Multi-objective Cost Function Research Articles

A Virtual Space Vector-Based Model Predictive Control for Inherent DC-Link Voltage Balancing of Three-Level T-Type Converters

The finite-control-set model predictive control (FCS-MPC) has been widely studied to control voltage source converters (VSCs) due to its fast-dynamic response and robustness. To design an FCS-MPC for a three-level VSC, it is necessary to design a multiobjective cost function, which usually consists of a term dedicated to control the neutral-point voltage (NP-V) of the VSC dc link. Nevertheless, selecting weighting factors for the multiple control objectives is time consuming. To address this issue, this article proposes a new FCS-MPC, which only uses voltage vectors and virtual voltage vectors that do not affect the NP-V, such that an inherent dc-link voltage balancing can be achieved. The proposed approach not only simplifies the controller design by using a simplified cost function, which significantly reduces the execution time of the MPC, but also eliminates the voltage and current sensors for the dc link. Moreover, the proposed MPC can improve the quality of VSC output waveforms. The simulation studies are carried out to verify the superiority of the proposed MPC over the existing ones. The effectiveness of the presented FCS-MPC is also experimentally validated on a T-type converter prototype.

Optimal and Heuristic Approaches for Constrained Flight Planning under Weather Uncertainty

Aircraft flight planning is impacted by weather uncertainties. Existing approaches to flight planning are either deterministic and load additional fuel to account for uncertainty, or probabilistic but have to plan in 4D space. If constraints are imposed on the flight plan these methods provide no formal guarantees that the constraints are actually satisfied. We investigate constrained flight planning under weather uncertainty on discrete airways graphs and model this problem as a Constrained Stochastic Shortest Path (C-SSP) problem. Transitions are generated on-the-fly by the underlying aircraft performance model. As this prevents us from using off-the-shelf C-SSP solvers, we generalise column-generation methods stemming from constrained deterministic path planning to the probabilistic case. This results in a novel method which is complete but computationally expensive. We therefore also discuss deterministic and heuristic approaches which average over weather uncertainty and handle constraints by scalarising a multi-objective cost function. We evaluate and compare these approaches on real flight routes subject to real weather forecast data and a realistic aircraft performance model.

Nonlinear Model Predictive Control of Autonomous Vehicles Considering Dynamic Stability Constraints

<div class="section abstract"><div class="htmlview paragraph">Autonomous vehicle performance is increasingly highlighted in many highway driving scenarios, which leads to more priorities to vehicle stability as well as tracking accuracy. In this paper, a nonlinear model predictive controller for autonomous vehicle trajectory tracking is designed and verified through a real-time simulation bench of a virtual test track. The dynamic stability constraints of nonlinear model predictive control (NLMPC) are obtained by a novel quadrilateral stability region criterion instead of the conventional phase plane method using the double-line region. First, a typical lane change scene of overtaking is selected and a new composited trajectory model is proposed as a reference path that combines smoothness of sine wave and comfort of linear functional path. Reference lateral velocity, azimuth angle, yaw rate, and front wheel steering angle are subsequently taken into account. Then, by establishing a nonlinear vehicle dynamics model where Magic Formula of nonlinear tire model is adapted, the quadrilateral vehicle stability region is defined in consideration of designed velocity, road adhesion coefficient, and front wheel steering angle. Working condition-variant constraints determined by the boundaries of the quadrilateral region are subsequently obtained to guarantee the stability and vehicle performance. Finally, a nonlinear motion state space model with measured and unmeasured disturbance for NLMPC tracking maneuver is proposed, Meanwhile, a multi-objective cost function based on track error, ride comfort, and the smoothness of control derivative is established. Laguerre functions are applied to design optimal control trajectory and Hildreth’s quadratic programming procedure is introduced to find converged solutions meeting constraints derived from previously investigated quadrilateral stability region for sake of lightening computation load and finding better numerically conditioned solutions of control when NLMPC is implemented online. The configuration of a real-time virtual test track is explained and the NLMPC algorithm is validated. The simulation and experiment results are illustrated to show the effectiveness of the designed nonlinear model predictive control scheme under the test of the overtaking scene compared with the conventional driver control. This work may provide a useful basis for researches of autonomous vehicle lane change in terms of track accuracy, ride comfort as well as stability.</div></div>

A MRAS Based Model Predictive Control for Multi-Leg Based Multi-Drive System Used in Hot Rolling Mill Applications

