Optimal Performance Index Research Articles

Optimal multi-segment trajectory of solar sail with analytical approximation

This paper focuses on the optimization of two-dimensional transfer trajectories for solar sails using analytical approximation and modified analytical approximation methods. Instead of assuming circular orbits [29-31,37], the proposed approach allows for the analysis of solar sails launched from general heliocentric elliptical orbits. By employing linear perturbation theory, analytical propelled trajectories with fixed pitch angles are derived, where the perturbation coefficient serves as the reference lightness coefficient. To obtain the optimal transfer orbit between general heliocentric elliptical orbits, the analytical segments corresponding to different pitch angles are spliced together. A nonlinear optimization algorithm is then utilized to optimize the multi-segment trajectory, aiming to achieve the optimal performance index while satisfying boundary and intermediate constraints. Remarkably, this method requires the optimization of only the pitch angle for each segment, resulting in a small number of optimization variables and significantly improved efficiency in transfer orbit optimization. Additionally, the analytical approximation approach offers substantial time savings compared to numerical integration, particularly when repeated calculations are involved. Furthermore, the modified approximate trajectory exhibits high accuracy, with the optimal analytical transfer trajectory closely approximating the optimal actual trajectory acquired through the shooting method. This paper extensively validates and analyzes the precision of the proposed analytical transfer orbit through numerical verification. Additionally, when contrasted with actual transfer orbit, the suggested analytical approach can significantly decrease the computation time for multi-object challenges by a minimum of 80%. This makes the proposed method highly suitable for rapid design of optimal transfer orbits in multi-target mission scenarios for solar sail-based probes.

Projection-Pursuit Regression-Based Optimization of Frost Resistance and Mechanical Performance in Alkali-Activated Slag Cement Pavements

In recent years, applying slag micro-powder as a substitute for cement in preparing alkali-activated slag cement stabilized sand (AASCSS) mixtures has become increasingly widespread. In the severe cold regions of Xinjiang, multi-objective optimization of the mechanical and frost resistance properties of AASCSS is particularly crucial. This paper adopts slag micro-powder to replace Portland cement, together with lime and desulfurization gypsum as activators, to explore the effects of activator type and dosage on the mechanical and frost-resistance properties of AASCSS. A prediction model for the mechanical and frost-resistance properties of AASCSS based on projection-pursuit regression (PPR) was proposed and established. Using the developed PPR model, contour plots of the comprehensive performance were calculated, simplifying the multi-objective problem into two single-objective problems focusing on mechanical and frost resistance properties for analysis. This method avoids subjective weighting and hypothesis-based modeling. By analyzing the contour plots of comprehensive performance, the optimal performance indices for mechanical and frost–thaw properties and the corresponding types and dosages of activators can be directly determined. When the required 7-day unconfined compressive strength in road engineering is 5.6 MPa, the optimal value of the freeze–thaw performance index (BDR) is 94.08%. At this point, the corresponding lime content is 2.1%, and the desulfurization gypsum content is 3.3%. The research results provide a reference for applying slag to road-based materials.

Granular transfer learning

Transfer learning is aimed at supporting the design of machine learning models in the target domain Dt, given that the knowledge (model) has already been constructed in the source domain Ds. The domains Dt and Ds (as well as the corresponding tasks Ts and Tt) are similar, yet not identical. As a result, the model transferred from Ds to Dt in this new environment exhibits its relevance (credibility) only to some limited extent. In this study, we develop an original approach, where we advocate that the knowledge transfer (model transfer) gives rise to a granular model where the level of information granularity associated with the produced results quantifies the relevance (quality or credibility) of the transferred model. In other words, we stress that the quality of knowledge transferred to Dt becomes captured through a granular generalization of the original numeric model. The overall systematic design process is elaborated on by focusing on the development process of granular neural networks carried out on a basis of the numeric neural networks coming from Ds. The key aspect of the design is to elevate the existing numeric neural network to its granular counterpart by admitting that the connections of the developed model come in the form of information granules, in particular intervals and fuzzy sets. The optimization process is guided by adjusting (optimizing) the level of information granularity being regarded as an essential design asset. The optimized performance index builds upon the descriptors of information granules commonly encountered in Granular Computing. In particular, coverage and specificity measures are treated as sound performance indicators of the quality of knowledge transfer (viz. the performance of the granular neural network expressed in the target domain). Several illustrative examples are provided to visualize the performance of the established design environment.

