit serious performance limitations when applied to nonlinear systems since nominal linear models used during design cannot represent the nonlinear plant in its whole operating range (Arslan et al., 2004). For this reason, several researches has been proposed new techniques in order to supply a solution for this problem. The main alternative technique, proposed by academy, to resolve the referred problem is known as multi-model approach. The basic idea of multi-model approach consists in decompose the system’s operating range into a number of operating regimes that completely cover the chosen trajectory as showed in (Foss et al., 1995). There are, basically, two approaches for multi-model. The first one consists of to design a set of suitable controllers (one for each operating regime) and to calculate weighting factors to them as showed in (Arslan et al., 2004) and (Cavalcanti et al., 2007a). The global control signal is a weighting sum of the contributions of each controller. The second one consists of to build a global model as a weighting sum of each local model as showed in (Foss et al., 1995) and (Cavalcanti et al., 2007b). In both cases, a way to measure distances between models is defined. Multivariable Model Predictive Control (MMPC) has been presented in this chapter. MPC is the an of the most important control technique used in industry. Multivariable Bilinear Generalized Predictive Control (MBGPC) is formulated and, its alternative solution, Multivariable Bilinear Generalized Predictive Control with Iterative Compensation (MBGPCIC) is presented. This chapter shows either proposed metrics in order to build multi-model based controllers (based in MBGPC and MBGPCIC) and presents simulation results applied in distillation columns.

A New Nonlinear Predictive Control Approach Using Hammerstein Models with Compensation Term

In this paper is presented a contribution for development and implementation of nonlinear predictive control based on Hammerstein models as well as to make properties evaluation. In this work, nonlinear predictive control development has been used the time-step linearity method and a compensation term is used with an objective to make better the controller performance. An example demonstrating the viability of the proposed methodology is presented.