In this paper, a multi-leg based multi-drive configuration is presented to be used in multi AC drive applications such as the hot rolling mills. The proposed configuration enjoys compactness, fewer semiconductors, and lower drive cost compared with conventional topology, making it a promising approach. In a conventional rolling mill stand, an active front-end (AFE) rectifier and two inverters are required for grid-side and motor-side connections. However, in the proposed configuration, all converters are unified using a multi-leg structure. To improve the operational performance of the drive, a model predictive control (MPC) is designed. In this approach, an individual cost function for every output, along with a comprehensive cost function containing all control objectives for the overall system, is defined. By obtaining the valid and invalid switching states of the proposed converter, the defined multi-objective cost function is minimized to find the most optimum switching states in each sampling time. In addition, to improve the robustness of the proposed multi-drive system, a model reference adaptive system (MRAS) is designed to estimate rotor speed and stator resistance. The high-performance capability of the proposed multi-drive system is evaluated in both steady and dynamic states using MATLAB/Simulink software and compared with a conventional configuration. According to simulation results, it is deduced that the independent control of the top and bottom motors in a rolling mill stand can be guaranteed using the proposed low-cost sensor-less drive.

Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer

Considering the changeable driving conditions in reality, energy management strategies for fuel cell hybrid electric vehicles should be able to effectively distribute power demands under multiple driving patterns. In this paper, the development of an adaptive energy management strategy is presented, including a driving pattern recognizer and a multi-mode model predictive controller. In the supervisory level, the Markov pattern recognizer can classify the real-time driving segment into one of three predefined patterns. Based on the periodically updated pattern identification results, one set of pre-optimized control parameters is selected to formulate the multi-objective cost function. Afterwards, the desirable control policies can be obtained by solving a constrained optimization problem within each prediction horizon. Validation results demonstrate the effectiveness of the Markov pattern recognizer, where at least 94.94% identification accuracy can be reached. Additionally, compared to a single-mode benchmark strategy, the proposed multi-mode strategy can reduce the average fuel cell power transients by over 87.00% under multi-pattern test cycles with a decrement of (at least) 2.07% hydrogen consumption, indicating the improved fuel cell system durability and the enhanced fuel economy.

Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system

Using separate cooling coils for sensible and latent loads provide extra control flexibility to optimise the energy efficiency and comfort in air-conditioning and mechanical ventilation (ACMV) systems. A popular implementation of such technology is dedicated outdoor air system (DOAS)-assisted separate sensible and latent cooling (SSLC) systems. However, a sophisticated control technique is needed to coordinate the control of multiple cooling coils in such systems. This paper presents a novel model predictive control (MPC) developed for a DOAS-assisted SSLC system. The MPC adopts a linear state-space model that captures building thermodynamics, thermal comfort and ACMV for building response prediction and optimization. Subsequently, a multi-objective cost function is employed to optimize energy use and thermal comfort while fulfilling constraints of predicted mean vote (PMV) (-0.5, 0.5) and relative humidity (0%, 65%) in buildings. The performance of the MPC for controlling a conventional single-coil air-handling unit (AHU) system and a DOAS-assisted SSLC system is experimentally investigated and compared to a conventional feedback-control-based building management system (BMS). The MPC system achieved 18% and 20% electricity savings for the single-coil AHU and DOAS-assisted SSLC, respectively, as compared to the BMS controlled single-coil AHU. Furthermore, indoor thermal comfort is significantly improved, compared to the BMS. DOAS-assisted SSLC is shown to be advantageous compared to single-coil AHU to achieve better indoor environment in terms of thermal comfort and humidity, when both systems are controlled by MPC.

Optimization of underground mine water supply network

A qualified and effective mine water supply system is of great significance for mine dust prevention, mine cooling, fire fighting and other related mining activities that contributing to mine safety. The study in this paper was to improve the efficiency of an underground mine water supply network while taking the water demands, hydraulic conditions, resilience capability and economic cost into consideration. By means of model predictive control technology, an optimal control sequence was computed to minimize a multi-objective cost function that was subject to both physical and operation constrains in the actual network. Comparison of the performance between the original and optimized network was undertaken at a hydraulic condition with typically severe breakdown. Results showed the optimized mine water supply network achieved 95.5% increase for resilience performance with only 17.6% additional investment budget.