Optimization Design of Semi-active Suspension for Vehicles Based on LQR Optimal Control

Abstract The suspension system is crucial for providing a comfortable ride and ensuring smooth driving. This article presents a random road surface model and a 1/4 vehicle two-degree-of-freedom semi-active suspension model. The optimal control performance index function was obtained by using the minimum principle, and the optimal controller for semi-active suspension was designed. Simulations were conducted in MATLAB/Simulink, using vehicle acceleration, suspension dynamic deflection, and tire dynamic deformation as evaluation indicators. The study found that the vehicle’s acceleration, suspension dynamic deflection, and tire dynamic deformation were reduced by 21.79%, 21.86%, and 9.88%, respectively, with the use of the designed semi-active suspension optimization controller. This resulted in improved driving smoothness and riding comfort.

Zero‐sum game‐based H∞ optimal finite‐time prescribed performance control for nonlinear multiagent systems with actuator faults

AbstractThis article proposes an optimal leader‐follower consensus control protocol with finite‐time prescribed performance for nonlinear multiagent systems suffering from the actuator fault based on the zero‐sum game framework. First, to guarantee the specified performance of the system within a finite time frame, a novel error transformation function (ETF) is constructed. The ETF proposed in this paper can adjust the mapping range of error transformation to achieve better error convergence performance, thereby enhancing the flexibility of the controller design. Then, to compensate for the negative impact of actuator fault, an error‐related item is separated from the optimal performance index function to design an online fault estimator, which can obtain a better error estimation accuracy. Furthermore, to lessen the need for difficult‐to‐verify persistent excitation signal in adaptive dynamic programming algorithm, both historical and real‐time data are applied to adjust the updating law of neural network weight. Moreover, it demonstrates that the signals within the closed‐loop system are bounded and the expected finite‐time prescribed performance can be ensured in the presence of faults. Finally, a numerical example of multiple single‐link robots manipulator system is executed to validate the efficacy of the developed scheme.

Reentry trajectory optimization of hypersonic glide vehicle based on improved particle swarm algorithm

The reentry process of hypersonic glide vehicle presents a host of intricate constraint issues. A method for optimizing the reentry trajectory of hypersonic glide vehicle is presented to achieve the shortest possible reentry time. The approach utilizes the improved particle swarm optimization (IPSO) algorithm. Firstly, population initialization is performed using the quasi-oppositional differential evolution (QODE) strategy to promote diversity within the population. Then an adaptive adjustment algorithm is designed for the inertia weight coefficient and learning factor to achieve a balance between global and local search capabilities. Finally, utilizing the IPSO algorithm for optimizing the flight parameters of the reentry trajectory, the reentry time is reduced from 338 seconds to 335 seconds. The simulations results indicate that the IPSO algorithm outperforms both the standard PSO algorithm and genetic algorithm (GA) in terms of optimization performance index.

A Hybrid Controller for Musculoskeletal Robots Targeting Lifting Tasks in Industrial Metaverse.

In manufacturing, musculoskeletal robots have gained more attention with the potential advantages of flexibility, robustness, and adaptability over conventional serial-link rigid robots. Focusing on the fundamental lifting tasks, a hybrid controller is proposed to overcome control challenges of such robots for widely applications in industry. The metaverse technology offers an available simulated-reality-based platform to verify the proposed method. The hybrid controller contains two main parts. A muscle-synergy-based radial basis function (RBF) network is proposed as the feedforward controller, which is able to characterize the phasic and the tonic muscle synergies simultaneously. The adaptive dynamic programming (ADP) is applied as the feedback controller to address the optimal control problem. The actor-critic structure is applied in the ADP-based controller, where the critic network is trained to approximate the optimal performance index and the actor network is trained to compute the optimal muscle excitations. Furthermore, the convergence and stability of the ADP algorithm are also analyzed. Finally, experiments have been designed to verify the effectiveness of this hybrid controller on an upper limb musculoskeletal system, and the comparisons with other controllers are also illustrated. The results show that the proposed controller can obtain a satisfactory performance for lifting tasks.

ADP-Based Event-Triggered Constrained Optimal Control on Spatiotemporal Process: Application to Temperature Field in Roller Kiln.

