Online Optimization Strategy Research Articles

Event-triggered online energy flow control strategy for regional integrated energy system using Lyapunov optimization

Regional Integrated Energy System (RIES) provides an inspiring perspective for constructing the future-oriented Energy Internet. However, the integration of heterogeneous energy system increases the complexity of critical power infrastructures, which limits the feasibility of energy management in real practice. To mitigate this issue, we propose an online optimization strategy for RIES with heating, ventilation and air conditioning (HVAC) loads in this paper. Specifically, a stochastic problem is formulated to minimize the economic cost with the consideration of power to gas and combined heat and power techniques. In addition, considering the changes of specific events. i.e., essential load, electricity price, queue length of renewable energy sources (RES) and comfort level of end-users, an event-triggered mechanism is established to enable automatic execution of scheduling signals. Without relying on a priori knowledge, we use the Lyapunov optimization method to control energy queue of HVAC and the system stability in real time. The feasibility of the proposed online energy flow control algorithm has been validated by the theoretical deduction. The numeral simulation based on real-world traces demonstrates that the proposed method maximizes the economic profit by incenting the load transferring during peak period of electricity price and effectively improves the utilization ratio of RES. Moreover, this method can ensure the end-user’s comfort while dramatically reducing the dispatching frequency of HVAC with the event-triggered mechanism.

Torque Ripple and Efficiency Online Optimization of Switched Reluctance Machine Based on Torque per Ampere Characteristics

Relatively high torque ripple limits the further development of the switched reluctance machine, and the application of the ripple suppression method will reduce the efficiency of the system. To simultaneously improve the efficiency and torque ripple of the switched reluctance machine, a new commutation and online optimization strategy is proposed. According to the torque per ampere characteristics of the machine, two rotor position angles are defined, which are the boundaries for the high and low torque per ampere operation modes of the two adjacent phases, respectively, and for different operating modes, specific torque hysteresis control schemes are performed. Furthermore, an objective function is constructed with the efficiency and torque ripple coefficient, and during the operation of the machine, the defined boundary angles are online optimized by the genetic algorithm to maximize the value of the objective function. The effectiveness of the method is verified by detailed simulation and experiment under different operating conditions.

An improved particle swarm optimization algorithm for the optimization and group control of water-side free cooling using cooling towers

The application of cooling towers for free cooling is an effective energy saving method for cooling systems in data centers and industrial applications. This paper presented a cooling tower performance model suitable for on-line optimization and established an experimental system to verify the cooling tower model. Based on models of the system components, an improved particle swarm optimization algorithm was proposed to achieve optimization and group control of the water-side free cooling system. The simulation results showed that the fluctuations of the optimal energy consumption and outlet water temperature were 0.66% and 3.34% respectively, demonstrating improved stability and accuracy of the proposed algorithm compared with the conventional algorithm. The results revealed that the optimal outlet water temperature of the cooling tower varied minimally with the cooling load and was approximately linearly proportional to the outdoor wet bulb temperature. The optimal gas-water mass ratios were found to be inversely correlated to the wet-bulb temperature and cooling load. These results can provide a reference for the design and implementation of on-line optimization strategies for multi-components water-side free cooling systems.

Robust Control of Underwater Vehicle-Manipulator System Using Grey Wolf Optimizer-Based Nonlinear Disturbance Observer and H-Infinity Controller

This paper proposes a new trajectory tracking scheme for the constrained nonlinear underwater vehicle-manipulator system (UVMS). For overcoming the unmodeled uncertainties, external disturbances, and constraints of control inputs in the operation of UVMS, a modified constrained H∞ controller with a basic computed-torque controller (CTC) and a new designed nonlinear disturbance observer (NDO) are proposed. The CTC gives the nominal model-based control. The NDO is designed based on the system dynamics and used to online provide the estimation of the lumped disturbances. However, the designed NDO is an observer of biased estimation, i.e., it has a blind domain of disturbance estimation which cannot be rejected. In order to reject the biased estimation, the modified constrained H∞ controller is designed but with new features. To the best of our knowledge, the conventional H∞ robust controller is generally designed by calculating the Riccati equation offline and ignoring the constraints of control inputs made by the physical actuators, which are poor in handling the time-varying environment. In order to solve these issues, the modified constrained H∞ robust controller online optimized by grey wolf optimizer (GWO) is designed to ensure the control system has a compensation of the biased estimation, a satisfied constrained control input, and a fast calculation. In this paper, we modify the prior method of offline calculating the Riccati equation of the conventional H∞ robust controller to be an online optimization scheme and proposed a new constrained evaluation function. The new constrained evaluation function is online optimized by the GWO, which can both find out the constrained suboptimal control actions and compensate the biased estimation of the NDO for the UVMS. The whole system stability is proved. The effectiveness of the fast online calculation, tracking accuracy, and lumped disturbances rejection is shown by a series of UVMS simulations.

