Designed Cost Function Research Articles

Dynamics-Aware Metric Embedding: Metric Learning in a Latent Space for Visual Planning

In this letter, we consider vision-based control tasks of which the desired goals are given as target images. The problems are often addressed by an autonomous agent which optimizes a trajectory to minimize a manually designed cost function. However, it is challenging to design a suitable cost function for each goal by hand, especially, when the current and the goal states of the system are only described by visual observations. In order to tackle this issue, we propose a method called dynamics-aware metric embedding (DAME), which generates cost functions in a self-supervised manner to help the agent plan the controls to accomplish the desired goals. The proposed method learns a metric function that reflects how easy to find a path connecting two states considering the dynamics of the system. To learn the metric between states, we utilize a measure named probabilistic reachability, which is computed using the probability of reaching from one state to the other state via random walk. We evaluate the proposed method in several vision-based control tasks, including both various simulation benchmarks and real-world table-top manipulation tasks, and observe that DAME outperforms other baseline algorithms by over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {\text{30}\%}$</tex-math></inline-formula> in the terms of success rate.

FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to perform military missions autonomously. This paper proposes a novel algorithm called flight cost-based Rapidly-exploring Random Tree star (FC-RRT*) extending the standard Rapidly-exploring Random Tree star (RRT*) to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment. First, a flight cost function that includes threat strength and path length was designed to comprehensively evaluate the connection between two path nodes. Second, in order to solve the UAV path planning problem from the front-end, the flight cost function and flight constraints were used to inspire the expansion of new nodes. Third, the designed cost function was used to guide the update of the parent node to allow the algorithm to consider both the threat and the length of the path when generating the path. The simulation and comparison results show that FC-RRT* effectively overcomes the shortcomings of standard RRT*. FC-RRT* is able to plan an optimal path that significantly improves path safety as well as maintains has the shortest distance while satisfying flight constraints in the complex environment. This paper has application value in UAV 3D global path planning.

An enhanced finite control set model predictive control method with self‐balancing capacitor voltages for three‐level T‐type rectifiers

An effective finite control set model predictive control (FCS-MPC) is introduced for single-phase three-level T-type rectifiers supplying resistive as well as constant power loads (CPL). The main problem of CPL is the negative resistance phenomenon that endangers the rectifier's stability. Hence, the proposed FCS-MPC method is based on Lyapunov's stability theory such that the stability of the rectifier is guaranteed under all operating points. Unlike the existing FCS-MPC methods, the cost function design in the proposed control method is formulated on the rectifier's stability. According to Lyapunov's stability theory, the rectifier stays stable provided that the rate of change of Lyapunov function is negative. In this case, the derivative of the Lyapunov function can be used as the cost function without utilizing any weighting factor. Therefore, contrary to the existing FCS-MPC methods, the weighting factor requirement is eliminated which leads to easiness in the design and implementation of the controller. Experimental results reveal that the proposed control approach exhibits very good performance with undistorted and distorted grid voltage conditions when the rectifier feeds resistive and CPL loads.

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

Model Predictive Control of Non-Isolated DC/DC Modular Multilevel Converter Improving the Dynamic Response

The model predictive control (MPC) is a well-accepted method for controlling power electronic converters. This paper presents a tailored MPC approach in which the internal and external dynamics of the dc/dc modular multilevel converter (MMC) are integrated into the MPC algorithm. The proposed MPC approach introduces three control objectives to have full control over the internal and external dynamics. Each of the designed cost functions includes one primary term regulating one of the control objectives and one secondary term improving the converter performance. Unlike the conventional control approach based on multiple proportional-integral (PI) controllers, the proposed approach provides a straightforward way to design the control parameters. The operation of the presented MPC approach is thoroughly investigated and compared to that of the PI-based controller. Comparative simulation studies confirmed that the proposed MPC approach, compared to the conventional PI-based control, reduces the ac circulating current in the steady-state operation. In the transient mode, the MPC approach offers much smoother and faster responses to the changes in the power reference. The performance of the dc/dc MMC controlled by the proposed MPC approach under parametric uncertainty is investigated, and improved performance is obtained compared to the conventional PI-based control.

