On-line Control Strategy Research Articles

Study of the Optimal Control of the Central Air Conditioning Cooling Water System for a Deep Subway Station in Chongqing

Cooling water, a crucial component of the central air conditioning setup, exerts a relatively minor direct impact on the thermal comfort of building indoor environments while it has a great effect on the system’s energy efficiency. Numerous studies exist on the cooling water system, particularly focusing on the process by which the cooling tower system operates, but the linkage between the chiller and the cooling tower is typically overlooked. When the connection is long and the passage environment for the pipeline is not conventional, it cannot be neglected for the optimal control for system efficiency improvement and energy consumption reductions. Throughout this research, a control strategy of the cooling water system for deep subway stations with long pipelines is presented. This cooling system was connected with outdoor cooling towers through a corridor about one hundred meters long. In this process, the cooling water temperature is influenced by the corridor’s thermal environment. For this study, an online control strategy optimizes the cooling water temperature, and a simulation platform of the air conditioning cooling water system of the deep subway station was also developed to evaluate the energy-saving potential of the control strategy of this cooling water system. Atop this platform, a simplified heat transfer model of the pipe corridor was created to determine the cooling capacity provided by the cooling water pipe in the corridor. The outcomes suggest that, as opposed to the conventional control mode, the energy-saving ratio of the optimal control strategy during a typical day may reach 4.1%, and the cooling source system’s Coefficient of Performance (COP) might see an increase of about 4.2%. The energy consumption of the water system throughout the whole cooling season may decrease by 9778 kWh, and the energy-saving rate is 4.1%. The results also demonstrate that the cooling water pipes release heat to the air in the corridor most of the time, and the released heat is larger than the absorbed heat. The maximum heat dissipation to the air in the corridor from the cooling water supply and return pipe can be up to 24.3 kW. The cooling effect of the corridor of subway stations with large depths below the ground surface cannot be ignored when optimal control is considered for the cooling water system.

Precise regulating the specific oxygen consumption rate to strengthen the CoQ10 biosynthesis by Rhodobater sphaeroides

Coenzyme Q10 (CoQ10) is the most consumed dietary supplement and mainly biosynthesized by aerobic fermentation of Rhodobacter sphaeroides (R. sphaeroides). Oxygen supply was identified as a bottleneck for improving CoQ10 yield in R. sphaeroides. In this study, a precise regulation strategy based on dielectric spectroscopy (DS) was applied to further improve CoQ10 biosynthesis by R. sphaeroide. First, a quantitative response model among viable cells, cell morphology, and oxygen uptake rate (OUR) was established. DS could be used to detect viable R. sphaeroides cells, and the relationship among cell morphology, CoQ10 biosynthesis, and OUR was found to be significant. Based on this model, the online specific oxygen consumption rate (QO2) control strategy was successfully applied to the CoQ10 fermentation process. QO2 controlled at 0.07 ± 0.01 × 10− 7mmol/cell/h was most favorable for CoQ10 biosynthesis, resulting in a 28.3% increase in CoQ10 production. Based on the multi-parameters analysis and online QO2 control, a precise online nutrient feeding strategy was established using conductivity detected by DS. CoQ10 production was improved by 35%, reaching 3384 mg/L in 50 L bioreactors. This online control strategy would be effectively applied for improving industrial CoQ10 production, and the precise fermentation control strategy could also be applied to other fermentation process.

A comparative analysis of intelligent energy modelling approaches for a grid-tied PVT system application

Renewable energy resources are a long-term answer to the current chronic energy dilemma. Solar photovoltaic (PV) systems are the most often used form of a practical solution for power supply and energy management in buildings. A PV-thermal (PVT) system is essential to lowering the demand for grid energy by capturing electrical and thermal energy from sunlight, optimising energy usage and encouraging sustainable energy practices. The most significant difficulty faced by developing countries, such as South Africa, is a lack of power supply to meet demand while limiting grid overload. An alternative source of energy is a critical component. This research compares two intelligent energy modelling methodologies: optimal control scheme-based open loop and model predictive control (MPC) in a PVT application. The primary goal of the modelling is to limit the electricity required from the grid while stressing the system’s energy efficiency and cost-effectiveness. The open loop approach uses mathematical optimisation techniques to establish the best control strategy for the PVT system. The ideal set-points for various system parameters are found by presenting the problem as an optimisation task to reduce grid power usage. The optimal control technique delivers a global optimisation solution based on the system dynamics and constraints. The MPC technique employs a predictive control technique of the PVT system to create optimal control actions in a receding horizon manner. The MPC algorithm anticipates future system behaviour and generates control actions that minimise grid power drawn by iteratively solving an optimisation problem at each time step. This robust online control scheme enables real-time modifications and responses to system disruptions that effectively and dynamically coordinate system behaviour.

