Implementation Of Predictive Control Research Articles

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system’s robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology.

Decarbonization through smart energy management: Climate control in building-integrated rooftop greenhouses for urban agriculture across various climate conditions

This paper investigates the potential benefits of integrating rooftop greenhouses (RTGs) with buildings to reduce energy and CO2 costs, contributing to urban decarbonization efforts. This integration not only advances sustainable urban agriculture but also aligns with the United Nations Sustainable Development Goal 7 (SDG7) - Affordable and Clean Energy, and Sustainable Development Goal 11 (SDG11) - Sustainable Cities and Communities, through its energy-efficient, low-carbon approach. We hypothesize that this integration can significantly lower energy use in integrated RTGs (i-RTGs) and set out to validate this through dynamic models for both building and i-RTG climates, focusing on CO2 concentration, humidity, and temperature. Central to our approach is the implementation of a nonlinear model predictive control (NMPC) framework, which utilizes these dynamic models to make control decisions aimed at minimizing total control costs. This framework operates in a receding horizon procedure, regulating climate factors within the i-RTG and building using various actuators. We validate our approach by simulating an i-RTG atop a building in eleven different cities, demonstrating interactions such as energy, moisture, and CO2 exchange. Significantly, our study reveals that integrating i-RTG with buildings under the NMPC framework leads to a 15.2% reduction in control costs. Additionally, we find that the i-RTG model is adaptable to different climates, with colder regions showing greater cost reduction potential. Our study underscores the viability and advantages of integrating i-RTGs with buildings, offering a sustainable, decarbonizing solution for urban agriculture and building management that contributes to the fulfillment of SDG7 and SDG11.

Real-time implementation of nonlinear model predictive control for high angle of attack Maneuvers in fighter aircrafts using deep learning

ABSTRACT Nonlinear model predictive control (NMPC) provides a nice framework for accounting for multivariable nonlinear dynamics subject to constraints; however, the cost of implementing NMPC is high due to the need to solve a large-scale nonlinear program to optimality in real-time. This research is focussed on the design of real-time solution to NMPC (low computations) for fast dynamic systems. In this work, a comprehensive and innovative solution based on a deep learning-based approximate NMPC scheme has been proposed to overcome these computational challenges. The main idea is to learn a deep neural network followed by the symbolic simplification of the NMPC law, whose evaluation cost and memory footprint can be optimized offline. Since not all the states of the system are measured, we also combine the deep learning based NMPC approach with an unscented Kalman filter (UKF) as well as show how this scheme can be easily modified to be offset-free (i.e. remove steady-state error due to persistent disturbances and modelling error). The proposed approach is implemented on a highly fast dynamic system (i.e. F-16 fighter aircraft to perform a high angle of attack maneuver) to check the controller efficiency. Fighter aircraft need to be able to make fast and complex maneuvers (that exploit strongly nonlinear dynamics) to be able to maintain air superiority. However, such aggressive maneuvers require strong coordination between the actions to avoid destabilizing the system. A quantitative analysis from comparison with the standard MPC approach shows the practical utility of the proposed approach.

Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building

This paper presents a heuristic model predictive control (MPC) methodology to activate energy flexibility in fully electric school buildings in cold climates to reduce electricity demand during peak demand periods of the electric grid. To streamline the implementation of MPC, the proposed approach employs grey-box archetypes, a clustering of weather conditions to identify typical scenarios and a limited number of possible setpoint profiles. A data-driven grey-box approach is used to create archetype models for different thermal zones in a typical school building; this approach enables rapid development and requires much less calibration data than black-box models. A third-order resistance-capacitance thermal network for zones with convective heating and a fourth-order model for zones with radiant floor heating are developed and calibrated using measured data from an all-electric school building in Québec, Canada. The weather data are clustered into several categories, representing different weather conditions (6 clusters representing two ambient temperature ranges and three solar radiation ranges). The heuristic MPC strategy uses predefined optimal setpoint profiles for each cluster and weather prediction one day ahead to shift the building load from on-peak to off-peak hours. For each heuristic MPC scenario, the model runs a simulation using forecast weather data to quantify and activate energy flexibility in response to grid requirements. The developed MPC framework was implemented in the school used as the case study. Ten classrooms are investigated, with six using the MPC and four as a reference case with the reactive control system and default zone setpoint profiles. Results indicate that the school building can provide between 47% and 95% energy flexibility (load shifting relative to reference) during on-peak hours and up to 44% electricity cost reduction while satisfying acceptable temperature constraints. By implementing the proposed MPC, energy flexibility of 32 W/m2 of floor area for the zones with a convective heating system and 65 W/m2 of floor area for the zones with radiant heating can be achieved during a demand response event. The proposed strategy can be generalized and replicated in other school buildings.

