Application Of Predictive Control Research Articles

A wave forecasting method based on probabilistic diffusion LSTM network for model predictive control of wave energy converters

Accurate long-term prediction of wave excitation forces is critical for optimizing wave energy converters, enabling enhanced control, and improving energy absorption efficiency. Traditional prediction models often employ deterministic conservative approaches, overlooking uncertainties. This paper introduces a novel prediction method based on the probabilistic diffusion model, offering a more precise prediction while accounting for uncertainty. Notably, our approach goes beyond previous works by simultaneously achieving control objectives for maximizing energy absorption, contrasting with methodologies solely focused on prediction and control tasks. The proposed method utilizes long-short time memory to extract temporal information from historical wave excitation observation. The hidden features are then processed through a diffused probabilistic-based unit. Subsequently, a time-scale neural network is developed for wave excitation moment prediction, followed by the application of nonlinear model predictive control to maximize the energy absorption. The methodology is validated on the 1:20 Wavestar prototype devices through numerical simulations. Results indicate that the predicted excitation moment closely align with measured values. In cases 4, 5, 6, the proposed method yields a substantial improvement in maximum control energy absorption–22%, 16%, and 31%, respectively–compared to traditional prediction methods. The proposed approach not only achieves a more accurate wave excitation moment prediction with uncertainty consideration but also introduces advanced control strategies in a full-scale plant application. This work contributes significantly to the field by bridging the gap between precise prediction and effective control in wave energy conversion systems.

Energy optimization algorithms for multi-residential buildings: A model predictive control application

This study presents an optimization algorithm for Model Predictive Control (MPC) of the HVAC systems in multi-family residential buildings assessing the performance of four objective functions. Implemented in C++, using the free OR-Tools optimization library, the model is formulated a Mixed Integer-Linear Programming (MILP) problem. The study analyses the results of tests conducted on a 20-dwelling block in Switzerland across various weather and occupancy conditions, resulting in a parametric study of 64 cases.The models developed for the MPC are Grey-box type for the interconnected energy systems: the building, thermal storage tanks, a heat pump the ventilation system and PV collectors, highlighting a radiant wall heating system integrated into the building facade. The tanks and the heat pump models were informed with manufacturer data, while for the building a R3C3 thermal-electrical equivalent model was developed, calibrated using TRNSYS simulations with a root mean square error of 1.7%.Findings demonstrate how the algorithm optimizes the operation according to the desired criteria, while ensuring indoor comfort with a 15-minute time resolution. The time execution of the majority of cases is under 3 min in a low-specs computer, affirming its practical viability for real-world implementation.

Modeling of Cooperative Robotic Systems and Predictive Control Applied to Biped Robots and UAV-UGV Docking with Task Prioritization.

This paper studies a cooperative modeling framework to reduce the complexity in deriving the governing dynamical equations of complex systems composed of multiple bodies such as biped robots and unmanned aerial and ground vehicles. The approach also allows for an optimization-based trajectory generation for the complex system. This work also studies a fast-slow model predictive control strategy with task prioritization to perform docking maneuvers on cooperative systems. The method allows agents and a single agent to perform a docking maneuver. In addition, agents give different priorities to a specific subset of shared states. In this way, overall degrees of freedom to achieve the docking task are distributed among various subsets of the task space. The fast-slow model predictive control strategy uses non-linear and linear model predictive control formulations such that docking is handled as a non-linear problem until agents are close enough, where direct transcription is calculated using the Euler discretization method. During this phase, the trajectory generated is tracked with a linear model predictive controller and addresses the close proximity motion to complete docking. The trajectory generation and modeling is demonstrated on a biped robot, and the proposed MPC framework is illustrated in a case study, where a quadcopter docks on a non-holonomic rover using a leader-follower topology.

