Dynamic Cost Function Research Articles

A new model predictive current control strategy for PMSM based on extended control set and dynamic cost function

A new model predictive current control strategy for PMSM based on extended control set and dynamic cost function

A comprehensive design pipeline for FCEV powertrain configuration considering the evolutionary development process and extreme economic performance

Due to stringent emission regulations, the zero-emission hydrogen fuel cell electric vehicles (FCEVs) are gaining significant attention, but their slow power response requires careful selection of auxiliary energy sources and power systems to optimize energy efficiency. This paper presents a comprehensive design pipeline for the FCEV powertrain, encompassing methods for powertrain configuration design, feasible domain design for component parameters, and parameter matching. Initially, a powertrain configuration design method based on evolutionary game theory is introduced, forecasting trends in energy-efficient powertrain configurations using a dynamic cost function that adjusts based on the proportions of different configurations. Next, a feasible domain design method is developed to establish fixed constraints on component parameters, ensuring a fair balance between performance needs and overall costs, including development and lifecycle expenses associated with the powertrain configuration. Subsequently, dynamic programming (DP) is used for parameter matching, assessing the utmost energy-saving potential. The cost function in this method incorporates the degradation cost of powertrain components, underscoring the economic performance of the target FCEV over its lifespan. Finally, simulation assessments indicate that the proposed design pipeline effectively identifies the optimal powertrain configuration for FCEVs, endowing each possible configuration with exceptional economic efficiency across different component parameters.

Enhancing Cryptographic Primitives through Dynamic Cost Function Optimization in Heuristic Search

The efficiency of heuristic search algorithms is a critical factor in the realm of cryptographic primitive construction, particularly in the generation of highly nonlinear bijective permutations, known as substitution boxes (S-boxes). The vast search space of 256! (256 factorial) permutations for 8-bit sequences poses a significant challenge in isolating S-boxes with optimal nonlinearity, a crucial property for enhancing the resilience of symmetric ciphers against cryptanalytic attacks. Existing approaches to this problem suffer from high computational costs and limited success rates, necessitating the development of more efficient and effective methods. This study introduces a novel approach that addresses these limitations by dynamically adjusting the cost function parameters within the hill-climbing heuristic search algorithm. By incorporating principles from dynamic programming, our methodology leverages feedback from previous iterations to adaptively refine the search trajectory, leading to a significant reduction in the number of iterations required to converge on optimal solutions. Through extensive comparative analyses with state-of-the-art techniques, we demonstrate that our approach achieves a remarkable 100% success rate in locating 8-bit bijective S-boxes with maximal nonlinearity, while requiring only 50,000 iterations on average—a substantial improvement over existing methods. The proposed dynamic parameter adaptation mechanism not only enhances the computational efficiency of the search process, but also showcases the potential for interdisciplinary collaboration between the fields of heuristic optimization and cryptography. The practical implications of our findings are significant, as the ability to efficiently generate highly nonlinear S-boxes directly contributes to the development of more secure and robust symmetric encryption systems. Furthermore, the dynamic parameter adaptation concept introduced in this study opens up new avenues for future research in the broader context of heuristic optimization and its applications across various domains.

A two-level surrogate framework for demand-objective time-variant reliability-based design optimization

Complex engineering problems in the real world often involve uncertainties and require time-consuming simulations and experiments, hindering the efficiency of constraints processing. Additionally, practical engineering problems may have varying demands that pose new challenges for dealing with dynamic environments. However, most existing methods focus on immediate demands, making it inevitable to undergo tedious procedures to find feasible solutions. To address these issues, this paper proposes a demand-objective time-variant reliability-based design optimization framework to meet different demands in varying environments. Meanwhile, a corresponding two-level surrogate-based solving strategy is developed to reduce the computational resources required. The framework consists of two stages: time-variant reliability-based constraint handling and demand-objective optimization. An adaptive two-level surrogate method is proposed for time-variant reliability-based constraint handling by combining Kriging to reduce computational costs associated with evaluating constraints. This paper introduces moderate, conservative, and radical models for demand-objective optimization, combining the two-level surrogate method to deal with dynamic cost functions with different demands. Also, a new constrained minimax optimization method is developed for the radical model, which is the trickiest but very useful in practical engineering problems so that the algorithm can converge quickly. Finally, some examples are demonstrated to specify the proposed framework in applications.

