Designed Cost Function Research Articles

Model Predictive Control Strategies for Turbine Electrified Energy Management

Abstract The increasing electrification of aircraft propulsion systems is leading to new control architectures being developed to address integration between electric machines (EMs) and gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures often couple electric machines with the shafts of gas turbine engines and introduce energy storage. Leveraging the electrical power system of hybridized engines, turbine electrified energy management (TEEM) is a recent control approach that improves transient operability in an effort to enable more efficient and lighter weight turbomachinery. This study seeks to expand TEEM's application beyond traditional proportional-integral (PI) control by presenting linear model predictive control (MPC) schemes to execute the TEEM concept. Through constraint selection and cost function design, transient operability goals for TEEM are considered with no external logic or saturation. Unique to the designs are the use of a washout filter, which simplifies transient detection and motor activation logic. The proposed architectures are implemented with both centralized MPC (CMPC) and distributed MPC (DMPC) approaches, and comparisons are drawn to a benchmark PI controller simulated on a nonlinear turbofan engine model at one ground condition and one cruise condition. Performance is evaluated using compressor maps, stall margin performance, and two novel metrics: transient stack usage (TSU) and transient excursion integral (TEI). Results reveal that the linear MPC scheme performs comparably to the baseline controller and can be implemented in at least two distinct configurations with potential for further modifications, thus establishing the groundwork for future investigations.

Discrete‐Time Optimal Control of State‐Constrained Nonlinear Systems Using Approximate Dynamic Programming

ABSTRACTThis article investigates the optimal control problem (OCP) for a class of discrete‐time nonlinear systems with state constraints. First, to overcome the challenge caused by the constraints, the original constrained OCP is transformed into an unconstrained OCP by utilizing the system transformation technique. Second, a new cost function is designed to alleviate the effect of system transformation on the optimality of the original system. Further, a novel off‐policy deterministic approximate dynamic programming (ADP) scheme is developed to obtain a near‐optimal solution for the transformed OCP. Compared to existing off‐policy deterministic ADP schemes, the developed scheme relaxes the requirement on the learning data and saves computing resources from the perspective of training neural networks. Third, considering approximation errors, we analyze the convergence and stability of the developed ADP scheme. Finally, the developed ADP with the designed cost function is tested in two numerical cases, and simulation results confirm its effectiveness.

DIRT/µ - Automated extraction of root hair traits using combinatorial optimization.

Similar to any microscopic appendages, such as cilia or antennae, phenotyping of root hairs has been a challenge due to their complex intersecting arrangements in two-dimensional (2D) images and the technical limitations of automated measurements. Digital Imaging of Root Traits at Microscale (DIRT/μ) addresses this issue by computationally resolving intersections and extracting individual root hairs from 2D microscopy images. This solution enables automatic and precise trait measurements of individual root hairs. DIRT/μ rigorously defines a set of rules to resolve intersecting root hairs and minimizes a newly designed cost function to combinatorically identify each root hair in the microscopy image. As a result, DIRT/μ accurately measures traits such as root hair length (RHL) distribution and root hair density (RHD), which are impractical for manual assessment. We tested DIRT/μ on three datasets to validate its performance and showcase potential applications. By measuring root hair traits in a fraction of the time manual methods require, DIRT/μ eliminates subjective biases from manual measurements. Automating individual root hair extraction accelerates phenotyping and quantifies trait variability within and among plants, creating new possibilities to characterize root hair function and their underlying genetics.

Enhanced Model-Free Predictive Speed Control Without Weighting Factors for PMSM Based on Newly Designed Cost Function

Enhanced Model-Free Predictive Speed Control Without Weighting Factors for PMSM Based on Newly Designed Cost Function

Finite control set model predictive current control for three phase grid connected inverter with common mode voltage suppression

This research introduces an advanced finite control set model predictive current control (FCS-MPCC) specifically tailored for three-phase grid-connected inverters, with a primary focus on the suppression of common mode voltage (CMV). CMV is known for causing a range of issues, including leakage currents, electromagnetic interference (EMI), and accelerated system degradation. The proposed control strategy employs a system model that predicts the inverter’s future states, enabling the selection of optimal switching states from a finite set to achieve dual objectives: precise current control and effective CMV reduction, a meticulously designed cost function evaluates the potential switching states, balancing the accuracy of current tracking against the necessity to minimize CMV. The approach is grounded in a comprehensive mathematical model that captures the dynamics of CMV within the system, and it utilizes an optimization process that functions in real-time to determine the most suitable control action at each interval, Experimental validations of the proposed FCS-MPCC scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters, Experimental validations of the proposed (MPC with CMV) scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters. The proposed method achieved substantial reductions in CMV, notable improvements in current tracking accuracy, and extended system lifespan compared to conventional control methods.

