Implementation Of Model Predictive Control Research Articles

Continuous adaptive nonlinear model predictive control using spiking neural networks and real-time learning

Model predictive control (MPC) is a prominent control paradigm providing accurate state prediction and subsequent control actions for intricate dynamical systems with applications ranging from autonomous driving to star tracking. However, there is an apparent discrepancy between the model’s mathematical description and its behavior in real-world conditions, affecting its performance in real-time. In this work, we propose a novel neuromorphic (brain-inspired) spiking neural network for continuous adaptive non-linear MPC. Utilizing real-time learning, our design significantly reduces dynamic error and augments model accuracy, while simultaneously addressing unforeseen situations. We evaluated our framework using real-world scenarios in autonomous driving, implemented in a physics-driven simulation. We tested our design with various vehicles (from a Tesla Model 3 to an Ambulance) experiencing malfunctioning and swift steering scenarios. We demonstrate significant improvements in dynamic error rate compared with traditional MPC implementation with up to 89.15% median prediction error reduction with 5 spiking neurons and up to 96.08% with 5,000 neurons. Our results may pave the way for novel applications in real-time control and stimulate further studies in the adaptive control realm with spiking neural networks.

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.

Enhancing Electric Vehicle Charger Performance with Synchronous Boost and Model Predictive Control for Vehicle-to-Grid Integration

This paper investigates optimizing the power exchange between electric vehicles (EVs) and the grid, with a specific focus on the DC-DC converters utilized in vehicle-to-grid (V2G) systems. It specifically explores using model predictive control (MPC) in synchronous boost converters to enhance efficiency and performance. Through experiments and simulations, this paper shows that replacing diodes with SIC MOSFETs in boost converters significantly improves efficiency, particularly in synchronous mode, by minimizing the deadtime of SIC MOSFETs during switching. Additionally, this study evaluates MPC’s effectiveness in controlling boost converters, highlighting its advantages over traditional control methods. Real-world validations further validate the robustness and applicability of MPC in V2G systems. This study utilizes TMS320F28379D, one of Texas Instruments’ leading digital signal processors, enabling the implementation of MPC with a high PWM frequency of up to 200 MHz. This processor features dual 32-bit CPUs and a 16-bit ADC, allowing for high-resolution readings from sensors. Leveraging digital signal processing technologies and advanced electronic circuits, this study advances the development of high-performance boost converters, achieving power outputs of up to 48 watts and output voltages of 24 volts. Electronic circuits (PCB boards) have been devised, implemented, and evaluated to showcase their significance in advancing efficient V2G integration.

A stability governor for constrained linear–quadratic MPC without terminal constraints

This paper introduces a supervisory unit, called the stability governor (SG), that provides improved guarantees of stability for constrained linear systems under Model Predictive Control (MPC) without terminal constraints. At each time step, the SG alters the setpoint command supplied to the MPC problem so that the current state is guaranteed to be inside of the region of attraction for an auxiliary equilibrium point. The proposed strategy is shown to be recursively feasible and asymptotically stabilizing for all initial states sufficiently close to any equilibrium of the system. Thus, asymptotic stability of the target equilibrium can be guaranteed for a large set of initial states even when a short prediction horizon is used. A numerical example demonstrates that the stability governed MPC strategy can recover closed-loop stability in a scenario where a standard MPC implementation without terminal constraints leads to divergent trajectories.

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.

Real-world implementation of a cloud-based MPC for HVAC control in educational buildings

In their quest to enhance energy efficiency and carbon footprint management, many enterprises are turning their attention towards optimizing their facilities through amplified data monitoring and innovative control methods. In this context, this research highlights a practical solution in a commercial building located in the United States. It explains the creation, implementation, and costs of a universally applicable model predictive control (MPC) framework for the building’s heating, ventilation, and air conditioning (HVAC) system. The paper elaborates on both the hardware and software used to accumulate pertinent data and the formulation of the MPC system. This approach leverages cloud-based microservices and can be seamlessly integrated into current building management systems. In this regard, this paper presents three innovative strategies: Proportional–integral (PI)+Preheating, MPC, and occupancy-based control. These scenarios were designed to improve energy efficiency and thermal comfort significantly by reducing temperature fluctuations and adhering more closely to the setpoint temperature. The implementation of these strategies resulted in substantial energy savings, with reductions in energy consumption of 12.83%, 19.21%, and 14.98%, respectively.

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.