The precise control of the spatiotemporal process in a roller kiln is crucial in the production of Ni-Co-Mn layered cathode material of lithium-ion batteries. Since the product is extremely sensitive to temperature distribution, temperature field control is of great significance. In this article, an event-triggered optimal control (ETOC) method with input constraints for the temperature field is proposed, which takes up an important position in reducing the communication and computation costs. A nonquadratic cost function is adopted to describe the system performance with input constraints. First, we present the problem description of the temperature field event-triggered control, where this field is described by a partial differential equation (PDE). Then, the event-triggered condition is designed according to the information of system states and control inputs. On this basis, a framework of the event-triggered adaptive dynamic programming (ETADP) method that is based on the model reduction technology is proposed for the PDE system. A critic network is used to approach the optimal performance index by a neural network (NN) together with that an actor network is used to optimize the control strategy. Furthermore, an upper bound of the performance index and a lower bound of interexecution times, as well as the stabilities of the impulsive dynamic system and the closed-loop PDE system, are also proved. Simulation verification demonstrates the effectiveness of the proposed method.

Optimization of Cost Performance Index in Construction Project Based on Influencing Factors

Project control has become a critical success factor in the project implementation phase. The execution of construction projects requires a well-planned and designed management system to achieve optimal results. The development of a project always involves several issues that need to be managed, one of which is cost. The planning and implementation stages greatly influence cost escalation in a construction project. Earned value method is a method of project controlling, through performance indicators in execution phase including cost performance index. This research aims to develops a financing model in project implementation based on the project's cost performance index and influencing factors. The data was collected by distributing questionnaires to project stakeholders involved in construction. Modelling was generated with combining earned value criteria with a dynamic systems approach. The result shows that the developed model was run well through verification and validation. The model and manual calculation generates the same value of cost performance index at the control point (at week 32) of 1,29. The value is greater than 1, therefore indicates no cost overruns or potential savings. The final goal of this study is the accuracy of actual costs by controlling the causal factors through model simulations.

Adaptive optimal backstepping control for strict-feedback nonlinear systems with time-varying partial output constraints

This paper investigates the optimal backstepping control strategy for strict-feedback nonlinear systems subject to input saturation and time-varying partial output constraints. Compared with the existing results on output constraints, the time-varying partial output constraint is a more general constraint that can start and end at any time during the system operation, or remain unconstrained, which can be called as output constraints occurring in a limited time interval (OCOLT). A special barrier function is embedded into the optimal performance index function to satisfy the OCOLT condition under the optimal control framework, and a shift function is introduced to address the function discontinuity caused by the OCOLT problem. Then, an auxiliary system and a disturbance observer are respectively constructed to handle the influence of the input saturation and compounded disturbances. Meanwhile, the simplified reinforcement learning algorithm, employing the identifier-critic-actor architecture, is developed to achieve the optimal control objective within the framework of the backstepping technology. Moreover, the proposed optimal control method ensures the semi-globally uniformly ultimately bounded of all signals within the closed-loop system. Finally, two simulation examples are given to further prove the effectiveness of the presented optimal control approach.

Method Validation and Measurement Uncertainty (MU) Evaluation on Enrofloxacin and Ciprofloxacin in the Aquatic Products

This study aimed to investigate a detection method of enrofloxacin and ciprofloxacin to be avail for strictly supervising the quality and safety of aquatic products. The results displayed that the optimal extraction conditions for enrofloxacin and ciprofloxacin were the following five aspects: 15 g dosages of Na2SO4 to dehydrate, 8‰ of acetonitrile and 50% hydrochloric acid to deproteinization, 2 mL dosages of n-hexane to degrease, 10 min of ultrasonic time, and 20 min of extraction (stand) time. Meanwhile, it was also obtained for the optimal detection performance indexes of the recovery, precision, and accuracy from the tests of shrimp, grass carp, and tilapia. In particular, the expanded uncertainties were 2.8601 and 0.8613, and the factors of both the calibration curves ( U rel C ) and the analysis of the experiment ( U rel E ) were the two MU main contributors for enrofloxacin and ciprofloxacin together with the results above 40%. Consequently, the developed novel method was suited for the determination of the enrofloxacin and ciprofloxacin residues in aquatic products and would contribute to reinforce in supervision and inspection of the quality and safety of aquatic products.

Research on habitat quality assessment and decision-making based on Semi-supervised Ensemble Learning method—Daxia River Basin, China

