Adaptive Predictive Control System Research Articles

Adaptive model predictive control with ensembled multi-time scale deep-learning models for smart control of natural ventilation

Model predictive control (MPC) offers promising solutionsfor the smart control of natural ventilation. However, challenges arise in constructing precise models for such nonlinear systems, preserving accuracy over a long-prediction horizon while capturing short-term dynamics, and handling disturbances uncertainties. This study proposes a novel Ensembled Multi-time scale deep-learning-based Adaptive Model Predictive Control (EMA-MPC) system. It integrates an ensembled Long-Short-Term Memory (ensembled-LSTM) model, comprising two LSTM models for fast and slow time-scale dynamics, respectively, and continuously evolving to changing conditions through online model adaptation. A bound control module is incorporated as an additional safety mechanism ensuring the environmental control within the desired threshold during unforeseen scenarios. A multi-objective optimization problem is formulated to maintain indoor air temperature and CO2 concentration within the predefined comfort range while optimizing energy efficiency by controlling automated windows in a naturally ventilated room in winter. The EMA-MPC system demonstrates superior performance in balancing indoor air quality, temperature regulation, and energy efficiency. The ensembled-LSTM model significantly reduces the mean absolute error by 78.6% and 88.9% for CO2 and indoor air temperature predictions respectively, against a single LSTM model. The proposed EMA-MPC system achieves an 86% reduction in unmet CO2 hours compared to rule-based control and reduces occupied hours with temperature below 19 °C by 44% and 93% compared to enhanced MPC and basic MPC, respectively, while maintaining similar heating demand as other controllers. In conclusion, the proposed EMA-MPC system reduces modeling efforts and provides an effective approach towards reliable use of ML models in smart building control.

Power System Stabilizer as a Part of a Generator MPC Adaptive Predictive Control System

In this paper, a model predictive controller based on a generator model for prediction purposes is proposed to replace a standard generator controller with a stabilizer of a power system. Such a local controller utilizes an input-output model of the system taking into consideration not only a generator voltage Ug but also an additional, auxiliary signal (e.g., α, Pg, or ωg). This additional piece of information allows for taking oscillations into account that occur in the system and minimizing their impact on the overall system performance. Parameters of models used by the controller are obtained on the basis of the introduced black-box models both for a turbine and a synchronous generator, parameters of which are estimated in an on line fashion using a RLS method. The aim of this paper is to compare the behavior of the classical generator control system with a power system stabilizer and a model predictive control with an additional feedback signal. The novelty of the paper is related to the use of the predictive controller instead of the classical controller/stabilizer system and its possibility of stabilizing the power system. Contrary to the solutions found in the literature, which are commonly-based on a fuzzy logic approach, the authors propose the use of an adaptive model predictive controller, which takes advantage of the knowledge concerning the plant in the form of a model and adapts itself to the operating point of the system using the model parameters estimation mechanism. Moreover, the adaptive predictive controller, unlike other solutions, automatically adjusts signal levels to changes in the plant. The proposed solution is able to calculate the best control signal regardless of whether these changes of the plant are caused by a change in the operating point, or resulting from operation, e.g., wear of mechanical parts.

An Adaptive Model Predictive Control System for Virtual Coupling in Metros

Virtual coupling (VC) is an emerging concept and hot research topic in railways, especially for metro systems. Several unit trains in VC drive with a desired minimum distance, and they, as a whole, are regarded as a single train. In this work, a distributed adaptive model predictive control (AMPC) system is proposed to coordinate the driving of each unit train in VC. To obtain the accurate parameters of train dynamics model in a time varying environment, an estimator of the train dynamics model is designed for each AMPC controller. A variable step descent algorithm along the negative gradient direction is adopted for each estimator, which steers the estimated values of the parameters to real ones. Simulations are conducted and the results are compared with both nominal model predictive control system and AMPC system with fixed steps in the literature. Our proposed AMPC system with variable step (AMPCVS) has better performances than other two systems. Results indicate that there is an improvement of the proposed AMPC system with variable steps system when compared with other two existed systems. A running process of VC in a whole inter-station is also simulated here. Experimental results show that the trains track the desired objective well.

