Design Of Predictive Functional Control Research Articles

Generalized fuzzy cascade predictive functional control design for different types of challenging dynamics systems

This paper introduces a generalized form of cascade predictive functional control. The inner loop includes a state feedback controller, which aims to increase damping and stabilize the system. The outer loop utilizes an output feedback controller (OFC) to track reference and reject disturbance. The gains of the proposed controller structure are obtained optimally concerning to a specified prediction horizon through the predictive functional controller (PFC) method, which is improved by a fuzzy logic controller (FLC). The designed FLC plays a trade-off role between inner loop and outer loop gains values by tuning weighting factors in the PFC approach. In this paper, the proposed solution is formulated to convert the tracking problem into a regulation problem which results in complexity reduction and lower computational cost. Moreover, based on the proposed solution, a generalized form of OFC is obtained concerning to disturbance and reference characteristics. As a comparative study, the proposed controller is applied to the different types of challenging dynamics systems and compared to the discrete-time linear quadratic regulator (DLQR) control technique to reveal the benefits and reliable performance of the proposed control structure.

Multimodel Fractional Predictive Functional Control Design with Application on an Industrial Heating Furnace

This paper mainly targets the nonlinear characteristics in industrial heating furnace control and uses the multimodel method to decompose the global dynamics of the process into a series of local model sets. The corresponding predictive functional controller is designed using the local fractional order models of the set within their working range. The weight coefficients of the different models in the model sets are obtained based on the error of the different model sets at current moment, and the system performance at current moment is obtained by weighting between many models using the weight coefficient. Since the accuracy of the local models is increased, the error between the local model set and the process output is reduced, which decreases the influence of the system model inaccuracy on the system performance. Finally, the strategy is verified through the experiment on the temperature of a heating furnace.

Design and Implementation of Hybrid Modeling and PFC for Oxygen Content Regulation in a Coke Furnace

This paper proposes the implementation of hybrid data driven modeling and predictive functional control (PFC) strategy for regulation of oxygen in an industrial coke furnace. A comprehensive model that incorporates simple step-response test and nonlinear optimization using neural network is first developed through process operation data. Then, a nonlinear PFC is designed to improve the dynamic response and steady operation. The proposed PFC overcomes the disadvantages of proportional-integral-derivative or linear advance control strategies because the developed process model yields better process dynamics prediction and facilitates the subsequent PFC controller to improve process operation. In addition, the linear iterative form of the controller design is implemented such that engineers can easily apply it to industrial processes. Results are shown by way of simulations and experimental tests to demonstrate the effectiveness of the proposed strategy.

Commissioning procedure for predictive control based on ARX models of Type 1 Diabetes Mellitus patients

This work presents a systematic way to assist on the commissioning of the Predictive Functional Control (PFC) algorithm to be implemented in the context of the Artificial Pancreas (AP) for patients with Type 1 Diabetes Mellitus (T1DM). PFC has demonstrated its high potentiality in process industry since several decades ago. Its use for AP has been specifically adapted and tested in the well validated model of the endocrine system, known as the Uva/Padova model. Firstly, the tests were done in silico using the parameters of 30 patients: 10 children, 10 adolescents and 10 adults. The performance of the controller was evaluated by means of the Control Variability Grid Analysis (CVGA) giving excellent results. We propose here the use of data driven models, as a second step, to perform a PFC final tuning to be practically ready to use in AP implementations. The closed loop responses are tested through dynamic simulations with the obtained models when different meals intake and insulin dosages are given to the patient Nº 5004 from the Center of Diabetes Technology (UVa/USA). In this paper an ARX model is build based on the patient’s own historical data recorded during 27 days. The ARX model was used giving good results at 30 min. prediction such as 89.1% of FIT, RMSE=10.312 mg/dl, and the Clarke Grid 97.58% at Zone A and 2.42% at B, while for 60 min. prediction, the FIT was 65.5%, RMSE 25.945 mg/dl and the Clarke Grid 88.93% at Zone A and 11.07% at B. The simplified models used in the PFC structure were obtained from the ARX model to isolate the insulin impact from the carbohydrates effects on the blood glucose variations. Finally, the full closed loop simulations, taking into account the constraints related to the insulin pump, were performed to conclude the PFC design. Closing the work, comparisons between the blood glucose behavior without control and with PFC and PID implementations are given to drive the final conclusions and discuss future works.

A linear quadratic structure based predictive functional control design for industrial processes against partial actuator failures

An improved predictive functional control (PFC) scheme is proposed for linear processes to compensate for partial actuator failures. First, a new state space model based on the process state and output error variables is derived to provide more information of the system. The PFC is then designed through a linear quadratic form that makes the controller optimal. Thus, the control performance can be improved under partial actuator faults and unknown disturbances. Finally, an injection molding process is adopted to illustrate the feasibility and effectiveness of the proposed scheme. Simulation results show that satisfactory control performance can be obtained by comparing with traditional PFC.

A non-minimal state space formulation based predictive functional control design for inverse-response processes

In this paper, the design of predictive functional control (PFC) based on non-minimal state space models is proposed for nonminimum phase processes referred to as inverse-response processes. The merits of the proposed method lie firstly in the fact that the measured input and output variables and their past values are chosen as the state variables that avoid the need of state observers. Secondly, by using the formulation of a new state space variable, the resulting PFC control incorporates both the state and output feedback, which further improve closed-loop process responses over previous methods. The performance and robustness of the control system are discussed. A typical example of inverse-response process is given to compare the proposed controller and the previous.

Improved control using PFC and modified linear quadratic regulator for a kind of nonlinear systems

This paper describes the combination design of predictive functional control (PFC) and optimal linear quadratic (LQ) method for a kind of nonlinear process with output feedback coupling. In many existing control methods for this kind of nonlinear systems, the nonlinear part is either ignored or represented as a rough linear one when corresponding predictive control methods are designed. However, by assuming that the nonlinearity can be ignored or simplified to a linear time-varying part may not lead to the good control performance of subsequent linear control designs. The paper is a further investigation on this kind of systems, in which a procedure of PFC plus a modified optimal LQ control is developed. With respect to the proposed control strategy and the corresponding processes, the closed-loop performance is improved concerning tracking ability and disturbance rejection compared with previous predictive control methods. In addition, the proposed control is easy to implement as it selects a simple structure and a modification of the classical control scheme.

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method.

Support vector machine based predictive functional control design for output temperature of coking furnace

A new support vector machine based nonlinear predictive functional control design method has been developed and applied to an industrial coking furnace, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection compared with traditional PFC and PID control strategies. The nonlinear process is first treated into a linear part plus a nonlinear part, then a convergent overall linear predictive functional control law is designed. The method gives a direct and effective multi-step predicting method and uses linear methods to get the control law which avoids the complicated nonlinear optimization. Comparison results and application to the temperature control of the industrial heavy oil coking furnace are presented in the article showing the efficiency of the method.