Implementation of New Predictive Control Based Volterra Model for Fast Dynamic Systems Using Microcontrollers: Real Time Application

This paper presents a new contribution in the implementation of Volterra model predictive control for fast dynamics systems. The control approaches considered results on a switch paradigm that combine an online part based on suboptimal solution and an offline part referred to an offline neural network controller. The proposed approach has an advantage in comparison with nonlinear optimization-based control schemes. A real time application on STM32 to control a boost converter is studied and the results show very remarkable performances in time computing.

Predictive controllers for synchronous reluctance motor drive systems

This paper proposes the design and implementation of predictive controllers for synchronous reluctance motor drive systems to enhance their dynamic responses. The predictive speed and current controllers in this paper are designed in systematic procedures. The predictive speed controller is implemented by using Laguerre function procedure. The Laguerre function is used to simplify the algorithm and to minimize the execution time of the digital signal processor. For predictive current controller, a finite control set method improves the current tracking ability. The measured currents are used to predict the future phase-current based on the motor model. The optimal control inputs of both predictive controllers are determined by using a cost function minimization method. Experimental results show the proposed drive system provides a wide adjustable speed range, from 2 r/min to 1800 r/min. It has better performance than a proportional-integral (PI) controller including fast rise time, which is 0.9 second, small steady state error, which is 0.32 r/min, and small current ripples. A 32-bit floating-point digital signal processor, TMS-320-F-28335 DSP, is employed to implement the control algorithms.

Microgrid Formation and Real-Time Scheduling of Active Distribution Networks Considering Source-Load Stochasticity

The post-disruption microgrid (MG) formation and the subsequent scheduling are resilience-enhancing measures for active distribution networks (ADNs) against disastrous events. This paper proposes an integrated MG formation and scheduling solution, considering stochastic loads and distributed generators (DGs). Specifically, a first-stage MG formation model and a sec-ond-stage MG scheduling model are proposed to flexibly identify MG boundaries and restore as many critical loads as possible. The MG formation problem is formulated over multiple time steps to address time-varying generation outputs of intermittent DGs. Moreover, in the MG scheduling problem, the stochasticity of load demands and DG outputs is modeled as joint chance constraints (JCCs) to describe the possibility of security violations in the over-all system. An efficient approximation method is next developed to convexify the JCCs into solvable second-order cones. The interac-tion between the MG formation and scheduling by the model predictive control implementation achieves a responsive solution for enhancing the emergency support capability of ADNs by incor-porating power forecasts and real-time scheduling. The performance of the proposed method is verified in a modified real-world 136-node test feeder under catastrophic failures.

Development of a compact rotary series elastic actuator with neural network-driven model predictive control implementation

Development of a compact rotary series elastic actuator with neural network-driven model predictive control implementation

Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties

Doubly fed induction generators (DFIG) find extensive application in variable-speed wind power plants, providing notable advantages such as cost-effectiveness, operational flexibility across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional model predictive controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. To validate the proposed control approach, the results are compared with PID based controller in MATLAB/Simulink. The simulation results showed that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input.

Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed Traffic

Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed Traffic

A comparison between grey-box models and neural networks for indoor air temperature prediction in buildings

Model Predictive Control has gained much attention due to its potential to improve building operations by reducing costs, integrating renewable energy sources, and increasing thermal comfort. This paper aims to compare the accuracy of grey-box models based on resistance–capacitance (RC) networks and Long-Short-Term Memory (LSTM) neural networks in the prediction of the buildings’ thermal response, which is a key feature for the successful implementation of predictive controllers. Indoor air temperature prediction tests have been performed on simulated and measured data from buildings with different thermal insulation and thermal mass during both heating and cooling seasons. Results show that neural networks have, on average, a better prediction performance than grey-box models. Both modelling approaches are affected by the building characteristics and by the season considered. The grey-box models require less training data, although the latter seems to play a role only in the worse-performing tests. When user setpoint changes in the testing phase, the LSTM neural network shows a significant drop in the root mean square error. In conclusion, although LSTM outperforms grey-box models on average, the reduced training data and higher reliability under normal operating conditions, as well as their linearity, make RC models a strong alternative for predictive controllers.

Hybrid model development and nonlinear model predictive control implementation for continuous dry granulation process

This study focuses on the development of a hybrid model to integrate roll compaction and ribbon milling operations to design and control a continuous dry granulation process. The proposed hybrid model has three features: (1) it compensates for underestimated roll gap measurements with knurled rolls, (2) it represents the bimodal size distribution of granules using five fitting parameters of the bimodal Weibull distribution instead of only using specific size percentiles, and (3) it considers the impact of rotor-screen gaps on granules. Furthermore, the hybrid model facilitates the implementation of nonlinear model predictive control in a roller compactor Alexanderwerk WP120. Compared to widely applied open-loop operations in the pharmaceutical industry, nonlinear model predictive control demonstrates better performance, indicated by lower integral absolute errors in controlling mass throughput and ribbon solid fraction, in which real-time measurements can be obtained using a near-infrared sensor and a novel spectra selection approach.

Real-Time Implementation of Predictive Control in Power Inverters Based on Nearest Neighbor Searching

Real-Time Implementation of Predictive Control in Power Inverters Based on Nearest Neighbor Searching

Sustaining the DC Micro-Grid Bus Voltage through the Implementation of Finite Control Sets Model Predictive Control in a Photovoltaic Converter System

Sustaining the DC Micro-Grid Bus Voltage through the Implementation of Finite Control Sets Model Predictive Control in a Photovoltaic Converter System

Neural Network-Based Hammerstein Model Identification of a Lab-Scale Batch Reactor.

This paper focuses on two types of neural network-based Hammerstein model identification methods for the acrylamide polymerization reaction of a batch reactor process. The first neural-based identification type formulates the weights of the multilayer network directly as parameters of the nonlinear static and linear dynamic blocks of the Hammerstein model and trains the weights using a gradient-based backpropagation algorithm. In the second identification type, the nonlinear static block of the Hammerstein model is framed as a single hidden-layer feedforward network and both nonlinear and linear block parameters are trained using an extreme learning machine, where the training procedure is exempted from gradient calculation. The primary focus of the paper is neural-based model identification of a complex nonlinear system, which facilitates ease of linear/nonlinear controller design with good learning speed and less computations. A future work toward the machine learning-based nonlinear model predictive controller implementation using the Jetson Orin Nano board is also described.

Neural networks as an approximator for a family of optimization algorithm solutions for online applications

In this paper, we propose a sufficient condition at which a neural network can approximate a set of optimization algorithm solutions; we establish under which conditions a neural network can replace an optimization algorithm to solve a problem with the objective of safely deploying that network in a system where online solutions are necessary to simplify the hardware or allowing the processor to solve the optimization problem on time. To that end, first, we define the family of optimization problems to be addressed; then, we construct a vector with the parameters on which the solution depends, in order to propose a function based on the first-order Karush–Kuhn–Tucker conditions to find conditions under which the inverse of the proposed function maps the problem minimizer with respect to the constructed vector, we provide the sufficiency proof of, both, existence and feasibility of approximation by a neural network regarding the inverse function. Two case studies are proposed, one numerical case showing how a neural network can solve an optimization problem faster than popular solvers to illustrate how it can be implemented in applications where the computation time is tight, and the other case is a Model Predictive Control implementation with the optimization problem solver replaced by a neural network which allows a hardware downgrade; both cases are presented with time statistics comparisons.