A Simple and Robust Model Predictive Current Control of PMSM Using Stator Current Predictor and Target-Oriented Cost Function

Model predictive current control of permanent magnet synchronous motor(PMSM), which inherits from the vector control framework, has become a promising control strategy because of its simple structure and fast dynamic response. In order to protect the stator current prediction model from the influence of mismatched parameters, a simple and robust predictive current control with stator current predictor and target-oriented cost function (abbreviated as SCP-TOSF-PCC) is proposed in this paper. The feedback mechanism is introduced into the prediction equation in the discrete domain, and its design principle and stability analysis are also described in detail. In addition, different from the traditional cost function design method which is constructed by the tracking errors of stator current, this paper proposes a direct target-oriented cost function, which takes the average pulsations of d-axis current and q-axis current as the judge index. The design method of this cost function takes into account the influence of historical values on the voltage vector selection. The experimental results show that compared with the traditional methods, the proposed method has better dynamic, steady-state and robust performance.

Dynamic Computer-Aided Orchestration in Practice with Orchidea

Abstract The problem of target-based computer-aided orchestration is a recurring topic in the contemporary music community. Because of its complexity, computer-aided orchestration remains a partially unsolved problem and several systems have been developed in the last twenty years. This article presents a practical overview of the recently introduced Orchidea framework for dynamic computer-aided target-based orchestration. Orchidea continues the line of tools dedicated to the subject (the so-called Orchid* family) originally developed at the Institut de Recherche et Coordination Acoustique/Musique in Paris. Unlike its predecessors, Orchidea uses a combination of optimization techniques that include stochastic matching pursuit, long short-term memory neural networks, and monoobjective evolutionary optimization, with a specifically designed cost function. Symbolic constraints can be integrated in the cost function, and temporally evolving sounds are handled by segmenting them into a set of static targets optimized jointly and then connected. Orchidea is deployed in three different ways: a standalone application, designed to streamline a simplified compositional workflow; a Max package, targeted at composers willing to connect target-based orchestration to the more general area of computer-aided composition; and a set of command-line tools, mostly intended for research purposes and batch processing. The main aim of this article is to present an overview of such software systems and show several instances of the Orchidea framework's application in recent musical productions, tracing the path for future research on the subject.

Analytic Programming – a Novel Tool for Synthesis of Controller for Chaotic Lozi Map

In this paper, it is presented a utilization of a novel tool for symbolic regression, which is analytic programming, for the purpose of the synthesis of a new feedback control law. This new synthesized chaotic controller secures the fully stabilization of selected discrete chaotic systems, which is the two-dimensional Lozi map. The paper consists of the descriptions of analytic programming as well as selected chaotic system, used heuristic and cost function design. For experimentation, Self-Organizing Migrating Algorithm (SOMA) and Differential evolution (DE) were used. Two selected experiments are detailed described.

Dispatching to parallel servers

Policy iteration techniques for multiple-server dispatching rely on the computation of value functions. In this context, we consider the continuous-space M/G/1-FCFS queue endowed with an arbitrarily designed cost function for the waiting times of the incoming jobs. The associated relative value function is a solution of Poisson’s equation for Markov chains, which in this work we solve in the Laplace transform domain by considering an ancillary, underlying stochastic process extended to (imaginary) negative backlog states. This construction enables us to issue closed-form relative value functions for polynomial and exponential cost functions and for piecewise compositions of the latter, in turn permitting the derivation of interval bounds for the relative value function in the form of power series or trigonometric sums. We review various cost approximation schemes and assess the convergence of the interval bounds these induce on the relative value function, namely Taylor expansions (divergent, except for a narrow class of entire functions with low orders of growth) and uniform approximation schemes (polynomials, trigonometric), which achieve optimal convergence rates over finite intervals. This study addresses all the steps to implementing dispatching policies for systems of parallel servers, from the specification of general cost functions toward the computation of interval bounds for the relative value functions and the exact implementation of the first-policy improvement step.

