Minimization Of Cost Function Research Articles

Neuroadaptive Optimal Fixed-Time Synchronization and its Circuit Realization for Unidirectionally Coupled FO Self-Sustained Electromechanical Seismograph Systems.

This article investigates the neuroadaptive optimal fixed-time synchronization and its circuit realization along with dynamical analysis for unidirectionally coupled fractional-order (FO) self-sustained electromechanical seismograph systems under subharmonic and superharmonic oscillations. The synchronization model of the coupled FO seismograph system is established based on drive and response seismic detectors. The dynamical analysis reveals this coupled system generating transient chaos and homoclinic/heteroclinic oscillations. The test results of the constructed equivalent analog circuit further testify its complex nonlinear dynamics. Then, a neuroadaptive optimal fixed-time synchronization controller integrated with the FO hyperbolic tangent tracking differentiator (HTTD), interval type-2 fuzzy neural network (IT2FNN) with transformation, and prescribed performance function (PPF) together with the constraint condition is developed in the backstepping recursive design. Furthermore, it is proved that all signals of this closed-loop system are bounded, and the tracking errors fall into a trap of the prescribed constraint along with the minimized cost function. Extensive studies confirm the effectiveness of the proposed scheme.

A Cascaded Band Based Model Predictive Current Control for PMSM Drives

Conventional model predictive control for permanent magnet synchronous motor (PMSM) drives often introduces a weighting factor for lowering the switching frequency, which unfortunately leads to the tedious tuning works on weighting factors. Moreover, the computational burden is quite heavy since all the voltage candidates have to be enumerated. In this work, a cascaded band-based model predictive current control is proposed, which can reduce the switching frequency without the use of weighting factor as well as alleviate the computational burden. In this method, the latest applied voltage vector will be adopted to predict the current error prior to the iterative prediction process. Next, a band is defined to compare with the pre-calculated current error to determine whether it is to be maintained. As a result, the iterative cost function minimization turns to be an event triggered process. In addition, the hard switch constraint for the candidate voltage vectors are developed to combine with the cascaded band, which can effectively reduce the iterations and avoid the sub-optimum. The effectiveness of the proposed method is verified on a PMSM platform fed by 3-level T-type inverter.

Two-Phase Virtual Machine Placement in Cloud Computing Data Centers Using Extreme Learning Machine Prediction-Based Triggering Method

Background: Two-phase Optimization of Virtual Machine Placement (VMP) Problem considers both the Online Incremental VMP (iVMP) phase in which the new arrival of dynamic requests of Virtual Machines VMs are attended to and the Offline VMP reconfiguration (VMPr) phase that performs placement recalculation. In the two-phase scheme, the first part of the two-phase approach is the iVMP, where virtual machines (VMs) can be built, changed, or destroyed at runtime. While the second phase focuses on raising the standard of solutions produced by the iVMP, several studies have been done in different literature to solve the VMP problem. However, the methods used tend to be over-forecast and have long runs of a linear trend. This affects the prediction and produces a less optimal solution. Objective: The following four objective functions are optimized using the proposed Extreme Learning Machine Prediction-Based Triggering Method for Virtual Machine Placement in Cloud Computing Datacenters in Two-Phases, which combines the advantages of both online (dynamic) and static (offline) VMP formulations: the length of the reconfiguration process, the amount of energy used, the way resources are used, and the financial expenses. This study suggests a brand-new strategy for deciding when to start the VMP reconfiguration phase. Results: The Method provides more accuracy to the predicted requests as well as reduces the total economic penalties for service Level Agreement(SLA) violations. An experimental comparison with the existing approach is conducted utilizing 400 cases. Conclusion: The results demonstrated that, in comparison to the benchmark approach, the proposed work obtained a minimum cost function with a 10.5% improvement

Vertical profiles of aerosol and NO2 based on mobile multi-axis differential absorption spectroscopy