Research on the evaluation method of power grid equipment input–output efficiency based on LCC

The quantitative evaluation of the comprehensive efficiency of the equipment has important significance for making accurate maintenance and other decisions. From the perspective of equipment life cycle cost, this paper constructed the fixed cost function and dynamic cost function of power equipment and obtained the economic life model. The total cost of the whole life cycle of the optimal economic life was obtained by the derivation method. Then, a comprehensive performance evaluation index system including reliability, availability, maintainability and economy had been established. The comprehensive efficiency evaluation model based on economic life and the equipment maintenance optimization model based on comprehensive efficiency evaluation were designed. At last, taking 35 kV circuit breaker as an example, the economic life model and comprehensive efficiency evaluation model were analyzed and calculated. The results proved the correctness of the comprehensive efficiency evaluation model designed in this paper, which could provide technical guidance and support for the selection and maintenance of power equipment.

A comprehensive multi-topology minimum set cover link-state routing approach for emerging random all-IP access network topologies

Wireless access networks are ever-changing their setup, topologies and provisions of coverage. Forthcoming deployment trends include densification of cells and convergence of the ‘native IP’ with cellular access networks with more than best-effort expectations. Greater meshing in networks due to practical deployment requirements is inevitable and needed. This paper proposes a routing protocol to address the “randomness” of topology interconnections, routing paths and sizes in emerging IP access networks. Termed as Minimum Set Cover (MSC) approach, it is a generalization of the NP and NP complete mathematical problem. The offline component of the MSC multiplies intra-domain routing installations, called Routing Planes (RPs) modelled as graphs, to suitably cover the whole routing topology prior to the traffic injections in the online component. We introduce novel offline optimization features using a dynamic cost function that plans availability of capacities and correlation of routing paths via the chosen set of RPs. Our simulations verify effective path diversity using MSC with its modest protocol overhead and a heuristic used for RPs selection. For randomly constructed access network topologies with 7, 18 and 33 routers and various meshing levels, our results show convincing suitability and key performances gains compared with rival routing solutions.

Do Fossil-Fuel Price Distortions Impact the Low-Carbon Transition in China’s Energy Intensive Industries?

Energy intensive industries (EIIs) in China are predominantly reliant on fossil fuels. Consequently, such high fossil fuel dependency has amplified carbon emission levels and blocked the low-carbon transition. It is inappropriate to discuss the solution of the dependency before investigating fossil-fuel price distortion and its impact on the industrial energy consumption. Therefore, this paper built a dynamic trans-log cost function model based on provincial panel data of China’s Ells between 2004 and 2016, to investigate inter-fuel substitution effects caused by own price elasticities and cross price elasticities, and analyzed the impact of fossil-fuel price distortions on low-carbon transition. The level of price distortions in coal, gasoline and diesel was evaluated, based on which the CO2 mitigation potentials in China’s EIIs were estimated. Results show that: 1) in each EII sector, the own price elasticities of all fuels were negative while the cross price elasticities among coal, oil and electricity were positive, suggesting substitution effect exists; 2) the average level of price distortions in coal, gasoline and diesel is 7.48, 11.1 and 32.19%, respectively, which means the prices of coal tend to be more market- oriented than the other two fuels; 3) removing coal price distortions can potentially reduce CO2 emissions in China’s EIIs by 905.78 million tons, while the effects of removing oil price distortions were uncertain, unless the substitution of coal for oil was restrained. Therefore, there is still much room for improvement in China’s fossil-fuel market reform. Possible policies are required to improve the production in EIIs and the low-carbon transition by adopting cleaner energy resources to substitute fossil-fuels.