Depth-based removal of thermal reflection with the light-field theory.

Thermal imaging is a useful imaging technique in many scenarios. It can capture the temperature distribution of scenes in the dark and see through sparse smoke and dust. However, some surfaces such as steel and glass with high reflectivity lead to a reflection problem in thermal imaging, while heavy mist and gases lead to the occlusion problem. We proposed an efficient algorithm to solve the occlusion problem in our earlier work. The reflection in thermal images causes errors in detection and temperature measurement. Therefore, the precise model and efficient algorithms to solve this problem are in high demand. In this paper, we mainly model the reflection problem in thermal imaging and propose an algorithm to deal with it. In our experiments, a thermal camera array is built to capture the thermal light-field images. We first separate a part of the reflection pixels from thermal images based on the depth information. After that, the thermal reflection is removed by optimizing a designed cost function. The experiment results show that our reflection removal method can separate the thermal reflection with high precision, retain the objects in the scene, and get better performance than existing methods.

Adaptive Cost Function FCSMPC for 6-Phase IMs

In this paper, an adaptive cost function FCSMPC is derived from newly obtained results concerning the distribution of figures of merits used for the assessment of stator current model-based control of multi-phase induction machines. A parameter analysis of FCSMPC is carried out for the case of a six-phase motor. After extensive simulation and Pareto screening, a new structure has been discovered linking several figures of merit. This structure provides an simple explanation for previously reported results concerning the difficulty of cost function tuning for FCSMPC. In addition, the newly discovered link among figures of merit provides valuable insight that can be used for control design. As an application, a new cost function design scheme is derived and tested. This new method avoids the usual and cumbersome procedure of testing many different controller parameters.

Cost- and Risk-Based Seismic Design Optimization of Nuclear Power Plant Safety Systems

Seismic analysis, design, and qualification of systems, structures, and components (SSCs) is a significant contributor to the capital cost of a nuclear power plant. To reduce capital costs of advanced nuclear power plants and make commercial nuclear energy more competitive, innovations are needed in their structural design and construction, and not just in the reactor core and associated systems. Seismic isolation has been identified as an important cost-cutting technology that enables standardization of equipment across various sites. This paper develops and demonstrates a cost- and risk-based seismic design optimization of a representative safety system in a nuclear power plant with the dual goals of minimizing overnight capital cost and meeting safety goals. The design optimization can also include component seismic isolation, in which case, the optimized design includes a set of equipment that needs to be seismically isolated to minimize capital cost. The open-source codes MASTODON and Dakota are used for seismic probabilistic risk assessment and design optimization, respectively. A generic nuclear facility with a safety system comprising SSCs that are common to nuclear power plants is considered for the demonstration of the design optimization and is assumed to be located at the Idaho National Laboratory site. Generic costs and seismic design cost functions are assumed for the SSCs of the safety system. The sum of the costs of the SSCs is minimized in the optimization process, while the risk of failure of the safety system is provided as a constraint. Results show that the optimization process reduces capital costs significantly while automatically prioritizing the safety of SSCs that contribute most to the risk of the safety system.