Implementation of model predictive control in a programmable logic controller

The article is aimed at the development of modern automatic control systems, which should provide high-performance indicators in the conditions of variable operating modes of industrial equipment due to effective control structures and algorithms. The purpose of the study is to reduce the cost of the basic oxygen furnace steel, which is a consequence of the increase in the share of scrap metal due to the enhanced post-burning of CO to CO2 in the cavity, by optimal controlling the duty mode parameters using model-predictive control. The blowing mode of the basic oxygen furnace was considered as a technological object of control, and the problem of controlling blowing parameters in conditions of non-stationarity of the rate of metal decarburization was analyzed. The use of a model-predictive controller made it possible to improve the quality of control for the oxygen flow circuit by 39% and the maximum dynamic deviation of the CO2 content in the gases was reduced by 16.5% compared to the PID control. The implementation of a software-hardware control system using model-predictive control based on a programmable logic controller is considered.

Code generation for embedded predictive control of gas water heaters

Conventional control strategies usually employed in tankless gas water heaters present difficulty in controlling the hot water temperature when subjected to sudden changes in water flow rate. Inadequate control leads to temperature overshoots and undershoots with long settling times that severely affect the user comfort, increasing water and energy wastage and associated gas emissions. A strategy based on model predictive control is presented to reduce the impact of changes in hot water demand. A semi-empirical model, parameterized with experimental data and compatible with real-time simulation, is used for the heat cell. A tailored state observer is proposed, considering time-varying delays characterizing this thermal process. An automatic code generation software tool was developed for the embedded implementation of gas water heater predictive controllers. Numerical simulations and hardware-in-the-loop experiments were established to evaluate conventional and predictive control strategies. It was demonstrated that embedded model predictive control could be successfully implemented on computationally limited microcontrollers, even for thermal systems with extensive varying time delays. Predictive control has shown significant performance improvements, with decreased temperature fluctuations, a gain in comfort index from 36% to 75% and a reduction of up to 32 s in the settling time.

Optimal multi-layer economical schedule for coordinated multiple mode operation of wind–solar microgrids with hybrid energy storage systems

The aim of this paper is the design and implementation of an advanced model predictive control (MPC) strategy for the management of a wind–solar microgrid (MG) both in the islanded and grid-connected modes. The MG includes energy storage systems (ESSs) and interacts with external hydrogen and electricity consumers as an extra feature. The system participates in two different electricity markets, i.e., the daily and real-time markets, characterized by different time-scales. Thus, a high-layer control (HLC) and a low-layer control (LLC) are developed for the daily market and the real-time market, respectively. The sporadic characteristics of renewable energy sources and the variations in load demand are also briefly discussed by proposing a controller based on the stochastic MPC approach. Numerical simulations with real wind and solar generation profiles and spot prices show that the proposed controller optimally manages the ESSs, even when there is a deviation between the predicted scenario determined at the HLC and the real-time one managed by the LLC. Finally, the strategy is tested on a lab-scale MG set up at Khalifa University, Abu Dhabi, UAE.

Certification of the proximal gradient method under fixed-point arithmetic for box-constrained QP problems

In safety-critical applications that rely on the solution of an optimization problem, the certification of the optimization algorithm is of vital importance. Certification and suboptimality results are available for a wide range of optimization algorithms. However, a typical underlying assumption is that the operations performed by the algorithm are exact, i.e., that there is no numerical error during the mathematical operations, which is hardly a valid assumption in a real hardware implementation. This is particularly true in the case of fixed-point hardware, where computational inaccuracies are not uncommon. This article presents a certification procedure for the proximal gradient method for box-constrained QP problems implemented in fixed-point arithmetic. The procedure provides a method to select the minimal fractional precision required to obtain a certain suboptimality bound, indicating the maximum number of iterations of the optimization method required to obtain it. The procedure makes use of formal verification methods to provide arbitrarily tight bounds on the suboptimality guarantee. We apply the proposed certification procedure on the implementation of a non-trivial model predictive controller on 32-bit fixed-point hardware.