Extremum Seeking With Enhanced Convergence Speed for Optimization of Time-Varying Steady-State Behavior of Industrial Motion Stages

Recently, an extremum-seeking control (ESC) approach has been developed for optimization of generically time-varying steady-state responses of nonlinear systems. A generic filter structure was introduced, the so-called dynamic cost function, which has been instrumental in facilitating the use of ESC in the more generic, time-varying context. However, the dynamic cost function must operate sufficiently slow compared to the time-varying nature of the system responses, thereby compromising the convergence speed of the ESC scheme. In this work, a modified ESC approach is proposed that incorporates explicit knowledge about the user-defined dynamic cost function, able to enhance the convergence speed of the ESC scheme. Moreover, we provide a stability analysis for this extended approach. The main contribution of this work is the experimental demonstration of both ESC approaches for the performance optimal tuning of a variable-gain control (VGC) strategy employed on a high-accuracy industrial motion stage setup, exhibiting generically time-varying steady-state responses. VGC is able to enhance the system performance by balancing the typical linear control tradeoff between low-frequency disturbance suppression properties and sensitivity to high-frequency disturbances in a more desirable manner. We experimentally show that, for the unknown disturbance situation at hand, the variable-gain controller can be automatically tuned using both ESC approaches to achieve the optimal system performance. In addition, enhanced convergence speed with the modified ESC approach is evidenced experimentally.

Model-Free Optimized Tracking Control Heuristic

Many tracking control solutions proposed in the literature rely on various forms of tracking error signals at the expense of possibly overlooking other dynamic criteria, such as optimizing the control effort, overshoot, and settling time, for example. In this article, a model-free control architectural framework is presented to track reference signals while optimizing other criteria as per the designer’s preference. The control architecture is model-free in the sense that the plant’s dynamics do not have to be known in advance. To this end, we propose and compare four tracking control algorithms which synergistically integrate a few machine learning tools to compromise between tracking a reference signal and optimizing a user-defined dynamic cost function. This is accomplished via two orchestrated control loops, one for tracking and one for optimization. Two control algorithms are designed and compared for the tracking loop. The first is based on reinforcement learning while the second is based on nonlinear threshold accepting technique. The optimization control loop is implemented using an artificial neural network. Each controller is trained offline before being integrated in the aggregate control system. Simulation results of three scenarios with various complexities demonstrated the effectiveness of the proposed control schemes in forcing the tracking error to converge while minimizing a pre-defined system-wide objective function.

Extremum-seeking control for optimization of time-varying steady-state responses of nonlinear systems

Extremum-seeking control (ESC) is a useful tool for the steady-state performance optimization of plants for which limited knowledge about its dynamical behavior and disturbance situation is known. The case when the steady-state plant responses correspond to equilibrium solutions received a lot of attention. However, in many industrial applications plant performance is characterized by time-varying steady-state behavior. In those cases, no static parameter-to-steady-state performance map can be defined. In this work, we propose an ESC method that employs a so-called dynamic cost function to cope with time-varying steady-state responses of general nonlinear systems. We prove semi-global practical asymptotic stability of the closed-loop ESC scheme in the presence of bounded and time-varying external disturbances. Moreover, the working principle is illustrated by means of the real-time performance optimal tuning of a nonlinear control strategy for a motion control application.

Online Distributed Optimization With Strongly Pseudoconvex-Sum Cost Functions

In this paper, the problem of online distributed optimization is investigated, where the sum of locally dynamic cost functions is considered to be strongly pseudoconvex. To address this problem, we propose an online distributed algorithm based on an auxiliary optimization strategy. The algorithm involves each agent minimizing its own cost function subject to a common convex set while exchanging local information with others under a time-varying directed communication graph sequence. The dynamic regret is employed to measure performance of the algorithm. Under mild conditions on the graph, it is shown that if the increasing rate of minimizer sequence's deviation is within a certain range, then the bound of each dynamic regret function grows sublinearly. Simulations are presented to demonstrate the effectiveness of our theoretical results.

Assessing worker performance using dynamic cost functions in human robot collaborative tasks

The aim of this research is to develop a framework to allow efficient human robot collaboration on manufacturing assembly tasks based on cost functions that quantify capabilities and performance of each element in a system and enable their efficient evaluation. A proposed cost function format is developed along with initial development of two example cost function variables, completion time and fatigue, obtained as each worker is completing assembly tasks. The cost function format and example variables were tested with two example tasks utilizing an ABB YuMi Robot in addition to a simulated human worker under various levels of fatigue. The total costs produced clearly identified the best worker to complete each task with these costs also clearly indicating when a human worker is fatigued to a greater or lesser degree than expected.