Swarm intelligence inspired meta-heuristics for solving multi-constraint QoS path problem in vehicular ad hoc networks

Applications and services which run on top of vehicular ad hoc networks range from human safety and traffic management to assisted driving and real time battlefield communication. Many of these applications may have low-elasticity QoS requirements. Characteristics like high mobility of vehicles, dynamic topology, inherent distributed nature and consequent lack of central coordination make it challenging to provide QoS support in such networks. Since, QoS provisioning and path finding can be potentially handled together, this work aims at exploiting such a possibility. Consequently, an attempt has been made to solve the multi-constraint QoS optimal path problem by using an innovative combination of swarm intelligence based optimization strategies and an especially designed cost function. This work also presents a detailed theoretical as well as experimental analysis of the proposed algorithms, probably first of its kind. Additionally, the analysis part also investigates the applicability issues and related solutions considering select algorithms in view of solving the given problem for vehicular ad hoc networks. Meta-heuristics derived in this process have been applied on realistic modelled vehicular ad hoc network topology and as the presented analysis would indicate results are encouraging.

Analytical Models for Optimal Design of a Trapezoidal Composite Channel Cross-Section

Abstract In this paper, several analytical models are presented for the optimal design of a trapezoidal composite channel cross-section. The objective function is the cost function per unit length of the channel, which includes the excavation and lining costs. To define the system, design variables including channel depth, channel width, side slopes, freeboard, and roughness coefficients were used. The constraints include Manning’s equation, flow velocity, Froude number, and water surface width. The Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm was used to solve the optimization problem. The results are presented in three parts; in the first part, the optimal values of the design variables and the objective function are presented in different discharges. In the second part, the relationship between cost and design variables in different discharges is presented in the form of conceptual and analytical models and mathematical functions. Finally, in the third part, the changes in the design variables and cost function are presented as a graph based on the discharge variations. Results indicate that the cost increases with increasing water depth, left side slope, equivalent roughness coefficient, and freeboard.

A Novel Channel Phase Error Calibration Method Based on Hybrid AFSA-GSO-GA for Multichannel HRWS-SAR Imaging

The spaceborne high-resolution wide-swath synthetic aperture radar (HRWS-SAR) system generally does not meet the optimal SAR imaging configuration, and thus, it is necessary to apply digital beam-forming filtering technology to restore the nonuniformly sampled signal into the uniform grids. However, in practice, because of the influences of temperature, receiving machine, and other error factors, the channel errors may possibly exist, causing the SAR image to be smeared. To address this issue, this letter proposes a novel algorithm based on the hybrid artificial fish school algorithm–glowworm swarm optimization–genetic algorithm (AFSA-GSO-GA) to address the channel imbalance issue. First, coarse HRWS-SAR imaging processing is performed to obtain the positions of the Doppler ambiguity components. Then, according to the designed cost function, the hybrid AFSA-GSO-GA algorithm is used to realize the channel phase error estimation. Finally, a well-focused SAR image can be obtained after channel balance. The effectiveness of the proposed method is validated by both simulated and real SAR data.

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

With the increasing population of Industry 4.0, both AI and smart techniques have been applied and become hotly discussed topics in industrial cyber-physical systems (CPS). Intelligent anomaly detection for identifying cyber-physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this article, we propose a few-shot learning model with Siamese convolutional neural network (FSL-SCNN), to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPS. A Siamese CNN encoding network is constructed to measure distances of input samples based on their optimized feature representations. A robust cost function design including three specific losses is then proposed to enhance the efficiency of training process. An intelligent anomaly detection algorithm is developed finally. Experiment results based on a fully labeled public dataset and a few labeled dataset demonstrate that our proposed FSL-SCNN can significantly improve false alarm rate (FAR) and F1 scores when detecting intrusion signals for industrial CPS security protection.

Model Predictive Sliding Control for Cascaded H-Bridge Multilevel Converters With Dynamic Current Reference Tracking

