Predictive Functional Control Algorithm Research Articles

Predictive functional control for separation processes by liquid-liquid extraction

A separation process by liquid-liquid extraction is a well-known and widespread industrial technology implemented to quantitatively recover valuable chemical elements. In the nuclear industry, such processes have been used for decades to recover uranium and plutonium from spent fuel. The process is non-linear and time constants vary over a wide range. Former studies on a simplified model showed linear controllers such as PID were not adapted to regulate these separation processes. The objective of this study is to propose process monitoring by using available physical models within the PAREX code and to validate the feasibility to monitor a separation process by using directly the PAREX code as a black box. The Predictive Functional Control (PFC) command law manages to monitor non-linear separation processes by liquid-liquid extraction, when using an existing physical model implemented in the PAREX code. An online alignment of the model on process values is necessary to keep the model sufficiently representative to predict the future behaviour of the process. As a reference benchmark, the PID control loop is also simulated with the physical model. The PFC and PID regulations are compared to evaluate the gain of using physical models implemented in the PAREX code. A simulation tool has been developed to implement the PID and Predictive Functional Control (PFC) controllers for separation processes by liquid-liquid extraction. The PFC command law manages to monitor non-linear separation processes, when using a physical model connected to the PAREX code. Even if the PID controller may be locally more efficient, the great strength of the PFC controller is to enable good performances on wider operating conditions, with an easier parametrization. The PFC algorithm is a mean to deal with the process characteristic features, like non-linearity and time constant change. The PFC controller appears to be a good candidate for experimental tests. A mid-term objective is to include the state estimator tool in the control loop to consolidate the controlled variable measurements. These developments may be regarded as an add-on module in a digital factory concept. Results shown in this article are only from simulation. For the sake of data confidentiality, studies with the PAREX code cannot be published and numerical parameters of the process are normalized. These simulations will be validated during further experimental tests.

Application of Feedforward Cascade Compound Control Based on Improved Predictive Functional Control in Heat Exchanger Outlet Temperature System

Aiming at the problems of large delay and poor anti-disturbance ability in the outlet temperature control system of the heat exchanger to optimize the control accuracy of the system and improve the control performance, this paper proposes a control scheme combining predictive functional control with proportional-integral-derivative control. Using the incremental proportional-integral-derivative control algorithm to improve the optimization objective function of the predictive functional control algorithm, a predictive functional control optimization model with a proportional-integral-derivative structure is established. The feedforward compensation control is adopted to eliminate the influence of external disturbances on the heat exchanger temperature control system. Through simulation, the proposed control scheme is compared with the feedforward cascade compound control scheme based on a proportional-integral-derivative main controller. The results show that the scheme has a small over harmonic and strong anti-interference ability. The adaptability and stability of the system are significantly improved, and the exit temperature of the heat exchanger can be effectively controlled.

Indoor temperature regulation and energy consumption inside a working office in building system using a predictive functional control

ABSTRACT This paper presents a novel control approach for indoor temperature regulation in tertiary buildings, based on predictive functional control. The main control objective is to reduce the energy consumption while ensuring the desired thermal comfort of the occupants. The novelty of this approach may perform in the presence of disturbances such occupancy profile, electrical equipment, indoor temperature, surrounding temperatures and weather data. Predictive functional control algorithm consists in using a dynamic model of the process inside the controller in real time in order to anticipate the future behavior of the system. The proposed method is successfully illustrated using an example of temperature control of a working office which is implemented in the SIMBAD toolbox (SIMulator of Buildings and Devices) integrated into the Matlab/Simulink environment. The proposed control compromises a class of control algorithms that utilizes an online process model to optimize the future response of a plant. The efficiency of the proposed control has been validated using good performance indexes for various scenarios. Simulation results revealed a good level of comfort is ensured with a minimum energy (3.2015 kWh), temperature regulation with a very low error, a high level of comfort is generated in the office room during working hours. Thus, demonstrating the validity and effectiveness of this control strategy.

Modular substation intelligent control cabinet temperature control system

When the temperature control system of intelligent control cabinet of modular substation is designed by the current method, the PID controller is not improved, and there are problems such as low temperature prediction accuracy, low temperature tracking accuracy and poor control effect. The design method of temperature control system of intelligent control cabinet of modular substation is proposed. Through FPGA, input circuit, output circuit, man-machine interface, frequency division module five parts constitute the modular substation intelligent control cabinet temperature control system framework, in the temperature control system combined with the predictive function control algorithm to achieve the modular substation intelligent control cabinet temperature control system software system design. The experimental results show that the designed system can control the terminal temperature stably and reduce the temperature fluctuation to a small extent.

Predictive functional control for integrator systems

This paper presents a novel modification and insights in the topic of predictive functional control (PFC). Of particular interest is the consistency between the predictions deployed by PFC and the desired closed-loop behaviour. This paper focuses on integrator systems which pose some challenges to a conventional PFC algorithm and compares and contrasts two simple alternative but very effective modifications (one of which is novel) which enable better consistency of predictions and thus enable easier tuning and more reliable closed-loop behaviour. These insights are used to give some conclusions and proposals for how more challenging dynamics might be handled.