Adaptive Zone Model Predictive Control of Artificial Pancreas Based on Glucose- and Velocity-Dependent Control Penalties

Zone model predictive control (MPC) has been proven to be an efficient approach to closed-loop insulin delivery in clinical studies. In this paper, we aim to safely reduce mean glucose levels by proposing control penalty adaptation in the cost function of zone MPC. A zone MPC method with a dynamic cost function that updates its control penalty parameters in real time according to the predicted glucose and its rate of change is developed. The proposed method is evaluated on the entire 100-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the zone MPC tested in an extended outpatient study. For unannounced meals, the proposed method leads to statistically significant improvements in terms of mean glucose (153.8 mg/dL vs. 159.0mg/dL; ) and percentage time in [70, 180] mg/dL ([Formula: see text] vs. [Formula: see text]; ) without increasing the risk of hypoglycemia. Performance for announced meals is similar to that obtained without adaptation. The proposed method also behaves properly and safely for scenarios of moderate meal-bolus and basal rate mismatches, as well as simulated unannounced exercise. Advisory-mode analysis based on clinical data indicates that the method can reduce glucose levels through suggesting additional safe amounts of insulin on top of those suggested by the zone MPC used in the study. The proposed method leads to improved glucose control without increasing hypoglycemia risks. The results validate the feasibility of improving glucose regulation through glucose- and velocity-dependent control penalty adaptation in MPC design.

Analysis of the Effect of Parameter Variation on a Dynamic Cost Function for Distributed Energy Resources: A DER-CAM Case Study

This paper investigates the effect of selected strategies of distributed energy resources (DER) on an energy cost function that optimizes the distribution of distributed energy resources for a mid-rise apartment building. This is achieved through comparing parameter optimization results for both a high-level and low-level optimizer, respectively. The optimization process is carried out using the following approach: (1) a two-objective function is constructed with one objective function similar to that of the high-level optimizer (DER-CAM); (2) an evolutionary algorithm (EA) with modified selection capability is used to optimize the two-objective function problem in (1) for four selected cases of DER utilization that were previously optimized in DER-CAM; and (3) the optimization results of the low-level optimizer are compared with the outcome of DER-CAM optimization for the four selected cases. This is done to establish the capability of DER-CAM as an effective tool for optimal distributed energy resource allocation. Results obtained reveal the effect of load shifting and solar photovoltaic (PV) panels with power exporting capability on the optimization of the cost function. The Pareto-based MOEA approach has also proved to be effective in observing the interactions between objective function parameters. Mean inverted generational distance (MIGD) values obtained over 10 runs for each of the four cases that were considered show that a DER combination of PV panel, battery storage, heat pump, and load shifting outperforms the other strategies in 70% of the total simulation runs.

A Neural Network-Based Dynamic Cost Function for the Implementation of a Predictive Current Controller

Predictive current controllers based on finite control set-model predictive control (FCS-MPC) have been extensively used in power converters. One of the clear advantages of FCS-MPC is that several control targets and constraints can be included in a cost function and simultaneously controlled. In order to establish the importance of one controlled target in relation to the others, weighting factors are used. Once the weighting factors have been tuned, they remain unchanged. This paper presents a neural network-based novel approach to the problem, in which weighting factors are tuned online as a function of several merit figures and references. This adaptive method updates online the weighting factors in the cost function when either the merit figures or the references change to boost the performance of the controller. This strategy is validated through simulations and experiments are carried out on a three-level neutral point clamped converter. The results are compared with the conventional FCS-MPC, which is based on static cost functions.

A model-based decision support tool for building portfolios under uncertainty

Abstract Despite abundant energy use data, few facilities managers have a good benchmark for tracking energy performance in commercial buildings. Building energy self-benchmarking is an effective means of comparing performance to expectations. This paper presents an improved theory for a decision support tool that can self-benchmark building energy performance, identify energy faults, and quantify their severity. Detailed building energy simulation modeling of a big-box retail store with open source software is accessible and economical to industry for generating performance benchmarks. Methods of parametric sampling and uncertainty analysis are enhanced with detailed parameter uncertainty characterization. Uncertainty and sensitivity analysis are used to adjust risk tolerance thresholds for each unique monitored end-use. A dynamic cost function allows utility theory to compute expected costs covering multiple criteria. Improved theory for decision support tool is tested on ten faulted model scenarios placed in three climate zones. Finally, we demonstrate fault response prioritization.