Proactive Edge Caching in Content-Centric Networks With Massive Dynamic Content Requests

Edge computing is a promising infrastructure evolution to reduce traffic loads and support low-latency communications. Furthermore, content-centric networks provide a natural solution to cache contents at edge nodes. However, it is a challenge for edge nodes to handle massive and highly dynamic content requests by users, and if without an efficient content caching strategy, the edge nodes will encounter high traffic load and latency due to increasing retrieval from content providers. This paper formulates a proactive edge caching problem to minimize the content retrieval cost at edge nodes. We exploit the inherent content caching and request aggregation mechanism in the content-centric networks to jointly minimize traffic load and content retrieval delay cost generated by the massive and dynamic content requests. We develop a Q-learning algorithm, which is an online optimal caching strategy, as it is adaptable to dynamic content popularity and content request intensity, and derive the long-term minimization of the content retrieval cost. Simulation results illustrate that the proposed algorithm can achieve a lower content retrieval cost compared with several baseline caching schemes.

Multi-block tensor regression for quality prediction and root cause analysis in the production of active pharmaceutical ingredients

The large scale production of active pharmaceutical ingredients (APIs) is traditionally accomplished via batch processes. Nevertheless, their inherent complexity has limited the development and application of models for the processes as well as the use of advanced online monitoring, control and optimization strategies for their continuous improvement. The quality by design (QbD) strategy, defined by regulatory agencies, has brought a practical view into this. QbD seeks to determine methods to define the critical quality attributes (CQAs) of the product in terms of the critical material attributes (CMAs) and the critical process parameters (CPPs) of the input space along the whole process. This means not only on individual batch units but also throughout the multiple steps and stages of the production process. In this contribution, data-driven modelling methods were exploited to model two synthesis steps in the large scale production of an Active Pharmaceutical Ingredient (API). First, tensor factorization was applied to train correlation based models to capture the main directions of variability for each of the studied steps. Secondly, a partial least squares (PLS) model was trained to regress the concentration of an impurity on the product onto the latent variables of the primary monitoring models as well as the CMAs of the input materials. The proposed modelling approach was applied to data from the large scale production on an API. This resulted in an accurate model, which was validated on an independent data set and which captures meaningful correlations that helped on the root cause identification for variations encountered in the CQA of the product. These results are in line with the observations made on the process operation.

Online Adaptive Supervised Hashing for Large-Scale Cross-Modal Retrieval

In recent years, with the continuous growth of multimedia data on the Internet, multimodal hashing has attracted increasing attention for its efficiency in large-scale cross-modal retrieval. Typically, most existing multimodal hashing methods are batch-based methods that cannot deal with the growing streaming data. Online multimodal hashing adopts online learning strategy to learn hash models incrementally, which can process large-scale streaming data. However, existing supervised online multimodal hashing methods still suffer from several limitations: (1) most methods cannot update hash codes of old data when hash model changes, which will hinder the retrieval accuracy. (2) the discrete optimizations of most methods for learning binary hash codes are less efficient or less effective. To address the above limitations, an efficient supervised online multimodal hashing method termed Online Adaptive Supervised Hashing (OASH) is proposed in this paper. OASH regresses class labels and training data to binary hash codes to learn discrete hash codes and hash functions with an efficient online optimization scheme. It learns hash functions incrementally with newly arriving data and updates hash codes with the latest hash model. By adaptively updating hash functions and hash codes with new data rather than accessing to old data, it gains much performance enhancement and computation savings. Extensive experiments on three benchmark data sets validate the superiority of OASH over state-of-the-art methods in both accuracy and efficiency.