Model-predicted effect of radial flux distribution on oxygen and glucose pericellular concentration in constructs cultured in axisymmetric radial-flow packed-bed bioreactors

Radial flow packed-bed bioreactors (rPBBs) overcome the transport limitations of static and axial-flow perfusion bioreactors and enable development of clinical-scale bioengineered tissues. We developed criteria to design rPBBs with uniform medium radial flux distribution along bioreactor length ensuring uniform construct perfusion. We report a model-based analysis of the effect of non-uniform axial distribution of medium radial flux on pericellular concentration of oxygen and glucose. Albeit pseudo-homogeneous, the model predicts how medium flux, solutes transport and cellular consumption interact and determine the pericellular oxygen and glucose concentrations in the presence of pore transport resistance to design optimal axisymmetric rPBBs and enable control of pericellular environment. Thus, oxygen and glucose supply may match cell requirements as tissue matures. Flow and solute transport in bioreactor empty spaces and construct was described with Navier-Stokes and Darcy-Brinkman equations, and with convection–diffusion and convection–diffusion-reaction equations, respectively. Solute transport in construct accounted for Michaelian cellular consumption and bulk medium-to-cell surface oxygen transport resistance in terms of a transport-equivalent bed of Raschig rings. The effect of relevant dimensionless groups on pericellular and bulk solute concentrations was predicted under typical tissue engineering operation and evaluated against literature data for bone tissue engineering. Axial distribution of medium radial flux influenced the distribution of pericellular solutes concentration, more so at high cell metabolic activity. Increasing medium feed flow rates relieved non-uniform solute concentration distribution and decayed at cell surface for metabolic consumption, also starting from axially non-uniform radial flux distribution. Model predictions were obtained in runtimes compatible with on-line control strategies.

Research of on-line monitoring technology and control strategy for laser-directed energy deposition: a review

Research of on-line monitoring technology and control strategy for laser-directed energy deposition: a review

Discrete Dynamic System Modeling for Simulated Moving Bed Processes

Although the simulated moving bed (SMB) process boasts advantages such as high productivity and low consumption, the cost of obtaining optimized parameters through practical experiments to control the separation process can be enormous due to its complex nonlinear characteristics. Consequently, the successful transformation of the SMB separation process into a mathematical dynamic model for computer simulation would greatly reduce the research costs associated with experimental studies. In this study, the Crank–Nicolson method was employed to discretize and dynamize the SMB process, enabling the simulation of processes under both linear and Langmuir isotherms. The results of the simulation experiments demonstrated the feasibility and high efficiency of this approach, thereby establishing a solid foundation for further advancements in online control strategies.

Utilizing two models of the converter to eliminate the partial shading on solar panel: a comparative scheme between classical offline and advanced online control strategies

Utilizing two models of the converter to eliminate the partial shading on solar panel: a comparative scheme between classical offline and advanced online control strategies

Collaborative strategy research of target tracking based on natural intelligence by UAV swarm

Regarding the regional area target collaborative tracking problem widely existing in intelligent scenarios, this paper built a distributed UAV swarm framework inspired by natural intelligence to heighten intricate missions’ efficiency. Also, a standoff collaboratively continuous tracking strategy was proposed based on a lateral guidance law with an improved Reference Point Guidance (RPG) and a longitudinal guidance law with an improved phase collaboration. Under an uncertain environment, this framework used an improved bat algorithm (IBA) to optimize the speed allocation of the UAV swarm’s online control strategy with information consensus estimation. Compared with a case without the designed transformation, statistically, the results demonstrate that the framework operates efficiently and robustly in phase error convergence, swarm flight distance, and fuel consumption, where a dynamic target exists.

