Multi-Gene Genetic Programming-Based Identification of a Dynamic Prediction Model of an Overhead Traveling Crane

This paper proposes a multi-gene genetic programming (MGGP) approach to identifying the dynamic prediction model for an overhead crane. The proposed method does not rely on expert knowledge of the system and therefore does not require a compromise between accuracy and complex, time-consuming modeling of nonlinear dynamics. MGGP is a multi-objective optimization problem, and both the mean square error (MSE) over the entire prediction horizon as well as the function complexity are minimized. In order to minimize the MSE an initial estimate of the gene weights is obtained by using the least squares approach, after which the Levenberg–Marquardt algorithm is used to find the local optimum for a k-step ahead predictor. The method was tested on both a simulation model obtained from the Euler–Lagrange equation with friction and the experimental stand. The simulation and the experimental stand were trained with varying control inputs, rope lengths and payload masses. The resulting predictor model was then validated on a testing set, and the results show the effectiveness of the proposed method.

An active set solver for constrained $ H_\infty $ optimal control problems with state and input constraints

<p style='text-indent:20px;'>This paper proposes an active set solver for <inline-formula><tex-math id="M2">\begin{document}$ H_\infty $\end{document}</tex-math></inline-formula> min-max optimal control problems involving linear discrete-time systems with linearly constrained states, controls and additive disturbances. The proposed solver combines Riccati recursion with dynamic programming. To deal with possible degeneracy (i.e. violations of the linear independence constraint qualification), constraint transformations are introduced that allow the surplus equality constraints on the state at each stage to be moved to the previous stage together with their Lagrange multipliers. In this way, degeneracy for a feasible active set can be determined by checking whether there exists an equality constraint on the initial state over the prediction horizon. For situations when the active set is degenerate and all active constraints indexed by it are non-redundant, a vertex exploration strategy is developed to seek a non-degenerate active set. If the sampled state resides in a robust control invariant set and certain second-order sufficient conditions are satisfied at each stage, then a bounded <inline-formula><tex-math id="M3">\begin{document}$ l_2 $\end{document}</tex-math></inline-formula> gain from the disturbance to controlled output can be guaranteed for the closed-loop system under some standard assumptions. Theoretical analysis and numerical simulations show that the computational complexity per iteration of the proposed solver depends linearly on the prediction horizon.</p>

A Predictive Energy Management Strategy for Multi-Energy Source Vehicles Based on Full-Factor Trip Information

To achieve the real-time application of a dynamic programming (DP) control strategy, we propose a predictive energy management strategy (PEMS) based on full-factor trip information, including vehicle speed, slip ratio and slope. Firstly, the prediction model of the full-factor trip information is proposed, which provides an information basis for global optimization energy management. To improve the prediction’s accuracy, the vehicle speed is predicted based on the state transition probability matrix generated in the same driving scene. The characteristic parameters are extracted by a feature selection method taken as the basis for the driving condition’s identification. Similar to speed prediction, regarding the uncertain route at an intersection, the slope prediction is modelled as a Markov model. On the basis of the predicted speed and the identified maximum adhesion coefficient, the slip ratio is predicted based on a neural network. Then, a predictive energy management strategy is developed based on the predictive full-factor trip information. According to the statistical rules of DP results under multiple standard driving cycles, the reference SOC trajectory is generated to ensure global sub-optimality, which determines the feasible state domain at each prediction horizon. Simulations are performed under different types of driving conditions (Urban Dynamometer Driving Schedule, UDDS and World Light Vehicle Test Cycle, WLTC) to verify the effectiveness of the proposed strategy.

Hydropower production prediction using artificial neural networks: an Ecuadorian application case

Hydropower is among the most efficient technologies to produce renewable electrical energy. Hydropower systems present multiple advantages since they provide sustainable and controllable energy. However, hydropower plants’ effectiveness is affected by multiple factors such as river/reservoir inflows, temperature, electricity price, among others. The mentioned factors make the prediction and recommendation of a station’s operational output a difficult challenge. Therefore, reliable and accurate energy production forecasts are vital and of great importance for capacity planning, scheduling, and power systems operation. This research aims to develop and apply artificial neural network (ANN) models to predict hydroelectric production in Ecuador’s short and medium term, considering historical data such as hydropower production and precipitations. For this purpose, two scenarios based on the prediction horizon have been considered, i.e., one-step and multi-step forecasted problems. Sixteen ANN structures based on multilayer perceptron (MLP), long short-term memory (LSTM), and sequence-to-sequence (seq2seq) LSTM were designed. More than 3000 models were configured, trained, and validated using a grid search algorithm based on hyperparameters. The results show that the MLP univariate and differentiated model of one-step scenario outperforms the other architectures analyzed in both scenarios. The obtained model can be an important tool for energy planning and decision-making for sustainable hydropower production.