Self-optimization of resilient topologies for fallible multi-robots

Effective exchange of information in multi-robot systems is one of the grand challenges of today’s robotics. Here, we address the problem of simultaneously maximizing the (i) resilience to faults and (ii) area coverage of dynamic multi-robot topologies. We want to avoid the onset of single points of failure, i.e., situations in which the failure of a single robot causes the loss of connectivity in the overall network. Our methodology is based on (i) a three-fold control law and (ii) a distributed online optimization strategy that computes the optimal choice of control parameters for each robot. By doing so, connectivity is not only preserved, but also made resilient to failures as the network topology evolves. To assess the effectiveness of our approach, we ran experiments with a team of eight two-wheeled robots and we evaluated it against the injection of two separate classes of faults: communication and hardware failures. Results show that the proposed approach continues to perform as intended, even in the presence of these hazards.

Robust Optimal Selection of Radio Type and Transmission Power for Internet of Things

Research efforts over the last few decades produced multiple wireless technologies, which are readily available to support communication between devices in various dynamic Internet of Things (IoT) and robotics applications. However, single radio technology can hardly deliver optimal performance across all critical quality of service (QoS) dimensions under the typically varying environmental conditions or under varying distance between communicating nodes. Using a single wireless technology therefore falls short of meeting the demands of varying workloads or changing environmental conditions. Instead of pursuing a one-radio-fits-all approach, we design ARTPoS , an Adaptive Radio and Transmission Power Selection system, which makes available at runtime multiple wireless technologies (e.g., WiFi and ZigBee) and selects the radio(s) and transmission power(s) most suitable for the current conditions and requirements. The principal components of ARTPoS include new empirical models of power consumption and packet reception ratio (the latter can also be refined online) and online optimization schemes. We have implemented our system and evaluate it on the physical testbed consisting of our new embedded platforms with heterogeneous radios. Experimental results show that ARTPoS can significantly reduce the power consumption, while maintaining desired link reliability, compared to standard baselines.

Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization

Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.

Online dynamic optimization studies of autocatalytic esterification in the semi batch reactor for handling disturbance and uncertainty

A Catalyzed Esterification of Propionic Anhydride with 2-Butanol in the semi batch reactor which involves complex pathways of the autocatalytic mechanism, is producing sec-butyl propionate and propionic acid. An online dynamic-cascaded based optimization is an effective way to handle the effect uncertainty and disturbance within semi batch process. The re-optimization and control problems are solved separately in two layers. The online optimization strategy includes an integration of dynamic re-optimizer, i.e. orthogonal collocation (OC) method in maximizing profit, estimator, i.e. Cubature Kalman Filter and controller, i.e. adaptive Proportional Integrator Derivative (PID). The re-optimization mechanism is activated conditionally by using simple trigger, i.e. ±5% of conversion as active constraint. The simulation results show that the proposed strategy offers a large improvement of reactor's performance in handling uncertainty and disturbance where re-optimized temperature and feed flowrate trajectories obtained able to keep the conversion within acceptable range (on-spec).

Cooperative Edge Computing With Sleep Control Under Nonuniform Traffic in Mobile Edge Networks

Mobile edge computing (MEC) is one of the key technologies for fifth generation networks and beyond, which brings computation resources in proximity to end users. While enabling computing capability at the network edge, it also introduces extra energy consumption to the network operation. Moreover, the nonuniform traffic distribution of mobile network makes the utilization of MEC platforms unbalanced. In this paper, we propose an online optimization strategy of MEC server (MECS) computation task offloading with sleep control scheme to minimize the long term energy consumption of the MECS network. First, the energy optimization problem under delay constraint is formulated which considers both the radio and computation resources. Then, a Lyapunov-based approach is proposed to convert the long term optimization problem to a per-slot optimization problem which only requires information of current time slot. An online offloading algorithm is proposed to make decisions in each time slot. Finally, the system performance is evaluated and the impacts of several key parameters are analyzed. Simulation results demonstrate that our proposed strategy can significantly reduce the long term average energy consumption of the mobile edge network by 30% to 90% under different workload scenarios, while keeping a relatively low system delay compared with the schemes without MEC cooperation and sleep control.