Quad-functioning parity layout for nanocomputing: A QCA design

Quantum dot cellular automata (QCA) is considered an alternative to conventional technologies like CMOS (Complementary Metal-Oxide-Semiconductor) technology due to its potential for lower power consumption, higher speed, and increased device density. QCA introduces a novel approach to designing nano communication circuits and systems. Nano communications data mistakes are detected via parity generators and checkers. The parity bit of each data block ensures that the number of 1′s is either even or odd. Consequently, the system requires four circuits: an even parity generator, an odd parity generator, an even parity checker, and an odd parity checker. The whole system requires more space and cell complexity. In this work, we propose a QCA architecture that serves as a generator for both even and odd parities, as well as a checker for both even and odd parities. It is a quad-functioning circuit that performs four distinct operations within a single design, utilizing 118 QCA cells and occupying an area of 0.17 μm2. The recommended approach uses an efficient XOR gate, resulting in improvements across several performance metrics. QCAPro calculates energy dissipation and design parameters. The recommended QCA circuit outperformed similar QCA circuits in size, complexity, and energy dissipation. The circuit's design cost functions are also low. There has been a 17% reduction in latency and an 86% improvement in QCA-specific costs when compared to the optimal existing design. Moreover, it necessitates a 40% reduction in majority gate usage. The proposed design may compete effectively with other equivalent higher-order circuit designs by reducing the need for multiple blocks in conventional circuits to execute the same task. This architecture holds potential benefits for nano processors and nano communication networks.

Optimization of robot manipulator configuration calibration by using Zhang neural network for repetitive motion

High precision and low complexity control algorithm plays an important role in the developing of the end-effector instrumentation of different robot manipulators. In order to reduce the kinetic energy and the high-speed drift phenomenon of the repetitive motion tracking task, the robot manipulator needs to calibrate its configuration. In this paper, we formulate the configuration calibration of the robot manipulator for the repetitive motion task as a future quadratic programming optimization problem constrained with equality constraints, which is also regarded as a fundamental problem in artificial intelligence and modern control engineering. Zhang neural network, which is a canonical method, can be adopted to deal with the continuous form of the future optimization problem, named as temporally dependent quadratic programming problem with equality constraints. In order to overcome the issue of temporally dependent inverse computing, a novel Zhang neural network model and its uncertain disturbance tolerant model, which are termed as filtered reciprocal-kind Zhang neural network model and uncertain disturbance tolerant filtered reciprocal-kind Zhang neural network model, respectively, are proposed by integrating the energy-type cost function and Zhang neural network design formula for solving the temporally dependent quadratic programming problem with equality constraints in this paper. Based on the Euler discrete formula and the models, the discrete filtered reciprocal-kind Zhang neural network and the discrete uncertain disturbance tolerant filtered reciprocal-kind Zhang neural network algorithms are proposed for solving the future quadratic programming problem with equality constraints and the robot manipulator configuration calibration problem of repetitive motion. The convergence properties of the reciprocal-kind Zhang neural network model and its corresponding uncertain disturbance tolerant model are obtained by Lyapunov stability theory of nonlinear system and its corresponding perturbed system, while the convergence property of the filtered reciprocal-kind Zhang neural network model is analyzed by the limit thinking. For the repetitive motion task, three experiments for solving the configuration calibration problem of PUMA560, Kinova Jaco2, and Franka Emika Panda robot manipulators are performed to illustrate the effectiveness, robustness and superiority of our proposed discrete filtered reciprocal-kind Zhang neural network algorithms.

Additional Acceleration of Antenna Optimal Characterizationwith Modeling Support

Optimal characterization is assumed to provide the best solution for the designed cost function among the possible solutions within the specified range. These processes can take a long time depending on the applications and computer hardware used. Here, the optimization process is supported by ANN modeling in order to shorten the current optimization processes as much as possible. For this purpose, the selection of design parameters of the bowtie patch antenna (BPA) is presented as a multi-dimensional, multi-objective modeling-supported design optimization problem. The operating frequency of the proposed antenna is 28 GHz, which is the standard for millimeter wave band and 5G technologies. To overcome this challenging design optimization, a new, fast and powerful optimization algorithm was used by modifying the non-dominant sorting genetic algorithm (NSGA)-III, and the optimal characterization of the microwave antenna design was achieved. Although the proposed method gives the same results compared to the existing process, it takes much less time. Therefore, it is possible to shorten the process and reduce costs without the need for extra applications or hardware. As a whole, the proposed design optimization process is an efficient, fast and reliable solution for all design problems.