Reflections on good manufacturing practice and quality control system of personalized traditional Chinese medicine preparations

Personalized traditional Chinese medicine(TCM) preparations have entered a stage of rapid development. The key to the healthy development of this industry is to establish a sound manufacturing standard and quality control system. This paper analyzed the characteristics of personalized TCM preparations and drew reference from the quality management standards in the production of commissioned decoctions and oral pastes, on the basis of which the production quality management scheme and cautions for the safe production of personalized TCM preparations was put forward with consideration to various problems that may exist and occur in the production of such preparations. It provided references for formulating the production standards and quality management system of personalized TCM preparations. The production standards and quality control system should develop with the times. In the future, modern technologies such as big data and artificial intelligence should be employed to achieve the automated and intelligent production and establish a sound quality traceability system, online control strategy, and safety management mode of personalized TCM preparations, which will ensure the healthy development of this industry under requirement of good manufacturing practice(GMP).

Admissible Control Laws for Constrained Linear Power Flow: The General Case

Linearized power flow with line flow and voltage constraints can be modeled as a system of linear inequalities depending on the power injections. When some injections are controlled by the grid operator while others are determined exogenously, robust control aims at determining grid operator's actions under imperfect system observability such that the grid state is feasible for all possible realizations of the exogenous actions. It was shown how to design and analyze such admissible control policies efficiently for the subclass of affine control laws, but this paper shows that there are important cases that require more general control policies. We show that, for the constrained linear power flow setting, general admissible control policies can always be chosen as piecewise affine (PWA) mappings. The PWA mapping can be explicitly characterized offline or control actions can be computed online by solving an optimization problem given a current observation. For the latter setup, we provide an algorithm that verifies offline that the online control scheme is admissible. This verification step is a crucial precondition to apply the online control scheme in real, safety-critical power grids. The proposed framework is demonstrated with applications to voltage regulation in active distribution grids subject to uncertain prosumers and low observability.

Boundedness and liveness enforcement for labeled Petri nets using transition priority

This paper deals with the supervisory control problem of discrete event systems modeled by labeled Petri nets. The system is originally unbounded. First, the solvability of the problem is confirmed. A necessary condition is given and proven for the existence of a feasible priority-based controller based on the notions of liveness and transition invariants. Next, a cyclic behavior graph is constructed, which shows the reachable markings that guarantee the maximum liveness of the system within a given bound vector. Finally, an on-line control strategy is proposed to enforce boundedness and liveness to the given system by appending priority relations to transitions. The dynamic priority relation changes flexibly according to the current state of the system and enforces the system evolving in a bounded and live manner. In addition, numerical examples are studied to verify the validity of the proposed approach that remains the structure of the plant net and is efficient for on-line control.

Life extension of a multi-unit energy storage system by optimizing the power distribution based on the degradation ratio

Battery energy storage systems are widely used to absorb renewable energy. However, the difference in the initial state and operating conditions led to inconsistent degradation between the battery units. It is urgent to develop life extension algorithms to solve the problem. In this study, a calculation scheme is proposed for the power distribution toward an optimized cycle life. First, the degradation ratio between the energy storage units was calculated based on the Arrhenius degradation model validated by aging experiments. A decisive correlation was revealed between the current rate and the degradation ratios using Pearson correlation analysis. Next, a simplified calculation method was proposed toward various operating conditions, which proved to acceptable errors less than 3 %. It was inferred that the degradation ratio could be directly controlled by the current rate ratio. Based on this concept, the degradation path was optimized based on genetic algorithm, to obtain the optimized power distribution factor of the entire life. The results showed that the cycle life could be extended by 21.9 % after separately adjusting the power distribution with 4-stage optimization. The study has effectively extended the service life of energy storage, which helps to develop the on-line control strategy toward life extension.

Research on power fluctuation strategy of hybrid energy storage to suppress wind-photovoltaic hybrid power system

In this paper, an adaptive hybrid energy storage power optimal allocation strategy is proposed. The strategy aims to suppress the fluctuation of grid-connected power under different operation conditions and improve the operation reliability of a high proportion of renewable energy connected to the grid. Firstly, an online control strategy of grid-connected power fluctuation rate based on model predictive control (MPC) is established. This strategy can realize the grid-connected target power dynamic generation of wind-photovoltaic-energy storage (Wind-PV-ES) hybrid power system and the optimal allocation of energy storage (ES) output power. Secondly, an adaptive hybrid ES power allocation strategy based on the variational mode decomposition (VMD), Hilbert-Huang transform (HHT) and filtering algorithm is constructed to realize adaptive adjustment and optimal allocation of hybrid ES output power. Finally, the hybrid ES capacity configuration is verified by simulation analysis and theoretical calculation. The simulation results show that the proposed hybrid ES output power optimal allocation strategy has certain engineering practical value to suppress the grid-connected power fluctuation.