Instability and dynamic cost elasticities in freight transport systems

The paper studies two properties of freight transport systems with dynamic cost functions. The first property is that the dynamic cost functions can give rise to situations in which the transport system is unstable. This property has a number of consequences. One of them – important from a practical point of view – is that, if a transport mode implements a plan of improvements of its characteristics in order to stop the decline of the proportion of freight flow it carries, there is a time threshold to start the improvements, beyond which the plan is unsuccessful. The other property is that the dynamic cost functions give rise to cost elasticities that vary over time, with asymptotic values that tend to zero as transport cost decreases. This means that, if one implements successive improvements of the characteristics of a transport mode, their effects diminish progressively.

On the nature of motor planning variables during arm pointing movement: Compositeness and speed dependence

The purpose of this study was to investigate the nature of the variables and rules underlying the planning of unrestrained 3D arm reaching. To identify whether the brain uses kinematic, dynamic and energetic values in an isolated manner or combines them in a flexible way, we examined the effects of speed variations upon the chosen arm trajectories during free arm movements. Within the optimal control framework, we uncovered which (possibly composite) optimality criterion underlays at best the empirical data. Fifteen participants were asked to perform free-endpoint reaching movements from a specific arm configuration at slow, normal and fast speeds. Experimental results revealed that prominent features of observed motor behaviors were significantly speed-dependent, such as the chosen reach endpoint and the final arm posture. Nevertheless, participants exhibited different arm trajectories and various degrees of speed dependence of their reaching behavior. These inter-individual differences were addressed using a numerical inverse optimal control methodology. Simulation results revealed that a weighted combination of kinematic, energetic and dynamic cost functions was required to account for all the critical features of the participants’ behavior. Furthermore, no evidence for the existence of a speed-dependent tuning of these weights was found, thereby suggesting subject-specific but speed-invariant weightings of kinematic, energetic and dynamic variables during the motor planning process of free arm movements. This suggested that the inter-individual difference of arm trajectories and speed dependence was not only due to anthropometric singularities but also to critical differences in the composition of the subjective cost function.

Dynamic cost functions and freight transport modal split evolution

The paper studies the characteristics of the evolution over time of modal split for a transport system in which various modes compete with each other and face the freight flow increase in different ways. It is shown that the modal split evolution is constituted by a sequence of time periods, in each of which the dynamic characteristics of the system are different. An equilibrium, stable or unstable, is reached after a transition phase, whose length depends on the characteristics of the system, and in some cases exceeds the length of the period, so that an equilibrium is not reached.

Dynamic Multiple Description Wavelet Based Image Coding Using Enhanced Particle Swarm Optimization

In this paper, dynamic multiple description wavelet based image coding using enhanced particle swarm optimization (EPSO) is proposed for effective multimedia transmission in the dynamically changing network topologies such as mobile ad-hoc networks (MANETs). In wireless networks, multimedia transmission has become a challenging problem because of changing conditions over time such as packet loss rate (PLR), capacity/bandwidth, network and link conditions. The proposed method is used to simultaneously create optimal multiple description coding (MDC) or sub-images based on the dynamically changing capacity/bandwidth of wireless joint/disjoint communication channels/paths. To perform it, PSO is enhanced by adding a position mutation scheme as a generation feature to employ the proposed dynamic cost function to find the optimal solution within in a search space. To create MDs efficiently, three different wavelet transformation methods which are Discrete Wavelet Transform (DWT), Dual Tree-Complex Wavelet Transform (DT-CWT), and Stationary Wavelet Transform (SWT) are investigated. In this manner, a wavelet transform method is first applied to the given input image to create the sub-bands or set of coefficients as a search space, and then EPSO is employed to find optimal results in the search space to generate MDs. The proposed approach produces good quality of descriptions for effective multimedia transmission over dynamically changing and unreliable communication networks, particularly in wireless and ad-hoc networks (MANETs). The results of the proposed method are compared with the state-of-the-art techniques and simulation results demonstrate that using EPSO with DT-CWT provides a significant improvement of optimal and balanced MDC generation with good quality at different bit-rates.