Mobile acquisition of vertical distribution profiles of air pollutants in urban areas is of great significance for studying the transmission and traceability of urban pollutants. In this study, a portable mobile multi-axis differential optical absorption spectroscopy (MAX-DOAS) system was used to establish an algorithm based on the mobile system to retrieve air pollution profiles, which could quickly trace the source of pollutants in urban areas. First, this study used the mobile MAX-DOAS system to continuously measure a planned urban route from multiple angles in real time and collect the vertical distribution information of aerosols and NO2 in different positions. Then, the established algorithm and the vertical distribution information of the pollutants were used to retrieve the air pollution profiles via the minimum cost function. Finally, the pollution source orientation was traced, and the pollution transmission process was analyzed based on the wind field data of the model. Experimental results showed a significant correlation between tropospheric monitoring instrument (TROPOMI) and mobile measurements based on a coefficient of determination value of 0.85. It was found that the air quality of Xuzhou and Huaihai Economic Zone was adversely affected by the transport of the pollutants northeast of Xuzhou. This study realizes portable mobile retrieval of the aerosol and NO2 profiles in urban areas, improves the accuracy of pollution traceability, and provides insights into retrieving the trace gas profiles via a mobile MAX-DOAS system.

A multiscale framework for defining homeostasis in distal vascular trees: applications to the pulmonary circulation.

Pulmonary arteries constitute a low-pressure network of vessels, often characterized as a bifurcating tree with heterogeneous vessel mechanics. Understanding the vascular complexity and establishing homeostasis is important to study diseases such as pulmonary arterial hypertension (PAH). The onset and early progression of PAH can be traced to changes in the morphometry and structure of the distal vasculature. Coupling hemodynamics with vessel wall growth and remodeling (G&R) is crucial for understanding pathology at distal vasculature. Accordingly, the goal of this study is to provide a multiscale modeling framework that embeds the essential features of arterial wall constituents coupled with the hemodynamics within an arterial network characterized by an extension of Murray's law. This framework will be used to establish the homeostatic baseline characteristics of a pulmonary arterial tree, including important parameters such as vessel radius, wall thickness and shear stress. To define the vascular homeostasis and hemodynamics in the tree, we consider two timescales: a cardiac cycle and a longer period of vascular adaptations. An iterative homeostatic optimization, which integrates a metabolic cost function minimization, the stress equilibrium, and hemodynamics, is performed at the slow timescale. In the fast timescale, the pulsatile blood flow dynamics is described by a Womersley's deformable wall analytical solution. Illustrative examples for symmetric and asymmetric trees are presented that provide baseline characteristics for the normal pulmonary arterial vasculature. The results are compared with diverse literature data on morphometry, structure, and mechanics of pulmonary arteries. The developed framework demonstrates a potential for advanced parametric studies and future G&R and hemodynamics modeling of PAH.

Time-Varying Optimization of Networked Systems With Human Preferences

This article considers a time-varying optimization problem associated with a network of systems, with each of the systems shared by (and affecting) a number of individuals. The objective is to minimize cost functions associated with the individuals' preferences, which are unknown, subject to time-varying constraints that capture physical or operational limits of the network. To this end, this article develops a distributed online optimization algorithm with concurrent learning of the cost functions. The cost functions are learned on-the-fly based on the users' feedback (provided at irregular intervals) by leveraging tools from the shape-constrained Gaussian process. The online algorithm is based on a primal–dual method and acts effectively in a closed-loop fashion where: First, users' feedback is utilized to estimate the cost, and second, measurements from the network are utilized in the algorithmic steps to bypass the need for sensing of (unknown) exogenous inputs of the network. The performance of the algorithm is analyzed in terms of dynamic network regret and constraint violation. Numerical examples are presented in the context of real-time optimization of distributed energy resources.

A Safety Planning and Control Architecture Applied to a Quadrotor Autopilot

This letter presents a safety trajectory planning and tracking architecture for a quadrotor autopilot. Motor saturation constraints are explicitly considered in obstacle avoidance mission. Two challenging cases are covered: agile flight with a short task time and stable flight with actuator degradation. By explicitly considering the real physical constraints, the proposed method can make better use of the quadrotor maneuverability. Specifically, from control aspect, a sliding mode observer (SMO) based safety controller is implemented on the autopilot. Subsequently, the actuator saturation problem is analyzed and modeled as axis-coupled constraints. The B-spline curve is used to deal with the constraints due to its convex hull property. As a result, the planning problem can be transformed into a quadratically constrained quadratic programming (QCQP) problem with the proposed minimum energy cost function. Experiments show that the resulting trajectories can yield improvements in tracking accuracy in comparison of conventional planning method.