Sensorless Model Predictive Control of Permanent Magnet Synchronous Motors Using an Unscented Kalman Filter

This paper deals with the application of the Model Predictive Control (MPC) algorithm to the sensorless control of a Permanent Magnet Synchronous Motor (PMSM). The proposed estimation strategy, based on the unscented Kalman filter (UKF), uses only the measurement of the motor current for the online estimation of speed, rotor position and load torque. Information about the system state is fed into the MPC algorithm. The results verify the effectiveness and applicability of the proposed sensorless control technique. To demonstrate its real-world applicability, implementation in low-speed direct drive astronomy telescope mount systems is investigated. The outcomes of the implementation are thoroughly examined, leading to insightful conclusions drawn from the observed results. Through rigorous theoretical analysis and extensive simulation studies, this paper establishes a solid foundation for the proposed sensorless control technique. The results obtained from simulation studies and real-world applications underscore the efficacy and versatility of the proposed approach, offering valuable insights for the advancement of sensorless control strategies in motor applications. The main aim of this work is to demonstrate and validate the practical feasibility of combining two complex techniques, establishing that such an integration is not only possible but also effective in achieving the desired objectives.

Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control

The accuracy of the underlying model predictions is crucial for the success of model predictive control (MPC) applications. If the model is unable to accurately analyze the dynamics of the controlled system, the performance and stability guarantees provided by MPC may not be achieved. Learning-based MPC can learn models from data, improving the applicability and reliability of MPC. This study develops a nonlinear sparse variational Bayesian learning based MPC (NSVB-MPC) for nonlinear systems, where the model is learned by the developed NSVB method. Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty. The suggested approach ensures input-to-state stability (ISS) and the feasibility of recursive constraints in accordance with the concept of an invariant terminal region. Finally, a PEMFC temperature control model experiment confirms the effectiveness of the NSVB-MPC method.

AI APPLICATIONS IN RESERVOIR MANAGEMENT: OPTIMIZING PRODUCTION AND RECOVERY IN OIL AND GAS FIELDS

This paper explores various AI applications in reservoir management, highlighting how machine learning, data analytics, and advanced algorithms contribute to optimizing production and recovery in oil and gas fields. From predictive modeling to real-time monitoring and control, AI offers promising solutions to address the industry's evolving demands and enhance operational performance Reservoir management in oil and gas fields has historically presented challenges demanding continuous monitoring, analysis, and optimization for maximizing production and recovery. The integration of artificial intelligence (AI) technologies has ushered in a new era, providing reservoir engineers and operators with potent tools to augment decision-making processes and enhance overall efficiency. This paper delves into diverse AI applications within reservoir management, emphasizing the pivotal roles played by machine learning, data analytics, and advanced algorithms in optimizing production and recovery processes in oil and gas fields. The exploration encompasses predictive modeling, real-time monitoring, and control applications, showcasing how AI offers promising solutions to meet the dynamic demands of the industry while significantly improving operational performance. As we navigate through this technological evolution, the synergistic relationship between reservoir management and AI promises not only to address current challenges but also to lay the foundation for a more efficient and sustainable future in the oil and gas sector. The integration of artificial intelligence (AI) technologies has ushered in a new era, providing reservoir engineers and operators with potent tools to augment decision-making processes and enhance overall efficiency. The exploration encompasses predictive modeling, real-time monitoring, and control applications, showcasing how AI offers promising solutions to meet the dynamic demands of the industry while significantly improving operational performance. As we navigate through this technological evolution, the synergistic relationship between reservoir management and AI promises not only to address current challenges but also to lay the foundation for a more efficient and sustainable future in the oil and gas sector. Keywords: Reservoir Management, Oil And Gas Fields, Optimization, Recovery, Machine Learning, Data Analytics, Predictive Modeling.

A low computation-cost locomotion control for underwater snake robot based on Monte Carlo model predictive control and curvature derivative control

ABSTRACT This work studies the application of a sample-based model Monte Carlo Model Predictive Control (MCMPC) to address discontinuous events, such as collisions. The inclusion of contact dynamics in the prediction steps of MCMPC renders it advantageous for the operation of underwater snake robots in restricted environments during rescue missions. The Curvature Derivative Control (CDC) method is employed for the purpose of controlling the joints, with the exception of the head joint. The snake robot body demonstrates adaptability to various environments with the assistance of the CDC. Results obtained from the simulation demonstrate that the snake robot is capable of autonomously navigating from its beginning position outside the region to a pipe as the entrance in order to get inside. The robot successfully traverses through various internal structures within the pipe and ultimately reaches the intended destination, guided by a predetermined cost function of MCMPC. By using the capabilities of CUDA GPU parallel computing, the computational time is substantially lowered.

