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A correction estimation method considering sensor measurement bias was proposed to accurately estimate the lateral velocity of the tractor in autonomous semi-trailer vehicles. Based on the three-degree-of-freedom vehicle dynamics model, the measurement bias estimation model was established by expanding the front wheel angle, yaw rate bias, and lateral acceleration bias into the state variables. Taking into account the impact of dynamic model mismatch, a feedback correction mechanism based on the kinematic model was designed to adjust the estimation results of lateral velocity and yaw rate through feedback. The proposed method was implemented using the square-root cubature Kalman filter algorithm, and the observability of the estimator was analyzed. This method was verified on the joint simulation platform of TruckSim and MATLAB/Simulink. The results show that the proposed method not only eliminates the influence of measurement bias but also exhibits good convergence speed. Its performance is significantly better than estimation methods that do not consider measurement bias or only consider measurement bias. [ABSTRACT FROM AUTHOR]

Wind power exhibits low controllability and is situated in dispersed geographical locations, presenting complex coupling and aggregation characteristics in both temporal and spatial dimensions. When large-scale wind power is integrated into the power grid, it will bring a significant technical challenge: the highly variable nature of wind power poses a threat to the safe and stable control of the power, frequency, and voltage in the power system. Simultaneously, the model predictive control (MPC) technology provides more opportunities for investigating control issues related to large-scale wind power integration in power systems. This paper provides a timely and systematic overview of the applications of MPC in the field of wind power for the first time, innovatively embedding MPC technology into multi-level (e.g., wind turbines, wind farms, wind power cluster, and power grids) and multi-objective (e.g., power, frequency, and voltage) control. Firstly, the basic concept and classification criteria of MPC are developed, and the available modeling methods in wind power are carefully compared. Secondly, the application scenarios of MPC in multi-level and multi-objective wind power control are summarized. Finally, how to use a variety of optimization algorithms to solve these models is discussed. Based on the broad review above, we summarize several key scientific issues related to predictive control and discuss the challenges and future development directions in detail. This paper details the role of MPC technology in multi-level and multi-objective control within the wind power sector, aiming to help engineers and scientists understand its substantial potential in wind power integration in power systems.

In order to mitigate the excessive amplitudes of pitch and heave motions, a real‐time predictive control method is proposed for the high‐speed multihull with strict input constraints of anti‐pitching appendages. The vertical control model of the high‐speed multihull has been established with T‐foil and flap served as anti‐pitching appendages, and the motion couplings between heave and pitch have been analyzed. The wave disturbance considered as a kind of colored noise can be whitened so as to obtain the control signal, and then adaptive extended Kalman filtering method can be used to get the online estimates of the heave velocity and pitch angular velocity. The proposed predictive control‐based anti‐pitching method mainly contains two reformed aspects of the predictive control. One is to take into account the error between the actual and predicted system values, such that the prediction model with linear error correction can be obtained resulting in the enhanced robustness. The other is to employ the Laguerre function to convert the control sequence of moving horizon optimization into the fewer‐dimensional Laguerre parameters, and then the computational burden of online optimization can be significantly reduced indicating the improved real‐time performance. The closed‐loop system stability has also been theoretically analyzed. The simulation and experiment have been carried out to verify the effectiveness of the proposed algorithm in this paper. [ABSTRACT FROM AUTHOR]

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional information search technology performs full-text indexing on the data in the Internet, searches for information by means of keyword matching index, and returns information to the user. This retrieval method does not accurately understand the user's needs, and returns relevant links rather than the information the user needs. The user needs to find relevant information from the linked documents. In order to improve the shortcomings of the above traditional search technology, this paper is based on the knowledge map of multi-data source intelligent information distributed search technology, through data acquisition in the Internet, complete the transformation of data to knowledge to form a knowledge network and provide information retrieval. This paper studies the construction of knowledge maps for application domain representation, proposes a semantic similarity model with constraints and implicit feedback correction mechanism, and explores the realization of intelligent information search under certain conditions. Through the analysis of prototype experimental data in the application field, the accuracy of information search based on knowledge map can reach 90%, which has strong practicability. [ABSTRACT FROM AUTHOR]

Most actual industrial processes are multivariable and constrained complex systems. The state and output of the system also have uncertainty due to the existence of random disturbances. The output of the system is easier to be measured than the state and can more intuitively reflect the running state of the system. Considering the limitations of industrial equipment and the benefits of production, it is generally allowed to have a small portion of the output that can exceed the constraints. As a result, the outputs can be represented as corresponding single probabilistic constraints. In this paper, therefore, an output probabilistic constrained optimal control algorithm based on multivariable model algorithm control (MMAC) is proposed. First, the feedback correction link of the MMAC algorithm is improved, and the predicted outputs are corrected by taking the weighted average of the errors. Then, assuming that the disturbances obey Gaussian distribution, the output probabilistic constraints are transformed into deterministic ones. Next, the optimal control problem is solved as a quadratic programming (QP) problem after combining them with the performance index function of the MMAC. Finally, the proposed algorithm is applied to the looper control system in hot strip rolling process and compared with the single MMAC algorithm to verify its effectiveness.