3D multi-object tracking based on informatic divergence-guided data association

In the existing literature, most 3D multi-object tracking algorithms based on the tracking-by-detection framework employed deterministic tracks and detections for similarity calculation in the data association stage. Namely, the inherent uncertainties existing in tracks and detections are overlooked. In this work, we discard the commonly used deterministic tracks and deterministic detections for data association, instead, we propose to model tracks and detections as random vectors in which uncertainties are taken into account. Then, based on a modified Kullback-Leibler divergence, the similarity between two multidimensional distributions, i.e. track and detection, is evaluated for data association purposes. Lastly, the level of track uncertainty is incorporated in our cost function design to guide the data association process. Comparative experiments have been conducted on two typical datasets, KITTI and nuScenes, and the results indicated that our proposed method outperformed the compared state-of-the-art 3D tracking algorithms. For the benefit of the community, our code has been made available at https://github.com/hejiawei2023/UG3DMOT.

Reward (Mis)design for Autonomous Driving (Abstract Reprint)

This article considers the problem of diagnosing certain common errors in reward design. Its insights are also applicable to the design of cost functions and performance metrics more generally. To diagnose common errors, we develop 8 simple sanity checks for identifying flaws in reward functions. We survey research that is published in top-tier venues and focuses on reinforcement learning (RL) for autonomous driving (AD). Specifically, we closely examine the reported reward function in each publication and present these reward functions in a complete and standardized format in the appendix. Wherever we have sufficient information, we apply the 8 sanity checks to each surveyed reward function, revealing near-universal flaws in reward design for AD that might also exist pervasively across reward design for other tasks. Lastly, we explore promising directions that may aid the design of reward functions for AD in subsequent research, following a process of inquiry that can be adapted to other domains.

2-SAT discrete Hopfield neural networks optimization via Crow search and fuzzy dynamical clustering approach

<abstract> <p>Within the swiftly evolving domain of neural networks, the discrete Hopfield-SAT model, endowed with logical rules and the ability to achieve global minima of SAT problems, has emerged as a novel prototype for SAT solvers, capturing significant scientific interest. However, this model shows substantial sensitivity to network size and logical complexity. As the number of neurons and logical complexity increase, the solution space rapidly contracts, leading to a marked decline in the model's problem-solving performance. This paper introduces a novel discrete Hopfield-SAT model, enhanced by Crow search-guided fuzzy clustering hybrid optimization, effectively addressing this challenge and significantly boosting solving speed. The proposed model unveils a significant insight: its uniquely designed cost function for initial assignments introduces a quantification mechanism that measures the degree of inconsistency within its logical rules. Utilizing this for clustering, the model utilizes a Crow search-guided fuzzy clustering hybrid optimization to filter potential solutions from initial assignments, substantially narrowing the search space and enhancing retrieval efficiency. Experiments were conducted with both simulated and real datasets for 2SAT problems. The results indicate that the proposed model significantly surpasses traditional discrete Hopfield-SAT models and those enhanced by genetic-guided fuzzy clustering optimization across key performance metrics: Global minima ratio, Hamming distance, CPU time, retrieval rate of stable state, and retrieval rate of global minima, particularly showing statistically significant improvements in solving speed. These advantages play a pivotal role in advancing the discrete Hopfield-SAT model towards becoming an exemplary SAT solver. Additionally, the model features exceptional parallel computing capabilities and possesses the potential to integrate with other logical rules. In the future, this optimized model holds promise as an effective tool for solving more complex SAT problems.</p> </abstract>

An efficient distortion cost function design for image steganography in spatial domain using quaternion representation

