Advanced Model-based Control Research Articles

Model Predictive Control (MPC) of a Countercurrent Flow Plate Heat Exchanger in a Virtual Environment.

This research proposes advanced model-based control strategies for a countercurrent flow plate heat exchanger in a virtual environment. A virtual environment with visual and auditory effects is designed, which requires a mathematical model describing the real dynamics of the process; this allows parallel fluid movement in different directions with hot and cold temperatures at the outlet, incorporating control monitoring interfaces as communication links between the virtual heat exchanger and control applications. A multivariable and non-linear process like the plate and countercurrent flow heat exchanger requires analysis in the controller design; therefore, this work proposes and compares two control strategies to identify the best-performing one. The first controller is based on the inverse model of the plant, with linear algebra techniques and numerical methods; the second controller is a model predictive control (MPC), which presents optimal control actions that minimize the steady-state errors and aggressive variations in the actuators, respecting the temperature constraints and the operating limits, incorporating a predictive model of the plant. The controllers are tested for different setpoint changes and disturbances, determining that they are not overshot and that the MPC controller has the shortest settling time and lowest steady-state error.

Reinforcement learning based temperature control of a fermentation bioreactor for ethanol production.

Ethanol production is a significant industrial bioprocess for energy. The primary objective of this study is to control the process reactor temperature to get the desired product, that is, ethanol. Advanced model-based control systems face challenges due to model-process mismatch, but Reinforcement Learning (RL) is a class of machine learning which can help by allowing agents to learn policies directly from the environment. Hence a RL algorithm called twin delayed deep deterministic policy gradient (TD3) is employed. The control of reactor temperature is categorized into two categories namely unconstrained and constrained control approaches. The TD3 with various reward functions are tested on a nonlinear bioreactor model. The results are compared with existing popular RL algorithm, namely, deep deterministic policy gradient (DDPG) algorithm with a performance measure such as mean squared error (MSE). In the unconstrained control of the bioreactor, the TD3 based controller designed with the integral absolute error (IAE) reward yields a lower MSE of 0.22, whereas the DDPG produces an MSE of 0.29. Similarly, in the case of constrained controller, TD3 based controller designed with the IAE reward yields a lower MSE of 0.38, whereas DDPG produces an MSE of 0.48. In addition, the TD3 trained agent successfully rejects the disturbances, namely, input flow rate and inlet temperature in addition to a setpoint change with better performance metrics.

Model-based linear control of nonlinear pneumatic soft bending actuators

Advanced model-based control techniques hold great promise for the precise control of pneumatic soft bending actuators (PSBAs) with strong nonlinearities. However, most previous controllers were designed in a cumbersome nonlinear form. Considering the simplicity of linear system theory, this paper presents a novel perspective on using model-based linear control to handle nonlinear PSBAs, and for the first time, summarizes two methodologies, global linearization and pseudo-linear construction. Derived from them, Koopman-based and hysteresis-based linear control approaches are proposed, respectively. For the former, a novel fusion prediction equation is developed to build a high-fidelity Koopman model, realizing global linearization, and then the linear model predictive control (MPC) is deployed. For the latter, the inverse of the generalized Prandtl–Ishlinskii (GPI) model cascades with the PSBA to construct a pseudo-linear system, eliminating the asymmetric hysteresis, which activates the linear proportional-integral-derivative (PID) control. It is worth noting that the above two are based on data-driven models adapted to various PSBAs with material and structural customization. Finally, the two model-based linear control approaches are verified and compared through a series of experiments. The results show that the proposed linear controls, with more concise designs, achieve comparable or even superior performance than nonlinear controls.