Explicit model predictive control of magnetorheological suspension for all-terrain vehicles with road preview

The integration of magnetorheological (MR) semi-active suspension systems in all-terrain vehicles (ATV) has garnered significant attention due to their ability to enhance damping performance and off-road capabilities. However, traditional control strategies result in poor control accuracy and limited vibration reduction effects when facing complex road excitations and impact disturbances. With technological advancements, enhanced vehicle environmental perception and road sensing capabilities have made it possible to implement model predictive control (MPC) for vehicle suspensions. Nevertheless, traditional MPC is limited in vehicle suspension applications due to its high computational complexity. To address these issues, this study introduces an explicit model predictive control based on road preview (EMPC-P). Firstly, road data obtained through a non-contact measurement method enables the system to perceive road excitation information in advance. Subsequently, a 7 Degree-of-Freedom (7-DOF) suspension model incorporating road excitations is constructed. By adhering to system constraints and employing a multiparameter optimization method, the control problem based on rolling optimization is transformed into an explicit polyhedral system. The offline precomputation of control state relations enhances the computational efficiency of the control system. Through this approach, the designed EMPC allows the vehicle suspension system to make optimal control decisions quickly and accurately in response to complex driving conditions, thus improving the damping effect of the system. Through a combined approach of simulation and experimental validation, the designed EMPC-P controller is compared with the Skyhook controller under preview and non-preview states, respectively. Empirical testing confirms that the EMPC-P exhibits superior damping effects, significantly improving vehicle ride comfort and handling stability.

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.

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.

Desain dan Simulasi Model Predictive Control pada Sistem Pembagian Daya untuk Kendaraan Listrik Hibrida Fuel Cell – Baterai

This research examines the performance of implementing Model Predictive Control (MPC) in the energy management system of fuel cell – battery hybrid electric vehicles. Three parameter variations were carried out on the MPC controller, namely variations in the horizon value, objective function, and weighting in the objective function. In testing variations in the horizon value of the designed MPC controller, it shows that the higher the horizon value used, the SOC and the final SOC are faster and closer to the optimal SOC determined by compensating for higher hydrogen consumption. Testing of objective function variations shows that the objective function implemented on the MPC controller influences the system response characteristics. It was found in objective function testing that optimal use of fuel cell power produces fuel cell power output with a working efficiency range of 57% - 60% when compared to other objective functions that work in the efficiency range of 49.2% - 57%. Finally, in testing variations in weighting values, it was found that the higher the weighting of an expression in the objective function, the more the optimizer will penalize the expression so that the solver will minimize the expression in the optimization process. Therefore, the MPC controller parameter values ​​need to be paid attention to so that the response characteristics are in accordance with the design. which are desired. It should be noted that this research does not use a speed prediction model so it is assumed that the speed is known without any uncertainty.

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.

Signal temporal logic synthesis under Model Predictive Control: A low complexity approach

In this paper, we focus on the challenging problem of model predictive control (MPC) for dynamics systems with high-level tasks formulated as signal temporal logic (STL). The state-of-art for STL synthesis mainly suffers from limited scalability with respect to the complexity of the task and the planning horizon, hindering the real-time implementation of MPC. This work tackles this issue by STL formula reformulation and input blocking. Specifically, simplifications are applied on disjunctive STL (sub)formulae recursively in the framework of MPC to limit formula size. We show that the simplified STL can be reformulated into mixed integer linear programming (MILP) constraints with a modifiable number of binary variables being required. The move blocking scheme is then employed to further reduce problem complexity by fixing input variables to be constant over several time intervals. In order to trade off the control performance and computational load, a blocking structure design with on-line correction is proposed. The extension of the proposed STL-MPC algorithm to uncertain systems is achieved through STL constraint tightening. Simulations and experiments show the effectiveness of the proposed algorithm.

Efficient Navigation and Motion Control for Autonomous Forklifts in Smart Warehouses: LSPB Trajectory Planning and MPC Implementation

The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that combines a modified Linear Segment with Parabolic Blends (LSPB) trajectory planning with Model Predictive Control (MPC) to ensure efficient and secure robot movement. To validate the performance of our proposed path-planning method, MATLAB-based simulations were conducted in various scenarios, including rectangular and warehouse-like environments, to demonstrate the feasibility and effectiveness of the proposed method. The results demonstrated the feasibility of employing Mecanum wheel-based robots in automated warehouses. Also, to show the superiority of the proposed control algorithm performance, the navigation results were compared with the performance of a system using the PID control as a lower-level controller. By offering an optimized path-planning approach, our study enhances the operational efficiency and effectiveness of Mecanum wheel robots in real-world applications such as automated warehousing systems.

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.

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.