It is well known that an effective steganographic distortion cost function is essential for spatial image steganography in the framework of minimal distortion embedding. To define the embedding cost by exploiting the texture complexity characterized by the high-pass filtered image residuals has been a popular approach. In this paper, by incorporating the quaternion representation of cover images, the image complexity is evaluated in terms of quaternion magnitude and phase, and a quaternion magnitude-based distortion (QMD) and a quaternion phase-based distortion (QPD) can be defined accordingly. With Hadamard product of QMD and QPD, a novel distortion cost function, namely QMP (Quaternion Magnitude-Phase), is obtained, aiming to efficiently allocate embedding modifications in complex regions of cover images with rich texture contents to improve the statistical undetectability (i.e., security). In addition, the generalized QMP (GQMP) in exponential form is further developed to boost the security performance. Experimental results demonstrate that both the proposed QMP and its generalized variant GQMP can outperform the state-of-the-art schemes, e.g., S-UNIWARD, HiLL, and MiPOD, in terms of empirical security performance against steganalysis.

A real‐time critical‐scenario‐generation framework for defect detection of autonomous driving system

AbstractTo find the most‐likely‐failure scenarios given a certain operation domain, a critical‐scenario‐based test is supposed as an effective method. However, for the state of the art, critical‐scenario‐generation approaches commonly based on random‐search and take amounts of computing resource, some of them are also inapplicable in real time. Moreover, the approaches sometimes fail to obtain critical results, which are strongly relevant to the choice of initial condition. In order to address the above challenges, the authors proposed a Real‐time Critical‐scenario‐generation framework in this paper. The authors proposed an aggressive‐driving algorithm based on the model predictive control method to lead the agent vehicle. The agent vehicle will be controlled to directly create critical scenarios for a black‐box target under test, and the real‐time critical‐scenario test can be brought into reality. A specially designed cost function is presented that guides scenarios to evolve towards the interested conditions, and a self‐adaptive coefficient iteration is designed that enables the approach to be applied within a wider range of initial conditions. The authors carried out both simulation and Vehicle‐in‐the‐Loop (VIL) test; in the VIL test, the authors’ approach improves 15.45% criticality of scenarios with around 9.7 times of efficiency, or improves 38.67% criticality with still around 1.7 times of efficiency with further iterations.

Model Predictive Control of a PUC5-Based Dual-Output Electric Vehicle Battery Charger

In this study, a model predictive control (MPC) technique is applied to a packed-u-cell (PUC)-based dual-output bidirectional electric vehicle (EV) battery charger. The investigated topology is a 5-level PUC-based power factor correction (PFC) rectifier allowing the generation of two levels of DC output voltages. The optimization of the MPC cost function is performed by reducing the errors on the capacitors’ voltages (DC output voltages) and the grid (input) current. Moreover, the desired capacitors’ voltages and peak value of the input current are considered within the designed cost function to normalize the errors. In addition, an external PI controller is used to generate the amplitude of the grid current reference based on the computed errors on the capacitors’ voltages. The presented simulation and experimental results recorded using a 1 kW laboratory prototype demonstrate the high performance of the proposed approach in rectifying the AC source at different levels (dual rectifier), while drawing a sinusoidal current from the grid with low THD (around 4%) and ensuring a unity power factor operation.

Methodology for energy management strategies design based on predictive control techniques for smart grids

This article focuses on the development of a general energy management system (EMS) design methodology using on model-based predictive control (MPC) for the control and management of microgrids. Different MPC-based EMS for microgrids have been defined in the literature; however, there is a lack of generality in the proposed that would facilitate adapting to new architectures, energy storage system technology, nature of the bus, application, or purpose. To fill this gap, a novel general formulation that is parameterizable, simple, easily interpretable, and reproducible in different microgrid architectures is presented. This is the result of the development of a novel methodology, which is also presented. It considers the state space formulation of the controller from the initial modelling phase, from the dynamics of the energy storage systems represented by their models to the subsequent definition of the optimisation problem. This is developed through the design of the general cost function and the formulation of constrains, by means of general guidelines and reference values. To evaluate the performance of the developed methodology, simulation tests were carried out for four different microgrid architectures, with different applications and objectives, also considering different generation conditions, demand profiles, and initial conditions. The results showed that, with some simple guidelines and regardless of the case study, the developed MPC controller design methodology can address the technical-economic optimisation problem associated with energy management in microgrids in an easy and intuitive way.