On the applicability of advanced model-based strategies to control of electrified vehicle thermal systems

Electrified Vehicles (xEVs) often rely on complex thermal systems to meet energy efficiency and performance targets. These systems are typically made up of multiple interacting loops, such as coolant loop, oil loop, and refrigerant loop which can be highly nonlinear, strongly coupled, and teemed with multiple sources of uncertainties and disturbances. Designing effective control strategies for such complex thermal systems is a difficult and crucial task. This paper proposes three advanced Model-Based Control (MBC) strategies for the cabin active heating thermal system of an EV, namely, a Nonlinear Model Predictive Control (NMPC), a Model Predictive Control via Value Function Approximation (MPC-VFA), and a Linear-Quadratic Regulator (LQR). For this purpose, a physics-based model of the aforementioned thermal system is derived and validated using data generated from a high-fidelity thermal plant model created in GT-suite software. The parameters of the physics-based model are identified using the particle swarm optimization algorithm and it is shown that the nonlinear dynamics of the thermal system has been accurately captured. A comprehensive test study is performed and the performance of the proposed MBC approaches is evaluated using various indicators, such as reference tracking, energy consumption, robustness, computation time, and implementation complexity. Experimental data has been also utilized to validate the high-fidelity thermal plant model. The results of the test study demonstrate a high performance and efficiency of the proposed control strategies, offering significant advancements in thermal system control of Electrified Vehicles.

Model-based closed-loop control strategies for flex-fuel capability

Meeting future limits for greenhouse gas (GHG) and pollutant emissions represents major challenges for the further development of internal combustion engines for light and heavy-duty applications. To comply with increasingly higher standards, the use of renewable fuels is an important option due to their great potential in terms of energy density, sustainability and low-pollution combustion. However, the increased fuel diversity associated with their use increases the complexity of the powertrain development and calibration process, resulting in an increase in development time and cost. To address these challenges, advanced model-based closed-loop control strategies are applied to optimize the fuel- and air-path settings and make best use of the properties of the different fuels. One such approach is Combustion Rate Shaping (CRS). This ensures a closed-loop control of combustion parameters such as the indicated mean effective pressure, the center of heat release, and the combustion noise excitation. In addition, a model-based closed-loop NOx control could simplify the control on the air-path side. Such a closed-loop approach can further be combined with an onboard fuel detection algorithm and learning functionalities that eventually allow switching to optimal setpoints based on the fuel properties. This publication presents an evaluation of CRS as control concept with flex-fuel operation. The control strategy is implemented on a demonstrator vehicle built up with a Rapid Control Prototyping (RCP) system. The results of emission test cycles with conventional diesel fuel and later with a blend with 1- Octanol, OME3–5 and a paraffinic renewable diesel fuel are presented to illustrate the potential of the novel control concept. The developed CRS concept shows a reduced combustion parameters and emissions dispersion. In particular, the results with OME3–5 blend shows up to 40% reduction in NOx emissions and up to 1 dB(A) lower average combustion noise in comparison to conventional open-loop control. Moreover, an onboard fuel detection and an adaptive control functionality are proposed and experimental validations are carried out to demonstrate the potential to optimize engine emissions.

Studies on crystallization process for pharmaceutical compounds using ANN modeling and model based control

Solvent selection and Controlling of operating parameters play a crucial role in batch cooling crystallization process. Choosing a best solvent for crystallization process involves more experimentation and time. To overcome this problem, an Artificial Neural Network (ANN) model technique is used to predict the carbamazepine form Ⅲ solubility by considering the thermodynamic properties of different solvents i.e. critical temperature, critical pressure, temperature, molecular weight, and acentric factor. The ANN model was trained and evaluated for solubility at various input data sets using experimental solubility data available in the literature. The ANN model with 20 hidden neurons has given the R2 value of 0.9943 which shows that the developed ANN model can be used for the selection of best solvent for batch crystallization process. Further, to determine the optimal cooling profile of batch cooling crystallization process, a multi-objective optimization problem is formulated by considering objectives as minimizing the coefficient of variation (CV) and maximizing the Number mean size (NMS) of crystals subjected to population balance equations using “method of moments” technique. Two types of temperature strategies i.e., piece-wise constant and piece-wise linear are developed and solved using NSGA-Ⅱ dynamic optimization procedure. The optimal NMS value attained through piece-wise linear strategy was 197.1 µm. This value has been increased by 28.3 µm from the nominal case (without optimization) and the coefficient of variation has decreased from 0.951 to 0.76. Further, optimal NMS value attained through piece-wise constant strategy was 205 µm. The value has been increased by 36.2 µm and the coefficient of variation has decreased from 0.951 to 0.73. This proves that the crystal attributes can be improved by optimal cooling temperature profile obtained by multi-objective optimization framework. For implementing the optimal cooling profile an advanced model-based control, i.e., Generic Model Control (GMC) was developed. It was observed that the GMC controller has the good tracking profile with no offset with/without disturbances and small value of root mean square error (RMSE) of 0.0016 using piece-wise constant as set point temperature. Using piece-wise linear as set point temperature, the RMSE value was 0.0018. In particular, it is advantageous to operate the batch cooling crystallization process with piece-wise linear strategy for set point trajectory tracking problems.