Adaptive predictive control of a novel shape memory alloy rod actuator

Shape memory alloys are among the highly applicable smart materials that have recently appealed to scientists from various fields of study. In this article, a novel shape memory alloy actuator, in the form of a rod, is introduced, and an adaptive model predictive control system is designed for position control of the developed actuator. The need for such an advanced control system emanates from the fact that modeling and controlling of shape memory alloy actuators are thwarted by their hysteresis nonlinearity, dilatory response, and high dependence on environmental conditions. Real-time identification and dynamic parameter estimation of the model are addressed according to orthogonal Laguerre functions and recursive least square algorithm. In the end, the designed control system is implemented on the experimental setup of the fabricated shape memory alloy actuator. It is observed that the designed control system successfully tracks the variable step and sinusoidal control references with startling accuracy of ±1 μm.

Adaptive personalized multivariable artificial pancreas using plasma insulin estimates

An adaptive and personalized multivariable artificial pancreas (mAP) system using plasma insulin estimates is proposed to efficiently accommodate major disturbances to the blood glucose concentration, such as meal and physical activity. Accurate adaptive glycemic models are developed through a recursive subspace identification technique with wearable physiological measurements and estimates of unannounced meal effect and plasma insulin concentration (PIC) along with continuous glucose concentration signals to characterize the glucose concentration dynamics under various conditions such as food consumption and physical activity. The identified models with time-varying parameters are employed in the design of an adaptive model predictive control (MPC) system that is cognizant of the PIC. The adaptive controller parameters, dynamic PIC constraint, addition of physiological measurements from wearable devices, feature variables generated from the glucose measurements, and estimation of uncertain model parameters, including the meal effect, enable the mAP system to effectively compute the optimal insulin infusion over diverse diurnal variations without meal and exercise announcements. Simulation case studies using a multivariable simulator demonstrate the efficacy of the proposed mAP system.

Chord Length Distribution Based Modeling and Adaptive Model Predictive Control of Batch Crystallization Processes Using High Fidelity Full Population Balance Models

The control of batch crystallizers is an intensively investigated topic as suitable crystallizer operation can reduce considerably the downstream operation costs and produce crystals of desired properties (size, shape, purity, etc.). Nevertheless, the control of crystallizers is still challenging. In this work the development of a fixed batch time full population balance model based adaptive predictive control system for cooling batch crystallizers is presented. The model equations are solved by the high resolution finite volume algorithm involving fine discretization, which provides a high fidelity, accurate solution. A physically relevant crystal size distribution (CSD) to chord length distribution (CLD) transformation is also developed making possible the direct, real-time application of the focused beam reflectance measurement (FBRM) probe in the control system. The measured CLD and concentration values are processed by the growing horizon estimator (GHE), whose roles are to estimate the unmeasurable ...

A Framework for Adaptive Predictive Control System Based on Zone Control

In view of the degradation of predictive control performance caused by model mismatch, a multi-variable adaptive predictive control system framework which is composed of zone model predictive control (MPC), identification module and performance monitoring module, is presented. The proposed framework synthesizes the traditional control mode and the test mode to construct a unified form, which is convenient to implement with the MPC software packages. Traditional setpoint control is switched to zone control to ensure that the process constraints remain satisfied in testing, while multi-variable test signals are introduced to guarantee the sufficient excitation of the plant. In addition, in order to maximize the signal-to-noise ratio, an adaptive method of determining the amplitude of test signals is proposed. All the online open-loop identification methods are suitable for this framework, as the testing is treated as “open-loop,” which solves the problem of the correlation between input signals and noises in the closed-loop identification. These characteristics of the proposed framework are illustrated via a simulation.