Two-step phase shifting algorithm based on cost function minimization

A computationally efficient phase shift estimation algorithm is proposed for two-step phase-shifting interferometry based on a cost function minimization technique. The proposed method can accurately estimate the phase shift over a wide range resulting in reliable phase estimation. Due to the analytical solution offered by the proposed method, its computational efficiency is found to be higher than the existing state-of-art methods. While the performance of some methods is user-defined parameter-dependent, the proposed method is non-parametric and user-independent. The accuracy of phase shift and subsequent phase estimation using the proposed algorithm is found to be maximum around phase shift of π/2rad which further decreases for the phase shift values closer to 0rad and πrad. The experimental results demonstrate the practical applicability of the proposed method.

De-Noising of Sparse Signals Using Mixture Model Shrinkage Function

In this work a new thresholding function referred to as ’mixture model shrinkage’ (MMS) based on the minimization of a convex cost function is proposed. Normally, thresholding functions underestimate larger signal amplitudes during the de-noising process. The proposed model is a more flexible shrinkage function as it solves the underestimation problem to a greater extent and thus efficiently de-noises the signal without affecting signal amplitudes. The Expectation minimization (EM) algorithm is used to find the model parameters along with the majorization-minimization (MM) algorithm that minimize the monotonic cost function. The proposed model is then applied for de-noising group sparse signals and Shepp Logan phantom images. Our experimental study shows that MMS outclasses current thresholding functions and overlapping group shrinkage algorithm without results suffering from underestimation. Furthermore, the proposed model has the smallest Root Mean Square Error (RMSE) for de-noising group sparse signals.

Robust Finite Control Set Model Predictive Current Control for Induction Motor Using Deadbeat Approach in Stationary Frame

The model predictive control increased its prominence in the field of induction machine drives. However, the performance of this strategy depends on the accuracy of the machine model parameters. In order to overcome this deficiency, this paper proposes a robust model predictive current control employing indirect field-oriented control in stationary reference frame using the stator current and the rotor flux vectors as state and stator voltage vector as the input. The control algorithm combines the classical model predictive control with the deadbeat approach in order to calculate the applied stator voltage vector in two components: one element considers the stator current reference, and another employs the disturbances caused due to the parameter errors, which allows to compensate the parameter mismatches in the plant model. The minimized cost function employs the predicted stator voltage vector to select the voltage vector to be applied to the stator terminals of the motor. The control method performance was verified using an experimental test bench analyzing the system steady-state and dynamic actions. In this way, the results corroborate the proposed controller robustness against parametric variations.

Noise-Immune Machine Learning and Autonomous Grid Control

Most recently, stochastic control methods such as deep reinforcement learning (DRL) have proven to be efficient and quick converging methods in providing localized grid voltage control. Because of the random dynamical characteristics of grid reactive loads and bus voltages, such stochastic control methods are particularly useful in accurately predicting future voltage levels and in minimizing associated cost functions. Although DRL is capable of quickly inferring future voltage levels given specific voltage control actions, it is prone to high variance when the learning rate or discount factors are set for rapid convergence in the presence of bus noise. Evolutionary learning is also capable of minimizing cost function and can be leveraged for localized grid control, but it does not infer future voltage levels given specific control inputs and instead simply selects those control actions that result in the best voltage control. For this reason, evolutionary learning is better suited than DRL for voltage control in noisy grid environments. To illustrate this, using a cyber adversary to inject random noise, we compare the use of evolutionary learning and DRL in autonomous voltage control (AVC) under noisy control conditions and show that it is possible to achieve a high mean voltage control using a genetic algorithm (GA). We show that the GA additionally can provide superior AVC to DRL with comparable computational efficiency. We illustrate that the superior noise immunity properties of evolutionary learning make it a good choice for implementing AVC in noisy environments or in the presence of random cyber-attacks.

Stability Criterion and Frequency-domain Approximate Model Matching Based on V-Tiger

FRIT and VRFT are widely applied as representatives of Data-driven control, but the actual closed-loop response cannot be predicted. Recently, Data-driven prediction methods including V-Tiger that predict the actual closed-loop response have been proposed. V-Tiger predicts the virtual frequency response and virtual time response of a closed-loop system from one-shot experimental data to read the settling time and Nyquist locus, but these readings become difficult when the noise is large. Therefore, we propose (1) a stability criterion based on the virtual time response, (2) a noise reduction method that averages multiple virtual time responses using only one-shot experimental data, and (3) a control design method to minimize cost function in frequency domain to achieve robustness against noise.