Generalized predictive control application scheme for nonlinear hydro-turbine regulation system: Based on a precise novel control structure

Generalized predictive control (GPC), which has excellent control performance and robustness, is only applicable to the control object that can be expressed in the transfer function and is difficult to be directly applied to the hydraulic turbine regulation system (HTRS) that contain extremely complex nonlinearity. Therefore, considering the practical application of GPC in a hydropower station containing the Francis turbine, the double models, real-time feedback linearization module, and a nonlinear characteristic compensator are designed, and a generalized predictive control strategy combining these modules (GPC-DM-RFLM-NCC) is proposed. Firstly, the salp swarm algorithm (SSA) is improved based on the chaotic mapping and Levy flight, an accurate nonlinear hydraulic turbine model is constructed based on the backpropagation neural network (BPNN) and improved SSA (ISSA), and an HTRS simulation platform with nonlinear characteristics is built by combining modules such as water diversion system, servo system, and generator. Then, the irrationality of the commonly used linear hydraulic turbine models in HTRS dynamic process simulation is verified through quantitative calculation. Furthermore, by combining the real-time feedback linearization method and ISSA-based nonlinear characteristic compensator, the GPC-DM-RFLM-NCC is proposed. Finally, the HTRS simulation platform, which consists of GPC, double models, real-time feedback linearization module, and nonlinear characteristic compensator, is built, and the applicability, reliability, and excellent robustness of the proposed GPC-DM-RFLM-NCC are verified by the real data of a hydropower station.

Experimental Assessment of Predictive Current Control Applied to Induction Machine Drive Systems Operating Under Single-Phase Open-Circuit Fault


 Predictive Current Control (PCC) has been widely applied in several applications. However, the literature has not discussed its use as a fault tolerance control algorithm in induction drive systems. In this way, this paper discusses the PCC method in two faulttolerant squirrel-cage induction machine drive systems operating under single-phase open-circuit faults. PCC’s postfault performance is compared to Field Oriented Control (FOC) for different steady- and transient-state scenarios, analyzing harmonic distortion, torque ripple, and the transition from healthy to postfault operation. Also, experiments tested the robustness of postfault PCC to low-speed operation, parametric variation, and a step change in reference rotor speed, showing that PCC also presents fault-tolerant operation under these conditions.

Flexible development and evaluation of machine‐learning‐supported optimal control and estimation methods via HILO‐MPC

AbstractModel‐based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used successfully for decades in many engineering applications. Models describing the dynamics, constraints, and desired performance criteria are fundamental to model‐based approaches. Thanks to recent technological advancements in digitalization, machine‐learning methods such as deep learning, and computing power, there has been an increasing interest in using machine learning methods alongside model‐based approaches for control and estimation. The number of new methods and theoretical findings using machine learning for model‐based control and optimization is increasing rapidly. However, there are no easy‐to‐use, flexible, and freely available open‐source tools that support the development and straightforward solution to these problems. This article outlines the basic ideas and principles behind an easy‐to‐use Python toolbox that allows to solve machine‐learning‐supported optimization, model predictive control, and estimation problems quickly and efficiently. The toolbox leverages state‐of‐the‐art machine learning libraries to train components used to define the problem. Machine learning can be used for a broad spectrum of problems, ranging from model predictive control for stabilization, set point tracking, path following, and trajectory tracking to moving horizon estimation and Kalman filtering. For linear systems, it enables quick generation of code for embedded model predictive control applications. HILO‐MPC is flexible and adaptable, making it especially suitable for research and fundamental development tasks. Due to its simplicity and numerous already implemented examples, it is also a powerful teaching tool. The usability is underlined, presenting a series of application examples.

Field-Weakening Strategy with Modulated Predictive Current Control Applied to Six-Phase Induction Machines

The predictive current controller has arisen as a practicable technique for operating multiphase machines due to its fast dynamic response, control flexibility, and overall good performance. However, this type of controller has limitations, e.g., it tends to suffer from steady-state tracking errors in (d−q) currents; high computational burden; and high (x−y) currents, which become more pronounced at higher speeds, thereby worsening its sustainability. While some proposals have addressed these limitations by incorporating modulation stages and new cost functions, there is still room for improvement, particularly at higher speeds. In line with the pursuit of sustainable advancements, this article explores the integration of a field-weakening strategy with a modulated predictive current controller applied to a six-phase induction machine to improve its performance at current tracking for higher speed ranges. Experimental tests were conducted to validate the effectiveness of the proposed controller, assessing stator current tracking, reduction in the (x−y) currents, and the total harmonic distortion.

Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment: An Overview of the Special Issue

This paper summarizes the results of a Special Issue focusing on the practical applications of model predictive control and other advanced control methods in the built environment. This Special Issue contains eleven publications and deals with various topics such as the virtual sensing of indoor air pollutants and prediction models for indoor air temperature and building heating and cooling loads, as well as local and supervisory control strategies. The last three publications tackle the predictive maintenance of chilled water systems. Most of these publications are field demonstrations of advanced control solutions or promising methodologies to facilitate the adoption of such control strategies, and they deal with existing buildings. The Special Issue also contains two review papers that provide a comprehensive overview of practical challenges, opportunities, and solutions to improve building operations. This article concludes with a discussion of the perspectives of advanced controls in the built environment and the increasing importance of data-driven solutions.

Multivariable robust tube-based nonlinear model predictive control of mammalian cell cultures

In this paper, the application of a robust nonlinear model predictive control (NMPC) framework to mammalian cell cultures is proposed, dealing with possible large kinetic parameter uncertainties. Industrial constraints formulated in view of good manufacturing practice and quality-by-design approach are also considered, namely the assurance that all state trajectories are contained within a corridor defined by lower and upper safety bounds. The latter are assimilated to the well-known tube-based paradigm which is used to formulate the corresponding robust NMPC problem. Both classical and tube-based NMPC performances are assessed in numerical simulations where specific key-species are regulated while dealing with an uncertain plant model. The capability of the tube-based method to reduce the impact of the parameter variations on the state trajectories and the violation of the constraints is highlighted, suggesting the transfer of the method on a real pharmaceutical process.

Decomposition of Equations of Nonlinear Affine Control Systems and its Application to the Synthesis of Regulators

The article deals with the decomposition of nonlinear differential equations based on the group-theoretic approach. At the beginning, the decomposition of differential equations of linear systems using a transition matrix of state is presented, and then, based on the theory of continuous groups (Lie groups), the process of decomposition of differential equations of nonlinear systems is shown. The decomposition approach is based on the isomorphism theorem of the space of vector fields and Lie derivatives, which allows us to consider vector fields as differential operators of smooth functions. A formula is derived about the adjoin representation of a Lie group in its Lie algebra, which actually determines the finding of a vector field that characterizes the interaction of two or more vector fields. The Lie algebra of derivatives makes it possible to determine the infinitesimal action of the Lie group, i.e. the linearization of this action is carried out (transformation of the points of the trajectory space of the original system in a small neighborhood). Decomposition allows, as in the linear case, to separate the finding of an action (only locally) of a group of transformations from the transformed points themselves. For linear systems, this separation is global. It is also shown that the decomposition of linear equations is a particular case of the decomposition of nonlinear equations. An algorithm of the method of model predictive control with Gramian weighting using this decomposition is presented. A practical example of decomposition and application of the model predictive control for stabilization of a nonstationary nonlinear system is considered.

Computationally Efficient Sphere Decoding Algorithm Based on Artificial Neural Networks for Long-Horizon FCS-MPC

Successful application of finite control set model predictive control (FCS-MPC) strategies with long prediction horizon depends on the careful design of the optimization algorithm. The conventional method involves transforming the problem to an equivalent box-constrained integer least-squares (BILS) formulation that can be solved with branch-and-bound techniques such as the sphere decoding algorithm (SDA). In this work, it is proposed to define an artificial neural network (ANN) to replace the SDA, avoiding its inherent computational variability. Similarly to practical applications of the SDA, the ANN finds an approximate solution of the underlying optimization problem. In contrast, the main benefit of the proposed approach is that it can be implemented in a low-cost microprocessing platform, greatly improving the performance in terms of resources in comparison with other advanced techniques proposed in the literature.