Artificial Intelligence-Based Online Control Scheme for the Regulations of Interconnected Thermal Power Systems

Artificial Intelligence-Based Online Control Scheme for the Regulations of Interconnected Thermal Power Systems

Neural network-based iterative learning control of a piezo-driven nanopositioning stage

The piezo-driven nanopositioning stage (PNS) is a key device to provide fast and precise motions for applications such as micromanipulation, microfabrication, and microscopy scanning. However, inherent nonlinearities associated with system perturbations bring difficulties to controller design. Regarding repetitive tasks for a PNS, existing control schemes are mainly dedicated to model inversion-based iterative learning control (ILC), which relies heavily on model accuracy. In this paper, a novel online identification and control scheme named neural network-based ILC (NN-ILC) is proposed for repetitive tracking of the PNS. The ILC scheme reduces repetitive errors due to the linear dynamics and invariable disturbance during each iteration. Neural networks are integrated into the ILC scheme to minimize the residual non-repetitive errors resulting from unknown nonlinear dynamics and model perturbations. Convergence results in both the time and iteration domains are demonstrated according to the Lyapunov stability theory. Comprehensive experiments of sinusoidal and triangular tracking references with different frequencies (5∼20 Hz) and different peak-to-peak amplitudes (5∼20μm) are conducted on a real-time control testbed. Results show that the root mean square error of the proposed NN-ILC for 20 μm tracking cases is improved by up to 37% from feedback proportional-derivative (PD) control with neural networks and by up to 20% from feedforward PD-type ILC.

Online Control Strategy for Tolerating Resistance Asymmetry With Minimum Copper Loss in the Full Torque Range for Symmetrical Six-Phase AC Drives

Multiphase drives exhibit remarkable advantages (e.g., fault tolerance) over three-phase ones. Six-phase drives are particularly attractive, given their moderate complexity and suitability for off-the-shelf three-phase converters. Regarding the stator winding arrangement, the symmetrical one offers superior postfault capabilities in most scenarios. On the other hand, resistance asymmetry in the stator phases or connections may arise due to different causes. The conventional full-range minimum-loss strategy (FRMLS) generates stator-current references under open-phase (infinite resistance) faults so that torque ripple is prevented while minimizing the losses for each torque ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{d}$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{q}$</tex-math></inline-formula> current) value and maximizing the torque range; however, this method is unsuitable for unequal resistances of finite value. This article proposes an FRMLS for setting the current references to reach these goals in symmetrical six-phase drives with any resistance asymmetry. The optimum references are found online depending on the resistances, without lookup tables. The phase currents are individually limited by an iterative algorithm, so that minimum stator copper loss (SCL) is achieved over the maximum admissible torque range. In this manner, unlike the conventional FRMLS, minimum SCL and maximum torque range are attained even for finite resistance imbalance. The currents in phases affected by high resistances are suitably reduced. Experimental results are provided.

Online geometry assurance in individualized production by feedback control and model calibration of digital twins

In this paper, we consider online calibration of a Digital Twin and its use for control and optimization in the assembly process of sheet metal parts. This calibration is done based on a feedback signal received by calculating the quality of the simulated assemblies as compared to the prediction made by the Digital Twin.We develop a Kalman filter-based approach for online calibration of the Digital Twin, which in turn is used by a one-step look-ahead optimizer to define an online control scheme. This control scheme balances directly predicted quality gains against reduced uncertainty whose purpose is to enable long-term quality gains.The usage of a calibrated model in a one-step look-ahead optimizer as a controller allows to incorporate the benefits of the usage of Digital Twins for individualized control, where the control parameters of a production cell are optimized in a Digital Twin based on measured properties of the production inputs, over nominal control, where control parameters are set with respect to some reference production inputs, in an approach which is able to use measured final production quality for feedback control.The proposed approach is evaluated by computer simulations of two industrial product assembly use cases. In the first case, it demonstrates significant gains in quality of the produced assemblies, while in the second case it shows negligible to small improvements. The second case is, however, rather insensitive to miscalibration, which enables only small gains.