Research on active steering control strategy based on sideslip angle estimation for a steering‐by‐wire forklift

Taking the TFC35 electric forklift as the research object, we analyze the movements and forces of the forklift in three directions: yaw, lateral movement, and roll. Combined with the load transfer formula of the forklift and the nonlinear and linear tire model, the formula for lateral force of the four tires of forklift is deduced, and the nonlinear and linear three‐degree‐of‐freedom (3DOF) forklift dynamics model is established. Because the sideslip angle of the forklift is not easy to measure directly, based on the linear 3DOF forklift model, we take the yaw rate and lateral angle as the observation variables and consider the influence of model error. An adaptive Kalman filter is designed to estimate the sideslip angle of the forklift. In order to improve the accuracy of estimation, an online optimization strategy of the adaptive Kalman filter based on genetic algorithm with an adaptive factor is proposed. Simulation results show the effectiveness of the proposed method. In combination with the forklift reference model, the rear wheel angle of the forklift is corrected by comprehensive proportional feedback of yaw rate and the sideslip angle, and the active steering system for a steering‐by‐wire forklift is designed. Simulation and test results show that the control strategy of forklift's active steering system can improve its driving stability and handling performance. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Online Distributed MPC-Based Optimal Scheduling for EV Charging Stations in Distribution Systems

The increasing popularity of electric vehicles (EVs) has made electric transportation a popular research topic. The demand for EV charging resources has significantly reshaped the net demand profile of power distribution systems. This paper proposes an online optimal charging strategy for multiple EV charging stations in distribution systems with power flow and bus voltage constraints satisfied. First, we formulate the online optimal charging problem as an optimal power flow problem that minimizes the total system energy cost based on short-term predictive models and operates in a time-receding manner with the latest system information. Then, the problem is convexified by a modified convex relaxation technique based on the bus injection model, so that the globally optimal solution can be obtained with high efficiency. Moreover, a distributed model predictive control based scheme is designed to solve the optimization problem per concerns regarding data privacy, individual economic interests, and EV uncertainties. The obtained optimal schedules are dispatched to the EVs parked at each charging station according to a fuzzy rule, which guarantees full charging at the departure time for each vehicle. The effectiveness of the proposed method is demonstrated via simulations on a modified IEEE 15-bus distribution system with charging stations located in both residential and commercial areas.

Hybrid modeling and online optimization strategy for improving carbon efficiency in iron ore sintering process

Iron ore sintering is the second most energy consuming process in ironmarking, and the main energy consumption comes from the combustion of carbon. In order to improve the utilization of carbon in the process, taking a comprehensive coke ratio (CCR) as a metric of carbon efficiency, we develop a hybrid prediction model for CCR. On this basis, we present an online optimization strategy. Through the analysis of the sintering mechanism, the key sintering parameters affecting CCR are determined by a Pearson’s correlation coefficient analysis. Then, multiple operating conditions of the process are identified by a Fuzzy C-Means clustering. Multiple models for different operating conditions are built by least squares support vector machine, and combined by using a Takagi–Sugeno fusion scheme (i.e., T-S rule-based model) so that the CCR prediction model is formed. To achieve CCR optimization based on this model, an online optimization strategy based on a chaos particle swarm optimization is presented. The simulation involving actual run data verifies that the proposed modeling method exhibits high prediction accuracy. Moreover, the results of actual runs show that the proposed optimization method satisfies the requirements of the actual sintering production, where the CCR is reduced by 1.327 kg/t (on average).

Online latent semantic hashing for cross-media retrieval

Hashing based cross-media method has been become an increasingly popular technique in facilitating large-scale multimedia retrieval task, owing to its effectiveness and efficiency. Most existing cross-media hashing methods learn hash functions in a batch based mode. However, in practical applications, data points often emerge in a streaming manner, which makes batch based hashing methods loss their efficiency. In this paper, we propose an Online Latent Semantic Hashing (OLSH) method to address this issue. Only newly arriving multimedia data points are utilized to retrain hash functions efficiently and meanwhile preserve the semantic correlations in old data points. Specifically, for learning discriminative hash codes, discrete labels are mapped to a continuous latent semantic space where the relative semantic distances in data points can be measured more accurately. And then, we propose an online optimization scheme towards the challenging task of learning hash functions efficiently on streaming data points, and the computational complexity and memory cost are much less than the size of training dataset at each round. Extensive experiments across many real-world datasets, e.g. Wiki, Mir-Flickr25K and NUS-WIDE, show the effectiveness and efficiency of the proposed method.