As an open-loop model predictive control algorithm, finite control set model predictive control (FCS-MPC) scheme in power converter system is based on assumption that responses of optimal control implemented on prediction model agree well with actual system. The influence of model parameter mismatches and environment disturbance on control performance of scheme is neglected. Then, based on feedback correction strategy in traditional model predictive control algorithm, we derive a finite control set model predictive control with feedback correction scheme (FCS-MPCFC) that allows us to adjust prediction model output at current instant by model prediction error at previous instant, and the closed-loop correction of prediction model output is achieved. Simulations comparison analyses on a two-level three-phase inverter with multi-type model parameter mismatches controlled by traditional and improved FCS-MPC scheme are presented. Experiments are carried out on DSP controller platform.

The modular multilevel converter (MMC) has the advantages of high efficiency, low harmonic, modular design, and easy cascade, which has been widely used in the field of high voltage and large capacity energy conversion. In order to improve the dynamic response speed of the MMC-HVDC based on the modular multi-level converter, a novel method which combines a model predictive control (MPC) of MMC-HVDC system with improved sub-module voltage balanced control strategy is proposed. The method, which utilises the prediction model, feedback correction and rolling optimisation to obtain the optimal voltage control, overcomes the difficulties in the traditional way of setting PI parameters of the internal loop current controller and the outer loop controller and tackles the problem of low dynamic response. Finally, a 21-level MMC-HVDC system simulation model is built on PSCAD-EMTDC software platform. The simulation results indicate the effectiveness and feasibility of the control strategy.

The flow of IDSSM strategy. • Integrated demand and supply side management strategy. • Demand side rolling optimization for schedulable appliances over next 24 h. • Supply side rolling optimization for energy supply equipment units. • Feedback correction on discrepancy between actual and predicted demands. • Electric cars are important for effective demand side management. The multi-energy system that contains the highly coupled energy supply equipment units can be adopted to simultaneously satisfy the cooling, heating and electrical energy demands. Owing to the complex nature of multiple supplies and demands, an integrated demand and supply side management strategy was proposed for aligning the system operation and building energy demands. The proposed strategy included three core algorithms: demand side rolling optimization, supply side rolling optimization and feedback correction. Demand and supply side rolling optimizations were simultaneously implemented to determine the schedulable appliances and operating capacities of energy supply equipment units over the next 24-h planning horizon. Feedback correction was involved for continual modification on any discrepancy between various actual and predicted energy demands. In this study, a trigeneration system primed with solid oxide fuel cell-gas turbine was designed as the multi-energy system to serve a high-rise apartment building with electric cars. It was found that the primary energy consumption, the design capacity of the prime mover and the capacity of electricity storage would be decreased by 8.75%, 10% and 43% respectively through the integrated demand and supply side management strategy. It was also noted that electric cars played important roles in effective demand side management. [ABSTRACT FROM AUTHOR]

The modular multilevel converter (MMC) has the advantages of high efficiency, low harmonic, modular design, and easy cascade, which has been widely used in the field of high voltage and large capacity energy conversion. In order to improve the dynamic response speed of the MMC-HVDC based on the modular multi-level converter, a novel method which combines a model predictive control (MPC) of MMC-HVDC system with improved sub-module voltage balanced control strategy is proposed. The method, which utilises the prediction model, feedback correction and rolling optimisation to obtain the optimal voltage control, overcomes the difficulties in the traditional way of setting PI parameters of the internal loop current controller and the outer loop controller and tackles the problem of low dynamic response. Finally, a 21-level MMC-HVDC system simulation model is built on PSCAD-EMTDC software platform. The simulation results indicate the effectiveness and feasibility of the control strategy. [ABSTRACT FROM AUTHOR]

Combined cooling, heating and power (CCHP) system with the prime mover set of solid oxide fuel cell-gas turbine (SOFC-GT) would feature with high electrical efficiency, but contain the highly coupled equipment units for cooling, heating and electricity supplies. Due to such complex nature of multiple supplies and demands, the previously developed control strategies were not suitable, and it is a challenge to develop an appropriate control strategy for the SOFC-GT CCHP system. Therefore, a new approach, called multi-supply-multi-demand (MSMD) control strategy, is proposed in this paper. The MSMD control includes two core algorithms: rolling optimization (RO) and feedback correction (FC). RO is used to determine the operation of energy supply equipment units based on the forecasting weather and loading information of the next 24 h. FC is applied for continual mitigation in case any difference between the actual and predicted energy demands. In the SOFC-GT CCHP system with energy storages for building application, the effectiveness of the MSMD control strategy was tested. It was found that RO could determine the operating schedules of the related equipment units at lower primary energy consumption than the conventional mean, while FC could effectively rectify the prediction errors incurred from the real-time loading conditions. [ABSTRACT FROM AUTHOR]