A Hybrid-Fuzzy Logic Guided Genetic Algorithm (H-FLGA) Approach for Resource Optimization in 5G VANETs

To support diversified quality of service demands and dynamic resource requirements of users in 5G driven VANETs, network resources need flexible and scalable resource allocation strategies. Current heterogeneous vehicular networks are designed and deployed with a connection-centric mindset with fixed resource allocation to a cell regardless of traffic conditions, static coverage, and capacity. In this paper, we propose a hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for the software defined networking controller, to solve a multi-objective resource optimization problem for 5G driven VANETs. Realizing the service oriented view, the proposed approach formulates five different scenarios of network resource optimization in 5G VANETs. Furthermore, the proposed fuzzy inference system is used to optimize weights of multi-objectives, depending on the type of service requirements of customers. The proposed approach shows the minimized value of multi-objective cost function when compared with the GA. The simulation results show the minimized value of end-to-end delay as compared to other schemes. The proposed approach will help the network service providers to implement a customer-centric network infrastructure, depending on dynamic customer needs of users.

A motivational driver model for the design of a rear-end crash avoidance system

A motivational driver model is developed to design a rear-end crash avoidance system. Current driver assistance systems use engineering methods without considering psychological human aspects, which leads to false activation of assistance systems and complicated control algorithms. The presented driver model estimates driver’s psychological motivations using the combined longitudinal and lateral time to collision, the vehicle kinematics, and the vehicle dynamics. These motivations simplify both autonomous driving algorithms and human-machine interactions. The optimal point of a motivational multi-objective cost function defines the decision for the autonomous driving. Moreover, the motivations are used as risk assessment factors for driver–machine interaction in dangerous situations. The system is evaluated on 10 human subjects in a driving simulator. The assistance system had no false activation during the tests. It avoided collisions in all the rear-end crash avoidance scenarios, while 90% of human subjects did not.

APPLICATION OF MULTI-OBJECTIVE CONTROLLER TO OPTIMAL TUNING OF PID PARAMETERS FOR DIFFERENT PROCESS SYSTEMS USING CUCKOO SEARCH ALGORITHM

A time domain performance criterion based on the multi-objective Pareto front solutions is proposed to tune the Proportional-Integral-Derivative (PID) controller parameters with the Cuckoo Search (CS) algorithm for different process systems: first order plus dead time (FOPDT) and high order dynamics. The proposed multi-objective cost function consists of conflicting objective functions including the overshoot, rise time, settling time and steady state error. In this paper, multi-objective genetic algorithm (MOGA) is used for obtaining the Pareto optimal solutions of the conflicting objective functions. The weights in the proposed multi-objective cost function are calculated by way of nondominated solutions of the obtained Pareto fronts based on the four conflicting objective functions. Also, the optimal tuning parameters of the PID controller are obtained by minimizing the integral based objective functions commonly introduced in the literature using the CS algorithm. The obtained results show that the CS optimized approach based on the proposed objective cost function outperforms than that of the integral based objective functions with higher efficiency and better quality no matter whether the process systems are employed under unload or load conditions.

Computationally Efficient Design and Optimization Approach of PMa-SynRM in Frequent Operating Torque–Speed Range

This paper proposes a design process of permanent magnet assisted synchronous reluctance motors (PMa-SynRM) intended for vehicular propulsion. This approach takes into account range optimization and a multiphysics evaluation without the use of finite element analysis during the electro-magnetic-thermal optimization stage. The design process is based on a predesign stage to calculate a first approximation of the motor dimensions. The geometry obtained in the predesign step is used to initialize a genetic algorithm during the optimization process, which evaluates different motor designs by means of coupled magnetic, thermal, and electrical models. The magnetic model is based on a nonlinear reluctance network, which calculates the inductances and magnet flux linkages. The temperature dependence of the phase resistance and the temperature distribution in the main parts of the machine are obtained from a lumped thermal network. Once obtained the electrical parameters of the machine, its behavior can be assessed considering current and voltage restrictions, by means of a dq model that evaluates the iron and copper losses. The optimization process is based on a multiobjective cost function within a torque–speed interval.

Multi-objective generalized [formula omitted] control

Multi-objective control problem with generalized H2-norms as criteria, as well as its dual problem with generalized γ0-norms, is studied. By using a scalar multi-objective cost function in the form of Germeyer convolution we derive necessary conditions for Pareto optimality and characterize Pareto optimal controllers in terms of LMIs as optimal solutions for this function. Optimal protection of a high storey building from vibration is then considered in the novel multi-objective framework as an application of the approach proposed.