Methodology for fast development of digital solutions in integrated continuous downstream processing.

The methodology for production of biologics is going through a paradigm shift from batch-wise operation to continuous production. Lot of efforts are focused on integration, intensification, and continuous operation for decreased foot-print, material, equipment, and increased productivity and product quality. These integrated continuous processes with on-line analytics become complex processes, which requires automation, monitoring, and control of the operation, even unmanned or remote, which means bioprocesses with high level of automation or even autonomous capabilities. The development of these digital solutions becomes an important part of the process development and needs to be assessed early in the development chain. This work discusses a platform that allows fast development, advanced studies, and validation of digital solutions for integrated continuous downstream processes. It uses an open, flexible, and extendable real-time supervisory controller, called Orbit, developed in Python. Orbit makes it possible to communicate with a set of different physical setups and on the same time perform real-time execution. Integrated continuous processing often implies parallel operation of several setups and network of Orbit controllers makes it possible to synchronize complex process system. Data handling, storage, and analysis are important properties for handling heterogeneous and asynchronous data generated in complex downstream systems. Digital twin applications, such as advanced model-based and plant-wide monitoring and control, are exemplified using computational extensions in Orbit, exploiting data and models. Examples of novel digital solutions in integrated downstream processes are automatic operation parameter optimization, Kalman filter monitoring, and model-based batch-to-batch control.

On System Identification of Space Manipulator Systems Including Their Fuel Sloshing Effects

During maneuvers of a space manipulator system (SMS), thruster fuel in its spacecraft (S/C) results in sloshing effects which impact its dynamics and performance. In this work, sloshing effects are modeled by a 3D mass-spring-damper. A novel system identification scheme is developed, which identifies all parameters required for the reconstruction of system dynamics despite the unmeasurable sloshing states. This is achieved by two identification experiments. In Exp. 1, the SMS translates with locked joints allowing for the elimination of the unmeasurable sloshing states. As a result, all sloshing model parameters and the mass of the SMS base are identified. In Exp. 2, the SMS operates in free-floating mode, with the reaction wheels (RWs) and the manipulator(s) active. Based on kinematics and conservation of momentum, a method is developed which eliminates the unmeasurable sloshing states and identifies all the required SMS inertial parameters. The estimated parameters render the system dynamics fully identified and available to advanced model-based control algorithms. The effect of sloshing parameter errors is also discussed. The developed scheme is validated by simulation and experiments.

Investigation of control performance on an absorption/stripping system to remove CO2 achieving clean energy systems