Habitat quality is an indicator of the ecological evolution of a region, and evaluating habitat quality utilizing Machine Learning approaches can reflect the ecological status of a region more objectively. Gridded statistical ecological factors in the study region were utilized to build the Ecological Information State Layer (EISL) in the grid space. Calculate the Environmental Quality Index (EQI) of the sampled grid to evaluate the performance index of the models on the sampled grid. The model with the optimal performance index is selected for the task of habitat quality classification in the research region, and then especially decision-making advice is offered by inverting the degree of influence of ecological elements on habitat quality. The results show that: (1) The Semi-supervised Ensemble Learning model (Tri-training) Accuracy, Kappa coefficient, and F1-score are 0.93, 0.89, and 0.92, respectively, as the optimal model for the habitat quality classification task. (2) Based on the results of the ecological categories calculation, the current ecosystem quality of the southwestern section of the Daxia River Basin is maintained as a positive indicator. the ecosystem quality inside the living space centered on the cities of Linxia and Hezuo shows negative indicator changes. (3) The calculation results of the inverse importance of ecological factors show that vegetation index and human population density are the key factors impacting the habitat quality of the Daxia River Basin. In the ecological management of Daxia River Basin, the degree of spatial aggregation of people's settlements should be reduced while preserving and expanding vegetation cover. Using Tri-training's comprehensive analysis of multiple ecological factors, regional habitat quality can be accurately assessed.

Adaptive Finite-Time Optimal Formation Control for Second-Order Nonlinear Multiagent Systems

This article addresses an adaptive backstepping finite-time optimal formation control problem for second-order multiagent systems (MASs) with unknown nonlinear dynamics. Neural networks (NNs) are used to identify the unknown uncertain terms in the controlled system. Then, the finite-time optimal formation control is designed by constructing a novel optimal performance index function containing exponential power terms based on the framework of identifier–actor–critic. It is proved that all signals in the system are bounded in finite time, and the formation control is simultaneously achieved at minimum cost. The effectiveness and superiority of the proposed control algorithm are verified by simulation comparisons and data analyses.

An Efficient Impulsive Adaptive Dynamic Programming Algorithm for Stochastic Systems.

In this study, a novel general impulsive transition matrix is defined, which can reveal the transition dynamics and probability distribution evolution patterns for all system states between two impulsive "events," instead of two regular time indexes. Based on this general matrix, the policy iteration-based impulsive adaptive dynamic programming (IADP) algorithm along with its variant, which is a more efficient IADP (EIADP) algorithm, are developed in order to solve the optimal impulsive control problems of discrete stochastic systems. Through analyzing the monotonicity, stability, and convergency properties of the obtained iterative value functions and control laws, it is proved that the IADP and EIADP algorithms both converge to the optimal impulsive performance index function. By dividing the whole impulsive policy into smaller pieces, the proposed EIADP algorithm updates the iterative policies in a "piece-by-piece" manner according to the actual hardware constraints. This feature of the EIADP method enables these ADP-based algorithms to be fully optimized to run on all "sizes" of computing devices including the ones with low memory spaces. A simulation experiment is conducted to validate the effectiveness of the present methods.

Identification and classification of recyclable waste using laser-induced breakdown spectroscopy technology

The management and disposal of waste is a severe social issue and an essential part of ecological sustainability. As an important component of the green, low-carbon, and recycling economic system, the identification and classification of recyclable waste is the premise of its reuse and energy conservation. The main issues at hand are to improve the classification accuracy and reliability of recyclable waste and to achieve automatic classification. The methods based on physical characteristics and image-based methods are inaccurate and unreliable. The current spectroscopy methods need to process the detected samples in advance, unsuitable for automatic detection. Based on material composition properties, the Laser-Induced Breakdown Spectroscopy (LIBS) technology is here proposed to accurately and reliably identify and classify recyclable waste into six categories at the level of consumer, such as paper, plastic, glass, metal, textile, and wood. The method is also used to subclassify the same category of waste for reuse at the level of a recycling factory. We subclassified metals into iron, stainless steel, copper, and aluminum and plastics into polyvinylchloride, polyoxymethylene, acrylonitrile-butadiene-styrene, polyamide, polyethylene, and polytetrafluoroethylene. The drop-dimension methods of LIBS spectra of waste were researched to eliminate noise and redundant information by principal component analysis (PCA) and linear discriminant analysis (LDA), respectively. Their clustering effects were analyzed to choose a suitable dimension. Combining the random forest (RF), back propagation neural network (BPNN), and convolutional neural network (CNN), we established and compared five classification models, PCA + RF, PCA + BPNN, LDA + RF, LDA + BPNN, and 1D-CNN. For the classification of six categories, the accuracies of proposed classification models are all more than 96%, and LDA(5D) + RF has 100% accuracy and optimal classification performance indices. For the subclassification of metals and plastics, PCA(8D) + RF has the highest classification accuracy of 98.77% and 99.52%, respectively.