Pareto Optimality in Multilayer Network Growth.

We model the formation of multilayer transportation networks as a multiobjective optimization process, where service providers compete for passengers, and the creation of routes is determined by a multiobjective cost function encoding a trade-off between efficiency and competition. The resulting model reproduces well real-world systems as diverse as airplane, train, and bus networks, thus suggesting that such systems are indeed compatible with the proposed local optimization mechanisms. In the specific case of airline transportation systems, we show that the networks of routes operated by each company are placed very close to the theoretical Pareto front in the efficiency-competition plane, and that most of the largest carriers of a continent belong to the corresponding Pareto front. Our results shed light on the fundamental role played by multiobjective optimization principles in shaping the structure of large-scale multilayer transportation systems, and provide novel insights to service providers on the strategies for the smart selection of novel routes.

Modelling optimisation and protection outcomes from distributed assets controlled to reduce a PMU based multi‐objective cost function

In this study, the development of a wide-area, multi-objective cost function is explained. The cost function is designed for implementation on wide-area phasor measurement unit data. In this study, the cost function was applied to the IEEE 30 and 57 Bus models for monitoring and control purposes. Although the cost function was originally intended as a situational awareness tool that indicated system health; it has now been applied as a control metric for the operation of on-load tap changing transformers on a variety of standard IEEE test systems. The system benefits, relative to conventional local control, are quantified. The primary metrics for testing protection outcomes are maximum voltage deviation and line thermal overload. The wide-area optimisation is assessed from a voltage profile, reactive power demand, and transmission losses. The benefits of this type of wide-area control are demonstrated in a variety of situations and environments.

Automated Surgical Approach Planning for Complex Skull Base Targets: Development and Validation of a Cost Function and Semantic At-las.

Successful multidisciplinary treatment of skull base pathology requires precise preoperative planning. Current surgical approach (pathway) selection for these complex procedures depends on an individual surgeon's experiences and background training. Because of anatomical variation in both normal tissue and pathology (eg, tumor), a successful surgical pathway used on one patient is not necessarily the best approach on another patient. The question is how to define and obtain optimized patient-specific surgical approach pathways? In this article, we demonstrate that the surgeon's knowledge and decision making in preoperative planning can be modeled by a multiobjective cost function in a retrospective analysis of actual complex skull base cases. Two different approaches- weighted-sum approach and Pareto optimality-were used with a defined cost function to derive optimized surgical pathways based on preoperative computed tomography (CT) scans and manually designated pathology. With the first method, surgeon's preferences were input as a set of weights for each objective before the search. In the second approach, the surgeon's preferences were used to select a surgical pathway from the computed Pareto optimal set. Using preoperative CT and magnetic resonance imaging, the patient-specific surgical pathways derived by these methods were similar (85% agreement) to the actual approaches performed on patients. In one case where the actual surgical approach was different, revision surgery was required and was performed utilizing the computationally derived approach pathway.

Optimal deposition orientation in fused deposition modeling for maximizing the strength of three-dimensional printed truss-like structures

Whether for producing prototypes or functional parts by additive manufacturing, the fused deposition modeling is the most commonly used technique. Nevertheless, not only the hobbyist but also the industrial three-dimensional printers produce parts that suffer from anisotropy in their mechanical properties imposing important limitations on the strength of the manufactured piece. The aim of this work is to propose a strategy for determining the optimal build surface orientation of three-dimensional truss-like structures manufactured using fused deposition modeling. This can be achieved by minimizing the norm of the dot products of the normal direction of the deposition plane (build surface plane) and the directions of the tensile forces. Since three-dimensional trusses are subjected to tensile forces in different directions, a multi-objective cost function was proposed. Moreover, these structures might present rotational symmetry, which should be considered as design constraints. In this work, two three-dimensional truss-like structures were investigated. The nature of the optimization is case dependent and solvers were selected accordingly. Experimental campaigns were carried out for evaluating the specimens manufactured using fused deposition modeling. It could be concluded that higher yield tensile strength could be achieved by adopting the optimal deposition plane. This result demonstrates the applicability of optimization techniques for improving additive manufacturing results.