Choice of a logistics route for cargo delivery in modern conditions

The current conditions of the global movement of goods under sanctions make it necessary to build new logistics routes along those international transport corridors that will ensure the reliability of transportation. Crisis points in any industry are at the same time a potential for development and growth, which is confirmed by the history of the development of logistics in the XX century. Basic principles of logistics and methods of quality management theory should serve as a basis for choosing the vector of transport and production development. Transit potential of the Russian Federation is unlocked by the Northern Sea Route, latitudinal corridor “East-West” and meridional corridor “North-South”. Tough policies of some countries make it necessary to redirect commodity flows along new logistic routes. The analysis of goods traffic promotion through the North-South corridor shows its serious competitive advantages in comparison with the Southern sea route in terms of transportation distance, time, and cost of delivery. The study analyzes the problems of the international transport corridor. These include technological, documentary, and infrastructural unresolved problems. The multivariant logistic route in the central part of the North-South corridor poses the problem of choosing the optimal route. The article analyzes the existing methods of route choice based on equal advantageous transportation distance and full cost for solving this task. The limitation of the listed methods leads to the search for a cost minimization function for a logistics route.

Job scheduling problem in fog-cloud-based environment using reinforced social spider optimization

Fog computing is an emerging research domain to provide computational services such as data transmission, application processing and storage mechanism. Fog computing consists of a set of fog server machines used to communicate with the mobile user in the edge network. Fog is introduced in cloud computing to meet data and communication needs for Internet of Things (IoT) devices. However, the vital challenges in this system are job scheduling, which is solved by examining the makespan, minimizing energy depletion and proper resource allocation. In this paper, we introduced a reinforced strategy Dynamic Opposition Learning based Social Spider Optimization (DOLSSO) Algorithm to enhance individual superiority and schedule workflow in Fog computing. The extensive experiments were conducted using the FogSim simulator to generate the dataset and an energy-efficient open-source tool utilized to model and simulate resource management in fog computing. The performance of the formulated model is ratified using two test cases. The proposed algorithm attained the optimized schedule with minimized cost function concerning the CPU processing period and assigned memory. Our simulation outcomes show the efficacy of the introduced technique in handling job scheduling issues, and the results are contrasted with five existing metaheuristic techniques. The results show that the proposed method achieves 10% - 15% better CPU utilization and 5%-10% less energy consumption than the other techniques.

Optimal vaccination in a SIRS epidemic model.

We propose and solve an optimal vaccination problem within a deterministic compartmental model of SIRS type: the immunized population can become susceptible again, e.g. because of a not complete immunization power of the vaccine. A social planner thus aims at reducing the number of susceptible individuals via a vaccination campaign, while minimizing the social and economic costs related to the infectious disease. As a theoretical contribution, we provide a technical non-smooth verification theorem, guaranteeing that a semiconcave viscosity solution to the Hamilton-Jacobi-Bellman equation identifies with the minimal cost function, provided that the closed-loop equation admits a solution. Conditions under which the closed-loop equation is well-posed are then derived by borrowing results from the theory of Regular Lagrangian Flows. From the applied point of view, we provide a numerical implementation of the model in a case study with quadratic instantaneous costs. Amongst other conclusions, we observe that in the long-run the optimal vaccination policy is able to keep the percentage of infected to zero, at least when the natural reproduction number and the reinfection rate are small.

Time‐modulated multi‐tone antenna array for wireless information and power transfer

SummaryThis paper aims to design a multi‐tone radiator exploiting harmonic radiation characteristic of time‐modulated antenna array for wireless information and power transmission (WIPT) and multi‐operational WIPT receiver. The time‐modulated linear antenna array (TMLAA) radiating simultaneously at modulating and harmonic frequencies separated in multiple switching periods is utilized as a multi‐tone radiating system. TMLAA with suitable power in harmonic bands generates multi‐tone radiation by employing an optimized switching sequence. The ON and OFF time instants of the TMLAA are optimized to suppress sidelobe level and enhance sideband level. A population‐based optimization algorithm, teaching learning based optimization (TLBO), is employed to optimize the ON and OFF instants of the TMLAA to suppress sidelobe level and enhance sideband level. TLBO is utilized for a 16‐element TMLAA with minimizing cost function to achieve the above objectives. The 16‐element TMLAA with optimized switching radiates multi‐tone beams with a minimum of 5 dB peak power difference exploited as WIPT. The purpose of multi‐operation is accomplished by utilizing the Wilkinson power divider in the receiver system; its power dividing capability is analyzed using applied wave research (AWR). A voltage doubler type rectifier is modeled for DC generation and is tested using AWR. The DC generating capability is tested for fixed power available at various frequencies, and the fixed desired frequency with various available power levels is tested. The results show that the designed circuitry provides a maximum of 80% power conversion efficiency (PCE).