Developing of adaptive model predictive control system for heat treatment of iron-ore pellets with using recursive least square algorithm for online parameter estimation

The article discusses the problems of development of the system of adaptive predictive control of pellets heat treatment with online estimation of parameters of the process model. Due to nonstationarity in time of the process parameters caused by fluctuations of particle size distribution and fractional void of the layer, changes in the process equipment characteristics and the presence of noise in measurement channels, the existing automation systems of pellets heat treatment do not always allow to solve the problem of stabilization temperature profile in the pellets layer, as well as reduce the specific consumption of energy. To overcome these disadvantages the recursive least squares algorithm is proposed to use for estimating the parameters of process model which subsequently is the base for calculating the manipulated variable (the gas flow to the burner of the leading side of the indurating machine) with using the methods of Model Predictive Control theory that provides maintenance of a preset temperature regime of pellets indurating under conditions of uncontrolled disturbances. In accordance with the described approach it is suggested the variant of the structure of the system of adaptive predictive control of the temperature regime of pellets indurating in the separate gas-air chamber of indurating machine, and the simulation of this system was performed in Simulink package with the use of real data about the dependence of temperature in the heart of firing zone from gas consumption on the burner of leading side, which were obtained in a mode of passive experiment at the indurating machine OK-324 of JSC «Central GOK (ME)». The resulting system has demonstrated the high quality of the online estimation of parameters and sufficient convergence rate for conditions of pellets heat treatment. The obtained results allow us to recommend the developed method of formation of adaptive predictive control for automation of pellets heat treatment.

Adaptive output predictor based adaptive predictive control with ASPR constraint

The predictive controls are one of the major control strategies in the process control. However, in general, an exact model of the controlled system is required in order to predict the system’s outputs accurately. In this paper, we propose an adaptive predictive control using an adaptive output predictor with a simple structure for uncertain controlled systems. In the proposed method, the stability of the obtained adaptive predictive control system will be confirmed by setting a novel input constraint, “almost strictly positive real (ASPR) constraint”.

Stable Adaptive Predictive Control System Design via Adaptive Output Predictor for Multi-rate Sampled Systems

This paper deals with a design problem of an adaptive predictive control for uncertain multi-rate sampled systems. In the proposed method, an adaptive predictive control using a simple adaptive output estimator, which has been previously proposed for single-rate sampled system, will be expanded to a multi-rate sampled system. A robust and model-free design method of feedforward compensators for designing the adaptive output estimator for predictive control and for setting an input constraint, “almost strictly positive real (ASPR) constraint” for stable control system will also be provided for the considered multi-rate systems. The effectiveness of the proposed method will be confirmed through an experiments of two-tank process control.

Adaptive Predictive Control System with Disturbance Compensation based on Self-recurrent Wavelet Neural Network

Adaptive Predictive Control System with Disturbance Compensation based on Self-recurrent Wavelet Neural Network

Design and application of a steam temperature optimizer in a combined cycle

SUMMARYThis paper presents the application of an adaptive predictive control system, entitled ‘Steam Temperature Optimizer’ (STO), in the combined cycle of the Empresa Nacional de Electricidad SA at Barranco de Tirajana in the Canary Islands. This combined cycle comprises two gas turbines of 70 MW each, two recovery boilers, and one steam turbine also of 70 MW. The STO was applied to the control of the temperature of the high‐pressure recovery boilers and integrated in parallel with the plant's distributed control system by means of object linking and embedding for process control communication. The STO control strategies, designed to deal with the specific attemperation control problems and plant instrumentation limitations, are presented and the experimental results analyzed in comparison with those obtained by the existing PID‐based system. The STO improved significantly the control precision and the stability of the steam temperature and confirmed adaptive predictive control as a reliable self‐tuning methodology for this kind of process. Copyright © 2011 John Wiley & Sons, Ltd.

Modeling and control of quasi-keyhole arc welding process

Quasi-keyhole is a novel approach proposed to operate the keyhole arc welding process. Because the method's effectiveness depends on the amperage of the peak current used to establish the keyhole, this paper proposes adjusting the amperage based on the duration of the peak current, which equals the keyhole establishment time. A nominal model structure has been selected from those identified using experimental data and been used in the design of an adaptive predictive control system. Closed-loop control experiments have been conducted to verify the effectiveness of the developed system under varying set-points and varying travel speeds.