Modified Predictive Control System of the DC-DC Boost Converter

Nowadays, predictive control systems are becoming more and more popular, which significantly reduce the cost of setting up converters. However, DC-DC converter control problem persists. In this work, a modified model of the predictive control system (MPCS) for step-up DC-DC converters is presented. For its implementation, a nonlinear model of a converter with discrete time switching was derived, which describe a continuous conduction mode of operation. The synthesis of the controller was achieved by formulating the objective function that should be minimized considering the dynamic model of the converter. The proposed predictive control strategy, used as a voltage control system, allows keeping the output voltage at the reference level. The modified system for calculating the objective function makes it possible to significantly reduce the required computing power and expand the prediction horizon. The results of modeling have been presented that demonstrate the advantages of the proposed control method: a fast transient response and a high degree of robustness.

Time Adaptivity in Model Predictive Control

The core of the Model Predictive Control (MPC) method in every step of the algorithm consists in solving a time-dependent optimization problem on the prediction horizon of the MPC algorithm, and then to apply a portion of the optimal control over the application horizon to obtain the new state. To solve this problem efficiently, we propose a time-adaptive residual based a-posteriori error control concept based on the optimality system of this optimal control problem. This approach not only delivers an adaptive time discretization of the prediction horizon, but also suggests an adaptive time discretization of the application horizon, whose length could be either adaptive or fixed. We apply this concept for systems governed by linear parabolic PDEs and present several numerical examples which demonstrate the performance and the robustness of our adaptive MPC control concept.

Use of repeated mammograms to evaluate risk of breast cancer: a systematic review of methods used in the literature

Objective This systematic review aimed to assess methods used to relate repeated mammographic images to breast cancer risk, including the time from mammogram to diagnosis of breast cancer, and methods for analysis of data from either one or both breasts (averaged or assessed individually). Design A systematic review was performed. Setting The databases including in Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov. were searched through October 2021, to extract published articles in English, describing relationship of the change in mammographic features with risk of breast cancer. Participants Women with mammogram images. Main outcome measure Breast cancer incidence. Results Twenty articles were included in the final review. We found that BIRADs and Cumulus were most commonly used for classifying mammographic density and automated assessment was used on more recent digital mammograms. Time between mammograms varied from 1 to 4 years, and only 9 of the studies used more than 2 mammograms to quantify features. One study used a prediction horizon of 5 and 10 years, one used 5 years only and another 10 years only, while in the others the prediction horizon was not clearly defined with investigators using the next screening mammogram. Conclusion This review provided an updated overview of the state of the art and revealed research gaps; based on these, we provide recommendations for future studies using repeated measure methods for mammogram images to make the use of accumulating image data. By following these recommendations, we expect to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons.

A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays

Piezoelectric actuators are widely used in micro/nanoscale robotic manipulators. Due to its hysteresis and dynamic-related nonlinearity, accurate displacement tracking control of piezoelectric actuator is challenging. Besides, in some low-cost practical systems with low sampling rate, transmission delay causes mismatches between feedback and real displacement, further increasing the challenge in tracking control. In this article, a neural network-based model predictive controller (MPC) is proposed for precise tracking control of piezoelectric actuator’s displacement in situation where feedback is slow and delayed. The prediction model is based on a nonlinear-autoregressive-moving-average-with-exogenous-inputs framework, which outputs entire prediction horizon of future displacement in a single time, and is fulfilled by a multilayer feedforward neural network. An extended Kalman filter-based estimation for displacement is introduced to relieve the influence of feedback delays so as to improve dynamic performance of the controller. Another neural network is trained to provide initial values for MPC to reduce computation costs and improve performance in dynamic tracking. In a series of tracking experiments, the effectiveness of proposed controller is verified.

Motion Boundaries Formation For Autonomous Vehicles

<div>In this paper, the application of Model Predictive Control to perform curvilinear motion planning is explored. More specifically, nonlinear MPC will be focused on because of its proven efficiency in the modeling of uncertainties as well as in nonlinear model dynamics. The main objective of this report is to show that with proper modeling and formulation of motion constraints, curvilinear motion planning can be achieved with nonlinear MPC. The trajectory of the vehicle will be tracked with the least error while satisfying constraints such as speed and steering angles. Simulations are presented which demonstrate the ability of the suggested models to successfully perform curvilinear motion staying safely within the bounds, while simulations of several models validate its performance. A deterministic sensitivity analysis was conducted in order to determine the impact</div><div>of the prediction horizon time. Experimental results show that a critical prediction horizon time approximately 10 to 13 seconds was identified as the ideal range for optimal results of the model.</div>