Model Free Predictive Current Control Based on a Grey Wolf Optimizer for Synchronous Reluctance Motors

A Model-based predictive current control (MBPCC) has recently become a powerful advanced control technology in industrial drives. However, MBPCC relies on the knowledge of the system model and parameters, being, therefore, very sensitive to parameters errors. In the case of the synchronous reluctance motor (SynRM), where the parameters vary due to its ferromagnetic structure and nonlinear magnetic properties, MBPCC performance would suffer significantly. Accordingly, in this paper, a Grey Wolf Optimizer based model-free predictive current control (GW-MFPCC) of SynRM is proposed, to skip all the effects of the model dependency and parameters uncertainty. The proposed method predicts the stator current through tracking the minimum cost function using the grey wolf optimizer. The proposed GW-MFPCC scheme is compared to MBPCC, and its effectiveness is evaluated and confirmed by experimental results.

Optimal sequential decision rule for cooperative spectrum sensing of CUAVNs

AbstractCognitive radio (CR) technology is beneficial for unmanned aerial vehicles (UAVs) to access the spectrum resources needed for communication. Then, in order to integrate CR technology into UAVs without causing excessive interference to the primary users (PUs), cooperative spectrum sensing (CSS) is used as a key function of CR technology to detect the PUs for the UAV's available spectrum. However, the flexible locations of UAVs may significantly degrade the performance and efficiency of CSS within them. Hence, on the basis of a cognitive UAV networks (CUAVN) framework, we propose an internal cooperation paradigm to achieve the cooperative gain of CSS within an UAV. Furthermore, we convert the decision rule as an optimal stopping problem to optimize the decision rule for CSS. The optimization problem is carried out by the error probability regarding the PU presence of the phenomenon and the stopping time required to reach a global decision about the PU status. By optimizing the error probability respectively under the condition of a fixed sensing time, the optimization problem is transformed into an optimal stopping problem and solved by Markov stopping theory. Following this, we further determine a pair of thresholds in the optimal sequential decision rule from the minimal cost function. At last, numerical simulation results verify the correctness and effectiveness of the optimal sequential detection rule, and corroborate that in contrast to the external cooperative paradigm, the superiority of the internal cooperative paradigm under various network environments and parameters, in terms of the CSS performance and the stopping time.

Adaptive reinforcement learning optimal tracking control for strict-feedback nonlinear systems with prescribed performance

The reinforcement learning-based prescribed performance optimal tracking control problem is considered for a class of strict-feedback nonlinear systems in this paper. The unknown nonlinearities and cost function are approximated by radial-basis-function (RBF) neural network (NN). The overall controller consists of an adaptive controller and an optimal compensation term. Firstly, the adaptive controller is designed by backstepping control method. Subsequently, the optimal compensation term is derived via policy iteration by minimizing cost function. In addition, depending on the prescribed performance control, the tracking error can be limited in the prescribed area. Therefore, the whole control scheme can effectively guarantee that the tracking error converges to a bound with prescribed performance while the cost function is minimized. The stability analysis shows that all signals in the closed-loop system are bounded. Finally, the effectiveness and advantages of the designed control strategy are illustrated by the simulation examples.

Design and Development of Improved Squirrel Search-based Secured VM Migration in the Cloud Sector with Optimal Key Management

This article aims to design a new secured data transmission, VM migration, and placement. VM is constructed for hosting the applications by taking the essential resources, and OS is given by the user after receiving a request from the customer by the data center of the cloud. Here, the best fitness index-based Squirrel Search Algorithm (BFI-SSA) to minimize the number of active servers in the cloud. Further, the BFI-SSA is used for implementing the optimal VM migration with the help of a multiobjective function using the makespan and energy consumption. For migrated VM, the secured data transmission is performed by the same improved SSA with “data sanitization and restoration” with adaptive key management. This BFI-SSA algorithm is intended to generate the key and to increase the privacy of the migrated data. The energy consumption of the designed method attains 8% regarding the best rate value. Moreover, the designed method attains a minimum cost function to get a higher convergence rate in the 40th iteration. The given offered method attains a better makespan rate regarding best rate values. Thus, the simulation result has outperformed the suggested model and has achieved low energy and power consumption when compared to other-state-of-art methods.