Robust stability analysis for barrier-based equation-free multi-linear model predictive control

On-line optimization strategies such as model predictive control (MPC) have been widely used to compute control actions for a range of complex industrial systems. Barrier based MPC has recently been introduced, bringing together theory and algorithms for analysing the stability of linear models, however such models may not describe complex systems dynamics adequately. Multi-model linear MPC configurations can be used as a more reliable solution as piecewise affine (PWA) models can describe the underlying nonlinear dynamics more accurately. Additionally, model order reduction can be applied to large-scale distributed systems, to reduce their dimensionality, jeopardising however their closed-loop stability. As a result, there is a clear need for an input to output stability analysis for closed loop systems under unstructured uncertainty when multi-model barrier MPC is utilized. In this work, we combine equation-free model reduction with integral quadratic constraints (IQCs) for the stability analysis of large-scale closed-loop systems under unstructured uncertainties, including model approximation errors and nonlinearities, including MPC. An illustrative example is used to elucidate the proposed methodology.

Experimental validation of online monitoring and optimization strategies applied to a biohydrogen production dark fermenter

This work presents the proposal and experimental validation of an online optimization strategy for maximizing the hydrogen production rate (HPR) in an anaerobic bioreactor through the dark fermentation of glucose. The proposal comprises a heuristic optimization strategy, a coupled nonlinear observer, and a PI controller to track the desired maximum HPR. The optimization is achieved by solving online a nonlinear programming problem which considers the relation between the HPR and the organic loading rate (OLR), such that the latter is manipulated to maximize the former. Since the OLR requires online knowledge of the unmeasured influent substrate concentration, the nonlinear observer is used to estimate it using measurements of the hydrogen flow rate at the output of the bioreactor. This observer comprises the coupled operation of a robust Luenberger observer and a super-twisting observer. The proposal was designed and tested experimentally in a continuous stirred tank laboratory bioreactor fed with glucose, and the results are very promising.

On superposition of Hammerstein systems: Application to simultaneous hysteresis‐dynamics compensation

SummarySuperposition principle (SP)—the response (output) of a linear system to a weighted combination of inputs equals to the combination of the outputs with the same weights and each corresponding to the individual input, respectively—is one of the most fundamental properties of linear systems, and has been exploited for controls, for example, in the development of model predictive control. Extension of the superposition principle beyond linear systems, however, is largely limited. In this paper, the almost superposition of Hammerstein systems (ASHS) and its application to precision control of hysteresis‐Hammerstein systems is studied. We first show, under some minor conditions, the existence of a nonstrict form ASHS, and under one further condition, the strict‐form ASHS. We then present one application of the ASHS—simultaneous hysteresis and dynamics compensation in output tracking of hysteresis‐Hammerstein systems, where offline iterative learning control to track the output elements is integrated with online synthesis of the control input via an inverse Preisach modeling. The proposed ASHS‐based technique is further enhanced through two online optimization schemes, and then illustrated through a simulation example on piezoelectric actuators model.

A DDS-Based Energy Management Framework for Small Microgrid Operation and Control

The smart grid is seen as a power system with real-time communication and control capabilities between the consumer and the utility. This modern platform facilitates the optimization in energy usage based on several factors including environmental, price preferences, and system technical issues. In this paper, a real-time energy management system (EMS) for microgrids or nanogrids was developed. The developed system involves an online optimization scheme to adapt its parameters based on previous, current, and forecasted future system states. The communication requirements for all EMS modules were analyzed and are all integrated over a data distribution service Ethernet network with appropriate quality-of-service profiles. The developed EMS was emulated with actual residential energy consumption and irradiance data from Miami, Florida, and proved its effectiveness in reducing consumers’ bills and achieving flat peak load profiles.