Control study is crucial to ensure smooth operation of any unit operation for clean energy system. There has been growing attention on the utilization of advanced model-based controllers in control studies for CO2 capture. However, such advanced controllers are expensive hence proper performance justifications are needed prior to investment. In the current study, both Proportional-Integral (PI) and Model Predictive Control (MPC) control strategies are investigated and implemented on a pilot scale natural gas CO2 absorption/ stripping system running at higher pressure as well as higher CO2 content, utilizing MATLAB/SIMULINK-Aspen Plus Dynamics interface. The novelty of the work is mainly on the investigation study between the efficiency of the standard PI and MPC controllers for a high pressure and high CO2 content pilot-scale CO2 absorption system. For such high pressure and high CO2 content system, it is imperative to evaluate whether special focus on the control strategy is needed or not. This work simulates and investigates the dynamic behavior and performances of PI and MPC controllers of a high-pressure absorption/stripping system. In addition, a second order model of the system has been developed, which is useful for future studies. Controller performance of the Proportional-Integral (PI) and Model Predictive Controller (MPC) are compared using the features of the responses as well as the residual error index i.e., Integral Absolute Error (IAE). The performance of the PID and MPC controllers based on set point tracking of both the controlled variables for 5% and 15% step changes have been evaluated. Compared to the PI controller, the MPC controller has no peak overshoots, i.e., it shows 10% and 2.86% lower error in the IAE index, and it is capable of handling interactions between the variables of the absorption/stripping system.

Advanced model-based closed-loop combustion control strategies with combustion rate shaping

Combustion rate shaping (CRS) is an advanced closed-loop concept which controls the crank-angle resolved combustion trace by adapting the fuel injection profile. The thermodynamic potential of this concept to enable real-time combustion control independent of fuel property variations, injector drift, cylinder tolerances, or changing air-path boundary conditions has already been demonstrated. The highest benefit of this control concept is achieved when it is directly applied on the engine control unit (ECU) in combination with an in-cylinder pressure measurement. This enables online optimization and adaption of the fuel- and air-path settings to the fuel used during vehicle operation. Therefore, the development of a CRS concept with real-time capability has been the main focus so far. To ensure a robust online real-time combustion control, a key development step was the identification of control variables that describe the entire combustion process and only minimally influence each other. For this purpose, the combustion phasing, the combustion gradient and the indicated mean effective pressure have been determined as parameters for the feedback control. However, the stringent requirements for higher efficiencies, emission robustness and combustion noise necessitate the extension of the CRS control concept. Therefore, in this work, the CRS control concept is extended with the aim of realizing a combustion with constant volume pressure increase to the peak firing pressure limit to achieve highest efficiency. In addition, intelligent control of the fuel injection profile is to be used to achieve a combustion temperature for lowest possible NOx raw emissions and a robust combustion noise excitation limitation. The optimized control strategy is implemented on a heavy-duty single cylinder engine test bench using a Rapid Control Prototyping (RCP) system, and the control performance is demonstrated.

TASAC: A twin-actor reinforcement learning framework with a stochastic policy with an application to batch process control

Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes pose a challenge for process control. Due to the absence of accurate models and resulting plant-model mismatch, these problems become harder to address for advanced model-based control strategies. Reinforcement Learning (RL), wherein an agent learns the policy by directly interacting with the environment, offers a potential alternative in this context. RL frameworks with actor–critic architecture have recently become popular for controlling systems where state and action spaces are continuous. The current study proposes a stochastic actor–critic RL algorithm, termed Twin Actor Soft Actor–Critic (TASAC), by incorporating an ensemble of actors in a maximum entropy framework to improve learning due to enhanced exploration. The efficacy of the proposed approach is showcased by applying the same for the control of batch transesterification.

Control‐oriented estimation of the exchange current density in PEM fuel cells via stochastic filtering

Increasing efficiency and durability of fuel cells can be achieved through advanced model-based optimal control of its operating conditions, and the efficient online estimation of fuel cell parameters and internal states is fundamental for the implementation of such advanced controllers. The exchange current density is a driving parameter of performance for the catalyst layer of proton exchange membrane fuel cells (PEMFC). This study presents a control-oriented, stochastic filtering approach for online, continuous estimation of the exchange current density in low-temperature PEMFCs. The fuel cell is framed as a Markov model where the exchange current density is posed as the stochastic hidden state. The physics-based static equation of the exchange current density is converted into a state transition equation. This transition equation and the equation for cell voltage are used in the stochastic state estimator to approximate the posterior probability distribution of the exchange current density. In order to highlight the usefulness of the approach, the estimated value of the exchange current density is used to approximate the trend of the electrochemical active surface area (ECSA) in the catalyst layer and train a nonlinear auto-regressive model. This data-driven model is used to forecast the evolution in the ECSA associated with long-term degradation. The estimation algorithm is successfully implemented and tested in two different experimental datasets.