Practical Application of Model Predictive Zonal Temperature Estimation and Control for Vehicle Cabins

Practical Application of Model Predictive Zonal Temperature Estimation and Control for Vehicle Cabins

The Application of Adaptive Model Predictive Control for Fed-Batch Escherichia coli BL21 (DE3) Cultivation and Biosynthesis of Recombinant Proteins

A model predictive control (MPC) method was investigated as a route to optimize and control the growth of E. coli BL21 (DE3) and biosynthesis of two different recombinant proteins (nerve growth factor NGF and coat protein of bacteriophage Qβ (Qβ-CP)). To determine the target trajectory for the E. coli cultivation process and estimate the model parameters, the off-line run-to-run optimization method was used. The proven method allowed us to successfully control the growth of microbial biomass, with a deviation of 6–12% from the target trajectory. It was proven that it is possible to obtain a “Golden Batch” profile for the implementation of MPC using datasets from only four to eight fermentation runs. The method showed its robustness when the cultivation of E. coli was carried out with two different titrant supply control systems—volumetric and gravimetric. Furthermore, the MPC method exhibited high adaptability, reliability, and resistance to various types of disturbances. MPC proved to be a reliable and effective method for controlling the cultivation and recombinant protein biosynthesis of fast-growing microorganisms such as E. coli.

Thermal modeling for control applications of 60,000 homes in North America using smart thermostat data

As smart thermostats become increasingly available in residential buildings, there is an opportunity to use measured building data to calibrate models for community and district applications, instead of relying on high-fidelity simulations. This study used smart thermostat data from 60,000 houses in North America to create single-zone models. The model structure was defined with an automated forward selection procedure. 61% of the final models were classified as good fits and the structure of 80% of them was of 5th-order (5 thermal capacitances). An investigation of the 24-hour prediction error of the models showed that the ones classified as good fits are accurate enough for day-ahead predictions and Model Predictive Control (MPC) applications. An analysis of the model parameters suggested no strong correlation between them and the available metadata. The time constants of the houses were estimated, providing valuable information about the houses thermal inertia. Building models that can accurately capture and leverage building thermal inertia are ideal candidates for MPC and energy flexibility applications.

Multi-objective optimization of building HVAC operation: Advanced strategy using Koopman predictive control and deep learning

Predictive control is an effective method for addressing the increasing demand for heating, ventilation, and air conditioning (HVAC) systems to operate with greater flexibility and efficiency. In this paper, the multi-objective problem of energy-efficient HVAC operation while tracking desired setpoints is addressed through an advanced control strategy, including a novel supervisory data-driven predictive control (DPC) and a local loop with a PI controller. The supervisory level is based on the integration of the DPC framework and the bilinear Koopman predictor. A deep neural network architecture is developed to realize the bilinear building thermal predictor. In addition, the integral input-to-state stability (iISS) constraint is enforced in the training procedure to obtain a global iISS control-oriented thermal predictor that enables the application of predictive control purely based on historical data. The effectiveness of the proposed approach is demonstrated through the simulation of a three-zone office in the transient system simulation software (TRNSYS). The proposed control configuration is implemented using a coupled TRNSYS-MATLAB simulation framework. It has shown significant potential for keeping the desired environment in line with energy conservation. Furthermore, a comparison is conducted between the proposed approach, the conventional controller, and another recent DPC approach, which demonstrates the suggested method’s superiority.

Optimal Control of District Cooling Energy Plant With Reinforcement Learning and MPC

Abstract We consider the problem of optimal control of district cooling energy plants (DCEPs) consisting of multiple chillers, a cooling tower, and a thermal energy storage (TES), in the presence of time-varying electricity price. A straightforward application of model predictive control (MPC) requires solving a challenging mixed-integer nonlinear program (MINLP) because of the on/off of chillers and the complexity of the DCEP model. Reinforcement learning (RL) is an attractive alternative since its real-time control computation is much simpler. But designing an RL controller is challenging due to myriad design choices and computationally intensive training. In this paper, we propose an RL controller and an MPC controller for minimizing the electricity cost of a DCEP and compare them via simulations. The two controllers are designed to be comparable in terms of objective and information requirements. The RL controller uses a novel Q-learning algorithm that is based on least-squares policy iteration. We describe the design choices for the RL controller, including the choice of state space and basis functions, that are found to be effective. The proposed MPC controller does not need a mixed integer solver for implementation, but only a nonlinear program (NLP) solver. A rule-based baseline controller is also proposed to aid in comparison. Simulation results show that the proposed RL and MPC controllers achieve similar savings over the baseline controller, about 17%.