Rail pressure controller design of GDI basing on predictive functional control

Gasoline direct injection (GDI) is a pivotal technique for a highly efficient engine. However, how to maintain a stable rail pressure which offers good fuel economy and low emissions, is still a challengeable work. In this paper, a rail pressure controller is designed basing on predictive functional control (PFC), a model predictive control (MPC) method, to surmount the nonlinearity and discontinuity brought by the common rail pressure system (CRPS). A control-oriented piecewise linear model is presented to simplify the CRPS. The simulation results on a benchmark show that rail pressure tracks the setpoint accurately even with some perturbations. Profiting from the conciseness of PFC algorithm, the controller can compute the online solution in a short time, which makes it possible to realize the strategy on a fast response system.

Using Laguerre functions to improve the tuning and performance of predictive functional control

This paper proposes a novel modification to the predictive functional control (PFC) algorithm to facilitate significant improvements in the tuning efficacy. The core concept is the use of an alternative parameterisation of the degrees of freedom in the PFC law. Building on recent insights into the potential of Laguerre functions in traditional MPC (Rossiter, Wang, & Valencia-Palomo, 2010; Wang, 2009), this paper develops an appropriate framework for PFC and then demonstrates that these functions can be exploited to allow easier and more effective tuning in PFC as well as facilitating strong constraint handling properties. The proposed design approach and the associated tuning methodology are developed and their efficacy is demonstrated with a number of numerical examples.

Comparison of PFC Controllers for Heating Process

The paper presents research with Predictive Functional Control (PFC) for fluid heating process. Two types of models are proposed and used as internal models for PFC algorithm. The first one includes all nonlinearities that are captured in the process, while the second one includes additionally time varying dead time. Both models were calibrated and verified using experimental data. The paper compares performance of two PFC versions based on mentioned models to indicate the profit of including dead time in model based predictive (MPC) control. Experimental results indicate that including dead time in controller’s internal model result in better performance. Although including varying dead time in controller requires extra programming effort and implementation considerations. All identification and control experiments, which are presented in the paper, were made using experimental installation equipped with industrial control equipment.

Active disturbance rejected predictive functional control for space vehicles with RCS

Reaction control system (RCS) is a powerful and efficient actuator for space vehicles attitude control, which is typically characterized as a pulsed unilateral effector only with two states (off/on). Along with inevitable internal uncertainties and external disturbances in practice, this inherent nonlinear character always hinders space vehicles autopilot from pursuing precise tracking performance. Compared to most of pre-existing methodologies that passively suppress the uncertainties and disturbances, a design based on predictive functional control (PFC) and generalized extended state observer (GESO) is firstly proposed for three-axis RCS control system to actively reject that with no requirement for additional fuel consumption. To obtain a high fidelity predictive model on which the performance of PFC greatly depends, the nonlinear coupling multiple-input multiple-output (MIMO) flight dynamics model is parameterized as a state-dependent coefficient form. And based on that, a MIMO PFC algorithm in state space domain for a plant of arbitrary orders is deduced in this paper. The internal uncertainties and external disturbances are lumped as a total disturbance, which is estimated and cancelled timely to further enhance the robustness. The continuous control command synthesised by above controller-rejector tandem is finally modulated by pulse width pulse frequency modulator (PWPF) to on-off signals to meet RCS requirement. The robustness and feasibility of the proposed design are validated by a series of performance comparison simulations with some prominent methods in the presence of significant perturbations and disturbances, as well as measurement noise.

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.

Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace

An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers.

Design of fractional order predictive functional control for fractional industrial processes

Many phenomena in practical processes cannot be accurately described by conventional differential equations, while fractional order differential equations can describe the characteristics of such processes more accurately. In this paper, the fractional order predictive functional control (FPFC) method is designed for a class of single-input single-output (SISO) fractional order linear systems. The Oustaloup approximation is employed to derive the approximate model of fractional order system. Meanwhile, the Grünwald–Letnikov (GL) definition and the fractional calculus operator are used in its cost function, which further extend the applications of fractional order calculus to the predictive functional control algorithm. And then the optimal control is obtained. Compared with traditional predictive functional control based on integer reduced order model, simulation results reveal that the fractional order controller yields improved control performance.

Incremental multivariable predictive functional control and its application in a gas fractionation unit

The control of gas fractionation unit (GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay. PID controllers are still applied in most industry processes. However, the traditional PID control has been proven not sufficient and capable for this particular petro-chemical process. In this work, an incremental multivariable predictive functional control (IMPFC) algorithm was proposed with less online computation, great precision and fast response. An incremental transfer function matrix model was set up through the step-response data, and predictive outputs were deduced with the theory of single-value optimization. The results show that the method can optimize the incremental control variable and reject the constraint of the incremental control variable with the positional predictive functional control algorithm, and thereby making the control variable smoother. The predictive output error and future set-point were approximated by a polynomial, which can overcome the problem under the model mismatch and make the predictive outputs track the reference trajectory. Then, the design of incremental multivariable predictive functional control was studied. Simulation and application results show that the proposed control strategy is effective and feasible to improve control performance and robustness of process.