Design, Characterization, and Dynamic Simulation of the MAHI Open Exoskeleton Upper Limb Robot

Stroke and spinal cord injury are becoming ever more prevalent in the United States. Recent research has shown that rehabilitation robots have the potential to positively impact the rehabilitation process by providing a platform for repetition-based movement therapy. To advance the field, future research directions for robotic rehabilitation are focused on advanced model-based control algorithms, and the combination of robotics with cutting-edge neuromodulation technologies. These approaches necessitate devices that not only feature kinematic and dynamic properties well-suited for model-based control, but also require devices that allow for easy placement of sensors and electrodes on the limb when inserted in the robot. With these design goals in mind, we present the MAHI Open Exoskeleton, a four-degree-of-freedom robot with an open mechanical structure and simplified dynamics, combined with open-source software, that together lay the groundwork for advanced model-based control. The dynamic properties of each joint were characterized and compared against other recently developed rehabilitation robots. Open-source software was developed for the robot, which provides users with both low-level and application-level interfaces to implement a variety of control strategies. Dynamic equations were developed and implemented into a real-time simulation with a visualization, including a seamless interface to the developed software library. Impedance control and model predictive control were implemented and compared to the simulation, proving the value of the new designs.

Combustion rate shaping for flex-fuel applications

Compliance with future greenhouse gas (GHG) and pollutant emissions poses major challenges for the further development of advanced internal combustion engines for light and heavy-duty applications. To meet all standards, the use of synthetic fuels and/or E-Fuels is an important alternative due to their enormous potential in terms of energy density, sustainability and low pollution combustion. However, the increased fuel diversity associated with the use of these alternative fuels adds an additional layer of complexity to the powertrain development and calibration process, resulting in an increase in development time and cost. This necessitates the application of advanced model-based closed-loop control strategies to optimize the fuel- and air-path calibration to make best use of the properties of the different fuels. One potential solution to address the impact of flex-fuel variances on the combustion process is the Combustion Rate Shaping (CRS) concept presented in this paper. It can maintain a desired combustion trace irrespective of fuel variations, hardware drifts and other external factors, thus ensuring optimal combustion. The highest benefit of this control concept comes from its application directly on the engine control unit (ECU) in combination with an in-cylinder measurement. This enables the online real-time control of the combustion, regardless of which fuel is being used. In addition, this also enables optimization and adaption of the fuel- and air-path settings to the used fuel while driving the vehicle. However, so far, the high computational cost of this control concept limits its real-time capability. Therefore, the optimization of the control concept to achieve real-time capability is the focus of this paper. The optimized control strategy is implemented on a demonstrator engine test bench built using a Rapid Control Prototyping (RCP) system. The controller performance is demonstrated both under steady-state and transient operating conditions, and the potential of the concept is demonstrated in several cases such as engine temperature variations, injector drift, individual cylinder balancing, and fuel-variation.

Full dynamic model of 3-UPU translational parallel manipulator for model-based control schemes

AbstractOptimal mechanical design, model-based control, and robot dynamic calibration mainly rely on the analytical formulation of robot dynamics. In this paper, the kinematics equations of a general 3-UPU translational parallel manipulator (TPM) are derived, and then, by using the principle of the virtual work theorem, the full implicit dynamic model is derived. Furthermore, by making some modifications, the explicit dynamic formulation of the robot is attained, which is the basis of a wide range of advanced model-based controllers. To validate the proposed formulation, a prototype of the 3-UPU TPM is modeled in MSC-ADAMS® software, and the results of the dynamic formulation are validated using this model. The results show the high accuracy of the proposed dynamic formulation presented in this article.