Optimal security control for networked switched systems with a state‐prediction‐based anti‐DoS mechanism

AbstractThe openness of networks renders them vulnerable to various forms of attacks. When networked switched systems (NSS) suffer from denial of service (DoS) attacks and delays, the real‐time property of the dataset decreases, greatly affecting the control performance. To address this issue, this article proposes a switched adaptive dynamic programming (ADP) predictive control method. An event‐triggered mechanism is designed to reduce unnecessary waste of network transmission resources. A predictive mechanism is designed to accurately reconstruct the missing system state and switching signal under DoS network attacks. Then, the reconstructed data are applied to train the actor and critic neural networks, which are used to approximate the optimal control policy and performance index function (PIF) of the NSS, respectively. Furthermore, the iterative convergence of the switched ADP algorithm is proved. Finally, a numerical example is provided to verify the effectiveness of the proposed method.

Distributed Fuzzy Optimal Consensus Control of State-Constrained Nonlinear Strict-Feedback Systems.

This article investigates the distributed fuzzy optimal consensus control problem for state-constrained nonlinear strict-feedback systems under an identifier-actor-critic architecture. First, a fuzzy identifier is designed to approximate each agent's unknown nonlinear dynamics. Then, by defining multiple barrier-type local optimal performance indexes for each agent, the optimal virtual and actual control laws are obtained, where two fuzzy-logic systems working as the actor network and critic network are used to execute control behavior and evaluate control performance, respectively. It is proved that the proposed control protocol can drive all agents to reach consensus without violating state constraints, and make the local performance indexes reach the Nash equilibrium simultaneously. Simulation studies are given to verify the effectiveness of the developed fuzzy optimal consensus control approach.

PMSM Speed Control Using an Enhanced State Feedback Controller and Linear Quadratic Regulator for Effective Drive System

This paper introduced an effective method for regulating and controlling the speed of PMSMs (permanent magnet synchronous motors) by linearizing the non-linear and time-varying equations that model the PMSM dynamics. This method was achieved by utilizing an improved state feedback controller along with a linear quadratic regulator (LQR) to determine an optimal performance index. In order to assess external load torque disturbances in the system, a disturbance observer (DO) method was employed. Additionally, a sliding mode observer was utilized in conjunction with the disturbance observer to control motor speed. Simulations were conducted to compare the state feedback controller with a conventional PI- controller. The results showed that the state feedback controller achieved a robust as well as improved speed and torque dynamic performance with a reduced steady-state error percentage of 24.17% and 23.51%, as compared to 38.0% and 38.37% obtained using a PI-controller. The derived state feedback matrix (K), based on Ackerman’s rule, demonstrated that the entire system is controllable and that the performance index is marginally stable. All simulations were achieved in MATLAB/SIMULINK 2021 version.

The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters

The study of learning behaviors with multi features is of great significance for interactive cooperation. The data prediction and decision are to realize the comprehensive analysis and value mining. In this study, hierarchical learning behavior based on feature cluster is proposed. Based on the massive data in interactive learning environment, the descriptive model and learning algorithm suitable for feature clustering are designed, and sufficient experiments obtain the optimal performance indexes. The data analysis results are reliable. On this basis, the hierarchical learning behaviors based on feature clusters are visualized, the rules of different learning behaviors are summarized, then we propose the practical scheme of interactive cooperation. The hierarchical learning behaviors can be realized by feature clusters, which can effectively improve the modes of interactive cooperation, and help to improve the learning effectiveness.

Optimal control and stabilization for discrete‐time networked control systems with multichannel Markovian packet losses

AbstractThis article is concerned with the optimal control and stabilization for the discrete‐time networked control systems with Markovian fading channels in a diagonal form, which indicates that different channel possesses different correlated fading property. To overcome the essential difficulty resulting from the multi communication channel packet losses and the correlation of the Markov chain, a new multistate Markov chain is introduced, by which the optimal control and stabilization for the NCSs with the diagonal form of Markovian fading channels are transformed into the ones for the general Markovian jump linear system. According to the converted system, a new modified maximum principle is presented, that is, a forward‐backward stochastic difference equation (FBSDE) is obtained. In the finite horizon design, the key step is to seek for the relationship between the system state and optimal costate. By the obtained relation and based on a new coupled difference Riccati equation (CDRE), a necessary and sufficient solvability condition for the optimal control is obtained as well as an explicit expression for the optimal controller. For the infinite horizon case, a new type of coupled Lyapunov function is defined, which keeps consistent with the finite horizon optimal performance index. By introducing a new coupled algebraic Riccati equation and using the defined Lyapunov function, a necessary and sufficient stabilization condition in the mean‐square sense and an infinite horizon optimal controller are presented. Finally, a numerical example is supplied to illustrate the efficiency of the proposed results.