Nonsmooth funnel function-based model-free adaptive sliding-mode control for discrete-time nonlinear systems

This paper proposes a nonsmooth funnel function (NFF)-based model-free adaptive sliding-mode control strategy for the discrete-time nonlinear systems with unknown dynamics and asymmetric constraints. The dynamic linearization model of the nonlinear systems with unknown dynamics is obtained online only using the input/output data, which is combining with the designed NFF-based discrete-time sliding-mode surface to guarantee asymmetric constraints of the tracking error. In the proposed NFF, the coordinate transformation for asymmetric constraints simplifies the controller design, and the nonsmooth parameter can improve the transient and steady-state performances of the tracking error. The control consumption is further reduced by a proposed time-varying weighting factor-based cost function in the controller design. Furthermore, parameter selections of performance boundaries and the sliding-mode surface are given, and the performance margin (the minimum distance from the tracking error to the preset boundaries) is proved theoretically. Simulation and experimental results verify the effectiveness of the proposed control strategy.

NMPC-Based Integrated Thermal Management of Battery and Cabin for Electric Vehicles in Cold Weather Conditions

One of the major obstacles along the way of electric vehicles' (EVs') wider global adoption is their limited driving range. Extreme cold or hot environments can further impact the EV's range as a significant amount of energy is needed for cabin and battery temperature regulation while the battery's power and energy capacity are also impeded. To overcome this issue, we present an optimal control strategy based on nonlinear model predictive control (NMPC) for integrated thermal management (ITM) of the battery and cabin of EVs, where the proposed NMPC simultaneously optimizes the EV range and cabin comfort in real time. Firstly, the components of the designed ITM system are introduced and control-oriented modeling is done. Secondly, to demonstrate and validate the benefits of the proposed ITM, an optimal control problem is defined and dynamic programming (DP) is employed to find the global optimal solution. Thirdly, for practical implementation, NMPC-based control strategy is developed, where the cost function design and weights calibration are done in comparison with DP global optimal solution. Weight-tuning results show that our NMPC-based approach can achieve close driving range maximization as compared to the DP benchmark while ensuring cabin comfort. The developed NMPC-based ITM strategy is further illustrated by comparing its performance to two additional benchmark strategies, i.e., rule-based control and cabin heating only. Finally, our simulation results also identify several important factors that impact the benefits of the proposed NMPC-based ITM, which are used to summarize the operating conditions under which the proposed ITM is critically needed.

Model-Predictive Control of Multilevel Inverters: Challenges, Recent Advances, and Trends

Model predictive control (MPC) has emerged as a promising control method in power electronics, particularly for multi-objective control problems such as multilevel inverter (MLI) applications. Over the past two decades, improving the performance of MPC and tackling its technical challenges such as computational load, modeling accuracy, cost function design and weighting factors selection have attracted great interest in power electronics. This article aims to discuss the current state of MPC strategies for MLI applications, describing the significance of each challenge with the reported effective solutions. Through this review, the MPC methods are categorized into two groups, direct MPC (without modulator) and indirect MPC (with modulator). The recent advances of each category are presented and analyzed, focusing on direct MPC as the most applied method for MLI topologies. In addition, some of the important concepts are experimentally validated through a case study and compared under the same operating conditions to evaluate the performance and highlight their features. Finally, the future trends of MPC for MLI applications are discussed based on the current state and reported developments.

Design of cost functions for the real-time control of microgrids hosting distributed energy-storage systems

Design of cost functions for the real-time control of microgrids hosting distributed energy-storage systems

Continuous-Control-Set Model Predictive Current Control of Asymmetrical Six- Phase Drives Considering System Nonidealities

Finite-control-set model predictive control (FCS-MPC) of multiphase ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -phase, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> is assumed to be an odd number for simplicity) drives is challenging because of the large number of actual/virtual voltage vectors and the need for current control in ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -1)/2 sub-spaces (or planes; multi-plane current control). Any sub-optimal design (poor or no current control in some of the ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -1)/2 planes) may result in high individual plane current ripples, due to the low reactance. This work therefore investigates continuous-control-set (CCS) MPC for constant switching frequency multiphase motor drives as another alternative. The high-bandwidth CCS-MPC is designed to accurately account for system non-idealities, namely digital control and pulse width modulation delays, inverter dead time, and measurement noise. It will be shown that the CCS-MPC has the advantages of full voltage vector space access, regular switching characteristic, and improved cycle-by-cycle tracking control, while maintaining some of the known advantages of the FCS-MPC, e.g., intuitive cost function design, model-based control, and fast dynamics. The proposed control scheme is benchmarked experimentally against the classical, proportional-integral-based, field-oriented control in conjunction with an asymmetrical six-phase induction motor drive.