Advanced Model-Based Estimation and Control of Alumina Concentration in an Aluminum Reduction Cell

Advanced Model-Based Estimation and Control of Alumina Concentration in an Aluminum Reduction Cell

Data-driven Models for Advanced Control of Acid Gas Treatment in Waste-to-energy Plants

This paper presents a study of identification and validation of data-driven models for the description of the acid gas treatment process, a key step of flue gas cleaning in waste-to-energy plants. The acid gas removal line of an Italian plant, based on the injection of hydrated lime, Ca(OH)2, for the abatement of hydrogen chloride, HCl, is investigated. The final goal is to minimize the feed rate of reactant needed to achieve the required HCl removal performance, also reducing as a consequence the production of solid process residues. Process data are collected during dedicated plant tests carried out by imposing Generalized Binary Noise (GBN) sequences to the flow rate of Ca(OH)2. Various input-output and state-space models are identified with success, and related model orders are optimized. The models are then validated on different datasets of routine plant operation. The proposed modeling approach appears reliable and promising for control purposes, once implemented into advanced model-based control structures.

Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments

The control of modern buildings is a complex multi-loop problem due to the integration of renewable energy generation, storage devices, and electric vehicles (EVs). Additionally, it is a complex multi-criteria problem due to the need to optimize overall energy use while satisfying users’ comfort. Both conventional rule-based (RB) controllers, which are difficult to apply in multi-loop settings, and advanced model-based controllers, which require an accurate building model, cannot fulfil the requirements of the building automation industry to solve this problem optimally at low development and commissioning costs. This work presents a fully data-driven pipeline to obtain an optimal control policy from historical building and weather data, thus avoiding the need for complex physics-based modelling. We demonstrate the potential of this method by jointly controlling a room temperature and an EV to minimize the cost of electricity while retaining the comfort of the occupants. We model the room temperature with a recurrent neural network and use it as a simulation environment to learn a deep reinforcement learning (DRL) control policy. It achieves on average 17% energy savings and 19% better comfort satisfaction than a standard RB room temperature controller. When a bidirectional EV is connected additionally and a two-tariff electricity pricing is applied, it successfully leverages the battery and decreases the overall cost of electricity. Finally, we deployed it on a real building, where it achieved up to 30% energy savings while maintaining similar comfort levels compared to a conventional RB room temperature controller.

Multi-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering

For efficient operation, modern control approaches for biochemical process engineering require information on the states of the process such as temperature, humidity or chemical composition. Those measurement are gathered from a set of sensors which differ with respect to sampling rates and measurement quality. Furthermore, for biochemical processes in particular, analysis of physical samples is necessary, e.g., to infer cellular composition resulting in delayed information. As an alternative for the use of this delayed measurement for control, so-called soft-sensor approaches can be used to fuse delayed multirate measurements with the help of a mathematical process model and provide information on the current state of the process. In this manuscript we present a complete methodology based on cascaded unscented Kalman filters for state estimation from delayed and multi-rate measurements. The approach is demonstrated for two examples, an exothermic chemical reactor and a recently developed model for biopolymer production. The results indicate that the the current state of the systems can be accurately reconstructed and therefore represent a promising tool for further application in advanced model-based control not only of the considered processes but also of related processes.

A dynamic flotation model to infer process characteristics from online measurements

A dynamic flotation model incorporating fundamental and phenomenological relationships, information from froth images and steady-state models is described. Model outputs correspond with online measurements commonly available on flotation circuits, and the model parameters are estimated from industrial data. Simulation results are presented, highlighting important non-linearities that need to be taken into account for optimal flotation operation. Observability and controllability analyses are performed, proving that key flotation parameters can theoretically be estimated from online process measurements, and that the set of modelled inputs can control all the model outputs. This model can be used in advanced model-based control and optimisation applications. The ability to estimate key flotation parameters opens up opportunities for improved optimisation of operating variables such as aeration rates, froth depth and the reagent recipe.