This article presents a model predictive sliding control (MPSC) strategy with the dynamic reference tracking for cascaded H-bridge (CHB) multilevel converters. The nonlinear predicted sliding mode control (PSMC) and linear model predictive control (MPC) are combined as a two-level cascaded control structure. The proposed approach aims to get rid of time-consuming tuning to reach the good performance in the presence of uncertainties and disturbances, such as proportional–integral (PI). The MPC scheme is designed to predict the future optimal current and voltage vectors with a designed cost function. The high-level PSMC algorithm is proposed for the dynamic current reference with less chattering problems. The stability of the proposed strategy is analyzed with the Lyapunov direct method. The design principle of the system parameters is introduced with a step-by-step tuning procedure. With the proposed method, the response time can be reduced from 60 to 30 ms during the start-up conditions in the simulation tests. The overshoot of the cell voltages can be eliminated with low-harmonics current and dc-link voltage targets tracking. The validity and effectiveness of the proposed method are implemented by the experimental tests on a laboratory two-cell CHB converter. Compared with the PI-based MPC approach, the current total harmonic distortion (THD) can be reduced and the dc-side performance, including overshoot/undershoot and response speed, can be improved.

Design of Genetic Programming Control Algorithm for Low-Temperature PEM Fuel Cell

An effective control system for the air supply in fuel cell systems (FCS) is required to prevent oxygen starvation and to maximize the net power. For this purpose, conventional feedback and adaptive controllers are designed using genetic programming (GP). To minimize the time required for the GP convergence, FCS models of different complexity are studied and a comparison between them is carried out. Guidelines on applying the GP approach based on data obtained from simulations are developed along with a specially designed cost function that promotes closed-loop linearization. The advantage of this design method lies in its applicability to complex nonlinear systems for which nonlinear control methods may not be applicable. Adaptation is added to the oxygen excess ratio (OER) regulation problem by training a neural network that provides the optimal OER reference based on the stack current and temperature. The performance of both the regulation and adaptive controllers is tested under noise in the compressor flow and the stack current measurements. The robustness of the GP controllers is observed through the frequency response analysis.

Robust predictive visual servoing control for an inertially stabilized platform with uncertain kinematics

In this paper, a disturbance observer (DOB) based predictive control approach is developed for the image-based visual servoing of an inertially stabilized platform (ISP). As the limitation in degrees of freedom of a two-axes ISP, it is hard to estimate the variable feature depth of the target at each control cycle when using an uncalibrated camera, which brings the challenge in the design of the visual servoing controller. To this end, a depth-independent kinematic matrix that only involves nominal parameters is obtained by employing the partitioned scheme in the system modeling. The uncertain kinematics arising from the unknown feature depth, angular velocity tracking errors, and uncalibrated intrinsic parameters is considered as the lumped uncertainty. A discrete-time DOB is then constructed to estimate the lumped uncertainty in real time. Instead of taking an integral action to eliminate tracking errors induced by the uncertain kinematics, the disturbance estimation is actively incorporated into the receding optimization process of the predictive controller. The stability of the closed-loop system is fully analyzed. Experiments on tracking a moving target are performed to validate promising qualities of the proposed approach.

Hybrid Model Predictive Current and Voltage Control for LCL-Filtered Grid-Connected Inverter

The inductive-capacitive-inductive (LCL)-filtered grid-connected inverter features a high-order plant, complex parameter design, and vulnerability under disturbance factors. The transfer-function-based active damping (TFAD) scheme is commonly adopted to mitigate the resonance problem but with cascaded control loops and high design complexity. On the contrary, multiobjective optimization can be easily realized in model predictive control (MPC) via a predefined cost function. As the coupling dynamic among state variables, a hybrid cost function, which contains the grid current, inverter-side current, and capacitor voltage tracking, is designed to guarantee system stability. The optimized control law is generated according to the minimization of the designed cost function, which is further transformed into the driving pulse by the modulator stage. In this process, the weighting factors are exactly arranged to place the desired closed-loop poles. The control law can ensure zero error tracking of grid current reference, which is better than the state feedback way. The hybrid cost function with modified reference is also proposed to reject the harmonics caused by the unhealthy grid condition. The control delay is compensated by using the forecast reference and the predictive state variable estimated from the observer. The proposed scheme performs the intuitive concept, reduced tuning complexity, and fast dynamic response. Simulated results and experiments are presented to verify the proposed control strategy.