Chance-constrained model predictive control-based operation management of more-electric aircraft using energy storage systems under uncertainty

On more electric aircraft (MEA), reducing fuel consumption and guaranteeing flight safety are pursued by efficient operational management of the electrical power system (EPS). Considering the growing number of onboard electric loads and the increasing complexity of EPS architecture due to the integration of multiple power converters and energy storage systems (ESSs), system-level operation control is required to manage power distribution, load scheduling, and ESSs. In this paper, a chance-constrained stochastic model predictive control (CC-SMPC) method is proposed to improve both the system operation in terms of the system's cost and reconfiguration activities as well as the ability to cope with uncertainties due to fluctuating load demands. Both normal and faulty operating conditions are investigated with multi-failure cases, resulting in different uncertainty propagation paths. The system's operational and technical requirements are formulated as a set of deterministic and probabilistic constraints in the CC-SMPC model. To verify the effectiveness of the proposed strategy, a comprehensive comparison study is conducted. Two uncertainty/failure cases are taken into account and simulations are performed for both offline and online control strategies while the Monte-Carlo algorithm is used for scenario generation. The results are evaluated using the proposed evaluation framework, showing that the CC-SMPC achieves better performance compared to deterministic MPC (DMPC) in both cases. In an offline testing framework, comparing the performance of DMPC and CC-SMPC strategies shows that CC-SMPC reduces the power constraint violations for batteries and generators in all cases following the selected confidence level. In addition, in an online testing framework with 1 % violation probability, the following results are observed in the two cases: In the EPS normal condition, CC-SMPC reduces the total cost by 31.4 % and the overall constraint violation cost by 93 %; while in the EPS faulty condition, CC-SMPC reduces the total cost by 4.37 %, and the overall constraint violation cost by 96 %.

Battery Selection and Optimal Energy Management for a Range-Extended Electric Delivery Truck

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Delivery trucks and vans represent a growing transportation segment which reflects the shift of consumers towards on-line shopping and on-demand delivery. Therefore, electrification of this class of vehicles is going to play a major role in the decarbonization of the transportation sector and in the transition to a sustainable mobility system. Hybrid electric vehicles can represent a medium-term solution and have gained an increasing share of the market in recent years. These vehicles include two power sources, typically an internal combustion engine and a battery, which gives more degrees of freedom when controlling the powertrain to satisfy the power request at the wheels. Components sizing and powertrain energy management are strongly coupled and can make a substantial impact on the final energy consumption of a hybrid vehicle. An optimal control problem can be formulated and solved off-line, to evaluate the most energy efficient utilization of fuel and battery energy for the specific vehicle design. These off-line solutions can be used as benchmarks to compare different architectures and design options, and rules can be derived from them to design on-line implementable heuristic control strategies. In this work a plug-in series architecture with a range-extender engine is considered for a Class 6 beverage delivery truck and the optimal control problem is solved using dynamic programming. To study the strong coupling that exists between components sizing and energy management a simulation design space exploration is performed with four different cell chemistries and three different battery pack sizes. A drive cycle with variable payload, which is representative of delivery operations, is developed and used as a test case. The results show how the selection of the cell chemistry and the constraints on the battery weight can affect the battery performance and the impact on the optimal energy management strategy.&lt;/div&gt;&lt;/div&gt;

Online Control Strategy for Radial Vibration Suppression of PMSM by Multiharmonic Current Injection Method

In the radial vibration suppression of the motor, the current harmonic injection method is an effective method. The radial force model of surface permanent magnet synchronous machine (SPMSM) is analyzed, and the relationship of the frequency, amplitude and phase between harmonic current and radial vibration harmonic is obtained. A current harmonic injection method is derived by using the current closed-loop transfer function in vector control, which can adjust the frequency, amplitude and phase of current harmonics to realize the change in the amplitude of radial vibration harmonics. one current harmonic is selected to suppress one even-order radial vibration harmonic, and the gradient descent method is adopted to online calculate the frequency amplitude and phase of the current harmonics injected at the minimum amplitude of radial vibration harmonics. In order to avoid the influence of the single current harmonic on the high radial vibration harmonics, a sequential vibration suppression method is proposed to realize the comprehensive suppression of every low-even-order vibration harmonics. Finally, an on-line control system for the radial vibration suppression of SPMSM with multi-current harmonics injection is designed. The prototype is a 750W SPMSM, the amplitude of all low-even radial vibration harmonics are suppressed significantly by this method, which verifies the correctness and effectiveness of this method.