Application of Two-Dimensional Predictive Functional Control in Injection Molding

A batch process can be viewed as a two-dimensional (2D) system with dynamics in both time and batch directions. To tackle these 2D dynamics, many control algorithms have been proposed by combining iterative learning control (ILC) with high-performance continuous control algorithms under a 2D framework. However, these algorithms require large computational load and memory, which are often demanding for the software and hardware of the controllers in several fast batch processes. In this paper, a 2D predictive functional control algorithm that combines ILC with predictive functional control (PFC) is proposed. Owing to the compactness and effectiveness of PFC, the proposed control algorithm can reduce the required computational load and memory. Other than the general form of the control law, two concise and practical forms are provided as well. The proposed control scheme is tested through simulations and implementation in an injection molding process, which is a typical fast batch process. Results confirm t...

State space predictive functional control optimization based new PID design for multivariable processes

Proportional-integral-derivative (PID) controller encounters challenge when dealing with the multiple input and multiple output (MIMO) industrial processes, because the coupling, time delay and other characteristics make the design of PID controllers complicated. A novel PID controller based on extended non-minimal state space predictive functional control (ENMSSPFC) is proposed to control the chamber pressure of an industrial furnace where the process is a typical MIMO process. ENMSSPFC is an improved predictive functional control (PFC) algorithm in which the state variables and the tracking error are combined and regulated separately, so that more freedom can be offered in the adjustment and better performance can be provided. The new PID controller inherits the simple structure and the improved performance of ENMSSPFC, and two case studies of pressure regulation in the industrial chamber and Vinante and Luyben (VL) column system are introduced to show the effectiveness of proposed method.

Nonlinear Adaptive Predictive Functional Control Based on the Takagi–Sugeno Model for Average Cracking Outlet Temperature of the Ethylene Cracking Furnace

The conventional PID control has been proven insufficient and incapable for this particular petro-chemical process. This paper proposes a nonlinear adaptive predictive functional control (NAPFC) algorithm based on the Takagi–Sugeno (T-S) model for average cracking outlet temperature (ACOT) of the ethylene cracking furnace. In this algorithm, in order to overcome the effect on system performance under model mismatch, the structure parameters of the T-S fuzzy model are confirmed, and the model consequent parameters are identified online using the forgetting factor least-square method. Prediction output is calculated according to the identified parameters instead of computing the Diophantine equation, thereby obtaining directly the predictive control law and avoiding the complex computation of the inverse of the matrix. Application results on ACOT of the ethylene cracking furnace show the proposed control strategy has strong tracking ability and robustness.

Multi‐model direct generalised predictive control for automatic train operation system

The authors propose a novel multi-model direct generalised predictive control based on predictive function control (PFC) algorithm for automatic train operation system. The proposed method facilitates autonomous driving of a train through a given guidance trajectory. Firstly, they present a multi-model architecture based on fuzzy c-means clustering algorithm. In order to obtain the optimal number of sub-linear models, they apply Xie–Beni cluster validity index. In this regards, the multi-model set is established off-line. Secondly, the proper sub-linear model is selected as the predictive model by using switching performance index at each time slot. The control variables are calculated by direct generalised predictive controller based on PFC. The control algorithm is simple, and can reduce the on-line computation time by directly identifies the unknown parameters in the controller. It can avoid recursively solving the Diophantine equations. The calculation of compensation value becomes simple by introducing PFC. Finally, simulation results are provided to show the effectiveness of the proposed scheme.

Research on Time-delay of the Edge Drop Control in Tandem Cold Mill Based on the Predictive Functional Control Algorithm

Research on Time-delay of the Edge Drop Control in Tandem Cold Mill Based on the Predictive Functional Control Algorithm

Based on PFC Prediction Algorithm of Cross-Cutting Machine Electronic CAM Control Simulation

Predictive functional control (PFC) is an advanced control algorithm, it belongs to the model predictive control. Electronic CAM is an important component of cross-cutting machine, is driven by servo motor, its structure and control performance determines the length of paper cutting. Around the PFC algorithm and the control of electronic CAM, this paper aimed at the speed of the cutter and the paper feed speed synchronization problems and have to cut the length of the paper is different, is put forward based on the predictive functional control (PFC) algorithm to predict the change of parameters of electronic CAM model itself. In this article, through the simulation, proving that PFC algorithm has strong anti-interference ability, traceability and stability.

The Application of New Predictive Functional Control in the Converter Gas Recovery System

In view of the characteristics of the converter differential pressure control system are nonlinear, large delay, uncertainty and large disturbance, the traditional PID control is strict with process model, which can't satisfy the requirement of tracking set point and suppressing disturbance. Predictive functional control is not strict with process model, it has good robustness, but has problem of large amount of calculation. By introducing the output prediction error and the output increment prediction error in the performance index function, a new predictive functional control algorithm with the PID structure is proposed, which combines ability of dealing with delay and robust for predictive function control with ability of favorable anti-interference and fast tracking for PID control. The simulation shows that the control strategy is feasible, and can obviously improve the dynamic quality and anti-interference ability of the converter differential pressure control system.