Combined cooling, heating, and power (CCHP) systems have been widely applied in various kinds of buildings. Most operation strategies for CCHP microgrids are designed based on day-ahead profiles. However, prediction error for renewable energy resources (RES) and load leads to suboptimal operation in dispatch scheduling. In this paper, we propose an online optimal operation approach for CCHP microgrids based on model predictive control with feedback correction to compensate for prediction error. This approach includes two hierarchies: 1) rolling optimization; and 2) feedback correction. In the rolling part, a hybrid algorithm based on integrating time series analysis and Kalman filters is used to forecast the power for RES and load. A rolling optimization model is established to schedule operation according to the latest forecast information. The rolling dispatch scheduling is then adjusted based on ultra-short-term error prediction. The feedback correction model is applied to minimize the adjustments and to compensate for prediction error. A case study demonstrates the effectiveness of the proposed approach with better matching between demand and supply. [ABSTRACT FROM PUBLISHER]

For online digital image stabilization system, the camera usually moves with diverse and variable modes, which make the motion filtering process difficult to reserve the intentional fluctuations and remove unwanted jitters simultaneously. This paper presents an adaptive motion filtering algorithm with feedback correction. Firstly, based on the low frequency character of intentional motion in adjacent frames, the intentional velocity is regarded as the control variable, thus the modified one dimension Kalman filtering algorithm is proved to converge to a balance state of consistency and stabilization. Secondly, according to the mutual restraint of consistency and stabilization, this paper proposes two corresponding online feedback factors to reflect the immediate filtering performances. Hence, a motion filtering algorithm with improved Kalman filtering and parameter self-adjustment is realized, which can describe the real camera motion flexibly, as well as adapt to its changes. At last, an objective evaluation method for motion filtering is presented from the aspects of integral consistency, integral stabilization and integral robustness. Compared with other classical motion filtering algorithms, experimental results indicate that the proposed algorithm is more fast-computing and adaptive for different moving modes of the camera, which can reserve the intentional motions and remove the jitters steadily. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Kirsehir Education Faculty is the property of Journal of Kirsehir Education Faculty and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Timing variation in small group musical performance results from intentional, expressive and unintentional, error components in individual player timing. These timing fluctuations produce variability in between-player note asynchrony and require timing adjustments to keep the ensemble together. The size of the adjustments relative to the asynchrony (correction gain) affects the amount and nature of asynchrony variability. We present new listening tests to estimate thresholds for perception of between-player asynchrony variability and to determine whether listeners use differences in the nature of the variability, as well as in its magnitude, to judge asynchrony.In two experiments, computer-simulated ensemble performances of a 48-note excerpt from Haydn Op. 74 No. 1 were generated. Between-player note asynchrony was systematically manipulated in terms of level of within-player timing variability (Exp.1) and correction gain (Exp. 2). On each trial, participants listened to two samples, one (target) with more between-player asynchrony variability than the other (test), and reported which was less together. In both experiments, the test sample correction gain was fixed at the statistically optimal vlaue of 0.25 and the within-player timing variability was minimal (zero except for random variability in the initial note). In Exp. 1 the target correction gain was fixed at 0.25 and the timing variability was adjusted over trials by a staircase algorithm designed to converge on the level of asynchrony variability giving 75% correct identification. In Exp. 2 the timing variability in the target was set at half that in Exp. 1 and the correction gain was varied to converge on 75% correct identification.Our results show that the between-player asynchrony variability giving 75% correct identification in Exp. 2 was significantly lower than in Exp. 1. This finding indicates that people are sensitive to both the degree of variance and the micro-structure of the time-series

Abstract: Traditionally, approximation methods are utilized in the parametric interpolation because of the nonanalytic relationship between the spline parameter and the arc length. The approximation error has been considered as the source of the feedrate fluctuation. This paper shows that the discrepancy between the desired tool path and the target trajectory of the motion system is another primary source, and presents a feedback interpolator to eliminate the feedrate fluctuation. To evaluate the initial parameter value for the interpolator, an arc-length based Taylor's expansion with arc-length compensation is proposed, which alleviates greatly the feedrate command error caused by the trajectory deviation. Then, a feedback correction scheme is developed to further reduce the feedrate command error that results from the approximation error. Both computational load analysis and numerical simulations are conducted, and the results show that the present interpolator has very good performance in both efficiency and accuracy, thus is a good choice for high speed and high precision CNC machines. [Copyright &y& Elsevier]