ROBUST ADAPTIVE PREDICTIVE CONTROL OF WATER DISTRIBUTION IN IRRIGATION CANALS

Applying automatic control methods to irrigation canals is a way to improve the management of irrigation systems. The dynamic difficulties that involved this hydraulic systems such as non-linearity, varying time delay, and uncontrolled perturbations make the choice of a suitable automatic control method a challenge. To this purpose, this paper presents a methodology for design of decentralized robust adaptive predictive control system of water distribution in irrigation canals. The objective of this system is to maintain the downstream end water level of each pool at constant target value under external uncontrolled perturbation.

Switching Model Adaptive Predictive Control

The paper considers the results of an adaptive predictive control system implemented in a combustion furnace. The main goal is to optimize the temperature control replacing the existing PID. The furnace dynamic is strongly nonlinear, has an important delay and its behavior depends on the operating point. These characteristics make the system very attractive for implementing a controller that achieves a more precise temperature control. The application consists of a weighted predictive control with its parameters obtained indirectly from a recursive identification of the plant. The project was developed at Siderca - Argentina, a seamless steel pipe factory.

ADAPTIVE PREDICTIVE CONTROL IN A THERMAL POWER STATION

This paper presents the summary application of an adaptive predictive control system (APCS) to the control and optimization of the processes in a coal power station. The results obtained, corresponding to a Research and Development project at the Pasajes de San Juan power station, which belongs to the company Iberdrola, demonstrate the suitability of the APCS application in this field as well as the important benefits that may be derived from it. © 1997 John Wiley & Sons, Ltd.

Adaptive Predictive Control: Practical Aspects.

The paper considers the results of an adaptive predictive control system implemented in a combustion furnace. The main goal is to optimize the temperature control replacing the existing РID. The furnace dynamic is strongly nonlinear, has an important delay and its behavior depends on the operating point. These characteristics make the system very attractive for implementing sophisticated controllers which give a more precise temperature control. The application consists of a weighted predictive control with its parameters obtained indirectly from a recursive identification of the plant. The project was developed in Siderca - Argentina in collaboration with the University of Buenos Aires.

Automation of a fixed‐bedcontinuous–flow reactor

This paper describes the design and operation of a laboratory plant with a fixed-bed continuous-flow reactor, fully automated and controlled from a personal computer. The automated variables include two gas flows, one liquid flow, six temperatures, two pressures, one circulation of a cooling liquid, and 10 electrovalves. An adaptive-predictive control system was used. The chemical process chosen to run the automated reactor was the conversion of methanol to gasoline over a ZSM-5 catalyst. This is a highly exothermal process, so a cascade control system had to be used to control the reactor internal temperature. Pressure and weight hourly space velocity (WHSV) were fixed at 1 arm and 1.5h-1 respectively. Accurate control (±0.2°C) of the reactor’s internal temperature was achieved and repeatability for the conversion of methanol to gasoline was good.

Adaptive Predictive Control of a Boiling Water Reactor

This paper reports on an adaptive predictive control system (APCS) is applied to the design of the recirculation and feedwater control systems of a boiling water reactor. The APCS uses the dead zone method to modify the adaptive law; thus, it is stable in the presence of unmodeled dynamics and bounded disturbances. Two single-input/single-output control systems are used instead of a multi-input/multi-output control system in order to simplify parameter adaptation. The interactions among the subsystems are treated as unmeasured disturbances. A simulation using the reactor model shows that the dome pressure versus recirculation pump speed subsystem is a non-minimum-phase system. To handle this system, the weighting polynomials for the system input and output are incorporated to form an augmented minimum-phase system and then the augmented system is controlled. The proposed algorithm is stable, does not require persistent excitation of the reference input, and performs well, which makes it practical for implementation.

Discrete-time adaptive predictive control system by 2-port design

A discrete-time adaptive predictive control system is proposed for compensating the unmeasurable and time-varying disturbance. The disturbance is compensated by two mechanisms. One is the disturbance compensation based on the result from an observer for the unknown disturbance and the other is a feedback loop with a predictor for the output. This system can be interpreted within the framework of a 2-port system recently proposed by Stephanopoulos and Huang. Numerical examples are provided to demonstrate the performance and robustness of this proposed system.