Improvised predictive torque control strategy for an open end winding induction motor drive fed with four‐level inversion using normalised weighted sum model

Open end winding induction motor (OEWIM) drives are better alternate for multi-level inverter fed induction motor drives. OEWIM drives can be used in industries and electric vehicles but they entail ripple-free torque. Predictive torque control (PTC) strategy offers high dynamic performance and lesser ripple in torque, flux when compared with direct torque control. Classical PTC involves high switching frequencies and empirical methods to select weighting factors. The selection and tuning of weighting factors are cumbersome. In this article, a new normalised weighted sum model (WSM) based PTC of four-level inverter fed OEWIM is introduced to curtail torque, flux ripples, switching frequency and enhance the selection of weighting factors. The proposed algorithm uses multi-objective cost function and the optimisation of cost function is performed by using normalised WSM. The normalisation of individual cost function simplifies the selection of weighting factors to select optimal voltage vector. As a result, the proposed PTC offers all the features of classical PTC and overcomes the difficulties involved in classical PTC. Simulation and experimental studies are performed on dual inverter fed OEWIM with four-level inversion. The effectiveness of proposed algorithm is verified by comparing proposed PTC algorithm with classical PTC algorithm.

Finite-Set Model-Predictive Control Strategies for a 3L-NPC Inverter Operating With Fixed Switching Frequency

In this paper, finite-set model-predictive control (FS-MPC) methodologies for a grid-connected three-level neutral-point-clamped converter are investigated. The proposed control strategies produce fixed switching frequency, maintaining all the advantages of predictive control such as fast dynamic response, inclusion of nonlinearities and restrictions, and multivariable control using a single control loop. The first of the proposed FS-MPC strategies is based on a multiobjective cost function, designed to regulate both the inverter currents and the balancing of the dc-link capacitor voltages. The second FS-MPC strategy is derived from the first one, and it is based on a cost function that regulates only the grid current, with the balancing of the capacitor voltages being realized by controlling the duty cycles of the redundant vectors. The proposed control systems are experimentally validated using a 5-kW prototype. The experimental results show a good performance for both strategies, in steady-state and transient response, with a total harmonic distortion lower than 2% for the currents supplied to the grid.

Adversarial Stain Transfer for Histopathology Image Analysis.

It is generally recognized that color information is central to the automatic and visual analysis of histopathology tissue slides. In practice, pathologists rely on color, which reflects the presence of specific tissue components, to establish a diagnosis. Similarly, automatic histopathology image analysis algorithms rely on color or intensity measures to extract tissue features. With the increasing access to digitized histopathology images, color variation and its implications have become a critical issue. These variations are the result of not only a variety of factors involved in the preparation of tissue slides but also in the digitization process itself. Consequently, different strategies have been proposed to alleviate stain-related tissue inconsistencies in automatic image analysis systems. Such techniques generally rely on collecting color statistics to perform color matching across images. In this work, we propose a different approach for stain normalization that we refer to as stain transfer. We design a discriminative image analysis model equipped with a stain normalization component that transfers stains across datasets. Our model comprises a generative network that learns data set-specific staining properties and image-specific color transformations as well as a task-specific network (e.g., classifier or segmentation network). The model is trained end-to-end using a multi-objective cost function. We evaluate the proposed approach in the context of automatic histopathology image analysis on three data sets and two different analysis tasks: tissue segmentation and classification. The proposed method achieves superior results in terms of accuracy and quality of normalized images compared to various baselines.

Design and Development of a Multiobjective Cost Function for Robust Video Watermarking Using Wavelet Transform

Abstract Watermarking is the process of concealing the secret message into multimedia sources, such as audio, image, and video. Rather than other steganography, video watermarking is mainly focused on the robustness of the system. In this paper, we propose the multiobjective cost function for video watermarking. Initially, the cover image (video frame) is subjected into cost function computation. Then, the cost function is newly designed and developed by various objectives, such as intensity, energy, edge, coverage, and brightness. Subsequently, the wavelet transform is applied to the original frame that acquires a wavelet coefficient-based video frame. Simultaneously, the secret message is partitioned into binary images using the bit plane technique. According to the cost value, the message bit is embedded into the wavelet coefficients in the embedding phase. In the extraction phase, the secret message is retrieved. Finally, the experimental results are validated, and performance is analyzed using metrics, correlation coefficients, and peak-to-signal noise ratio (PSNR). From the results, it can be shown that the proposed method attains 98.19 dB PSNR and 1.00 correlation coefficient value to provide better robustness of the system.