We raise a traffic signal control method for isolate intersection based on heuristic search and feedback correction by combining ideas of the decision-generating traffic control methods and the decision-selection traffic control methods. The method searches the optimal control strategy by heuristic search and feedback correction in the practical control procedure. It can also detect and manage the change of the traffic flow dynamics through feedback correction. We verify the method by two kinds of numerical simulation experiments, the results show that our method is better than fixed-time control and actuated control, and it also has an advantage for it needs few initial configurations and manual settings. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online identification does not suit the real-time controller, due to its heavy computational burden. This work presents a computationally efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling air–fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives satisfactory performance and also adapts to the change in engine systems very quickly. [ABSTRACT FROM AUTHOR]

Abstract: Based on the matrix theory and the information sharing principle, the analytic relation among centralized Kalman filtering, decentralized filtering, and federated filtering is derived. It is proved that the global filtering of federated filters is optimal only when the dimensions of the master filter and the local filters are totally equal. If the dimensions of the master filter and the local filters are different, then only suboptimal solution can be obtained. The structure of a generalized federated filter is proposed. In terms of the information sharing principle, the information matrix of the one-step prediction state error and the one-step prediction state are reset to obtain the suboptimal solution of the global filtering. Furthermore, the suboptimal solution of the global filtering is used as observation feedback to correct the one-step prediction state and yield the optimal solution of the global filtering. The optimal feedback gain matrix is mathematically derived, so the filtering result is theoretically proved to be equivalent to the centralized Kalman filtering. The result of the simulation experiments with a dual-SINS/GPS integrated navigation system demonstrates the validity of the algorithm. [Copyright &y& Elsevier]

In order to reduce the impact of load power fluctuations on the power system and ensure the economic benefits of user-side energy storage operation, an optimization strategy of configuration and scheduling based on model predictive control for user-side energy storage is proposed in this study. Firstly, considering the cost and benefits of energy storage comprehensively, an energy storage configuration optimization model with the highest annualized net income as the goal is built to determine the parameters for configuring energy storage. Then, with the goal of maximizing the profit during the scheduling period, pre-month scheduling optimization model, day-ahead scheduling optimization model and intra-day scheduling optimization model are established. The goal of the pre-month scheduling optimization model is to determine the maximum monthly demand; part of the scheduling results in the day-ahead scheduling optimization model directly participate in the intra-day scheduling; the intra-day rolling optimization relies on the advantages of real-time feedback and closed-loop scheduling to smooth out power fluctuations caused by load forecast errors. Finally, the configuration and economic benefit of lithium iron phosphate batteries, lead-carbon batteries and sodium-sulfur batteries are analyzed and compared, and scheduling analysis is performed. The simulation results show that the proposed optimization method can cut peaks and fill valleys, ensure the economic benefits of users, and provide guidance for users to invest in energy storage. [ABSTRACT FROM AUTHOR]

Scheduling strategies are crucial for combined cooling, heating, and power-containing renewable energy source (RES-CCHP) systems to realize cost-effective operation. However, the prediction error generated by renewable energy sources (RES) and load leads to mismatch between the scheduling plan and actual demand. To address this problem, an interval scheduling method is proposed for the RES-CCHP system based on the model predictive control method. A genetic algorithm to optimize a back propagation (GA-BP) neural network combined with historical and weather data is used to predict the multistep RES and load to improve prediction accuracy. An interval optimization model (cost optimization) is then constructed based on RES and load prediction data to achieve an output consistent with the actual situation. Finally, feedback correction is used to compensate the RES and load prediction data online. The prediction error and interval are reduced, thereby improving the performance of the interval optimization method in achieving an optimal solution. This research is applicable to living spaces such as residential areas, schools, and hospitals. In this study, a hospital was considered as a case study to verify the effectiveness of the proposed method. A comparison of the results of the proposed method with those obtained in the following electrical load and following thermal load modes in a typical summer day revealed a cost reduction of 16.21% and 16.92%, respectively. The proposed method can improve the prediction accuracy of the RES and load of the RES-CCHP system and help decision makers to develop reasonable operation schemes and maximize profits. • Interval scheduling method for RES combined cooling, heating, and power systems based on model predictive control. • The weather data and genetic algorithm–BP neural network method accurately predict multistep source and load. • Interval optimization used upon considering source and load error of prediction caused by randomness. • Feedback correction to compensate prediction results and reduce optimization interval. [ABSTRACT FROM AUTHOR]