Feedforward Research Articles

Improved Model Predictive Control Path Tracking Approach Based on Online Updated Algorithm with Fuzzy Control and Variable Prediction Time Domain for Autonomous Vehicles

The design of trajectory tracking controllers for smart driving cars still faces problems, such as uncertain parameters and it being time-consuming. To improve the tracking performance of the trajectory tracking controller and reduce the computation of the controller, this paper proposes an improved model predictive control (MPC) method based on fuzzy control and an online update algorithm. First, a vehicle dynamics model is constructed and a feedforward MPC controller is designed; second, a real-time updating method of the time domain parameters is proposed to replace the previous method of empirically selecting the time domain parameters; lastly, a fuzzy controller is proposed for the real-time adjustment of the weight coefficient matrix of the model predictive controller according to the lateral and heading errors of the vehicle, and a state matrix-based cosine similarity updating mechanism is developed for determining the updating nodes of the state matrix to reduce the controller computation caused by the continuous updating of the state matrix when the longitudinal vehicle speed changes. Finally, the controller is compared with the traditional model prediction controller through the co-simulation of CARSIM and MATLAB/Simulink, and the results show that the controller has great improvement in terms of tracking accuracy and controller computational load.

Adaptive compound control of laser beam jitter in deep-space optical communication systems.

In deep-space optical communication systems, precise pointing and aiming of the laser beam is essential to ensure the stability of the laser link. In this paper, an adaptive compound control system based on adaptive feedforward and Proportional-Integral-Differentiation (PID) feedback is proposed. The feedforward controller is stabilized using Youla-Kucera (YK) parameterization. In the YK parameterized structure, the free parameter Q(z) consists of an adaptive filter. The proposed method constitutes an adaptive feedforward control algorithm through the adaptive filter Q(z). The problem of suppressing laser jitter is transformed into a problem of minimizing a sensitivity function containing the adaptive filter. The stability of the compound control system is ensured by configuring the individual parameters of the YK parameterized feedforward controller and the PID controller, and the adaptive regulation of the controller is realized on the premise of system stability. To verify the effectiveness of the compound control system, we established an experimental platform for laser beam stabilization control. We experimentally compare the effectiveness of the proposed control method with the classical method. The experimental results show that the method proposed in this paper can effectively suppress the complex laser beam jitter consisting of narrow-band sinusoidal and broad-band continuous vibrations.

Intelligent stability monitoring and improvement of grid‐connected converter under weighted average control

AbstractThis article presents an intelligent stability monitoring and improvement method for the grid‐connected converter system. The model of grid‐connected converter, based on the weighted average current feedback (WACF) and weighted average voltage feedforward (WAVF) control, is first established. Then, the time‐varying grid impedance and parameter perturbation of LCL‐filter are precisely identified by artificial neural network (ANN) module in real time. Furthermore, the control parameters are adaptively tuned by certain rules based on the predicted parameters to increase the high‐frequency stability margin of converter system. Simulation and experimental results are given to validate the proposed identification and parameter tuning method. The proposed method is able to monitor the real‐time operation state of the grid‐connected converter and improve the self‐adaptivity of the grid‐connected converter system against parameter perturbation.

Neuronal activity-driven O-GlcNAcylation promotes mitochondrial plasticity

Neuronal activity is an energy-intensive process that is largely sustained by instantaneous fuel utilization and ATP synthesis. However, how neurons couple ATP synthesis rate to fuel availability is largely unknown. Here, we demonstrate that the metabolic sensor enzyme O-linked N-acetyl glucosamine (O-GlcNAc) transferase regulates neuronal activity-driven mitochondrial bioenergetics in hippocampal and cortical neurons. We show that neuronal activity upregulates O-GlcNAcylation in mitochondria. Mitochondrial O-GlcNAcylation is promoted by activity-driven glucose consumption, which allows neurons to compensate for high energy expenditure based on fuel availability. To determine the proteins that are responsible for these adjustments, we mapped the mitochondrial O-GlcNAcome of neurons. Finally, we determine that neurons fail to meet activity-driven metabolic demand when O-GlcNAcylation dynamics are prevented. Our findings suggest that O-GlcNAcylation provides a fuel-dependent feedforward control mechanism in neurons to optimize mitochondrial performance based on neuronal activity. This mechanism thereby couples neuronal metabolism to mitochondrial bioenergetics and plays a key role in sustaining energy homeostasis.

Norm-Optimal Iterative Learning Control for Wind Turbines During Grid Faults

Due to the increasing share of (offshore) wind turbines, more stringent requirements on power quality have been established. Importantly, the low-voltage ride-through grid requirement states that a wind turbine must remain connected to the electrical grid after a short intermittent grid fault. In the industry mainly gain-scheduled PID-controllers are used to mitigate the effects of grid faults on turbine operation, whereas more advanced solutions have been proposed in the literature such as model predictive control or multiple parallel PI-controllers. Remarkably, all controller implementations mentioned earlier are based on feedback control, where no feedforward strategies have been discussed in the literature. However, feedforward control could improve grid fault recovery performance by exploiting the relatively known fault characteristics by virtue of the specification in the Transmission System Operator requirements. Therefore, for the first time, a norm-optimal Iterative Learning Control (NO-ILC) algorithm is presented that solves these issues by learning the feedforward signal that optimally mitigates the effects of a grid fault. The NO-ILC algorithm applies model-free learning based on iterations, in which the framework of NO-ILC has been extended to include explicit input constraints. The goal of the NO-ILC is to reduce a (quadratic) cost function on specific input and output channels whilst conforming to specific input constraints by solving an optimisation problem, with, for this study blade pitch and rotor speed as respective input and output channels. It is shown that the NO-ILC algorithm can yield improved performance on a high-fidelity model, with a 45% decrease in the cost function used by NO-ILC compared to the nominal feedback control. The optimised feedforward signals resulting from NO-ILC can be used as an analysis tool to closer match the nominal grid fault feedback controllers response with that of NO-ILC, or directly applied as a library that can supplement the feedback controllers output during a grid fault.

Friction compensation control method for a typical excavator system based on the accurate friction model

Traditional identification methods make it difficult to accurately determine the parameters of the friction model. Based on the LuGre friction model, a hybrid optimization algorithm (HO) is presented to address such aspects. This algorithm combines the genetic algorithm (GA) and particle swarm optimization algorithm (PSO) to obtain the globally optimal solutions. It incorporates three main improvements: introducing selection and crossover operations into the PSO algorithm, utilizing a variable inertia weight coefficient, and applying variable learning coefficients for enhanced performance. Compared to the static optimal solutions of the GA and PSO algorithms, the HO algorithm can improve the optimal solution by 40.68 % and 35.89 % respectively. The HO algorithm can achieve the highest model accuracy: the maximum identified friction error with the HO algorithm is only 1.70 kN, compared to 3.69 kN with the GA method and 8.52 kN with the PSO algorithm. Furthermore, a novel adaptive friction compensation controller (AC) is introduced based on the identification results to improve the stability and accuracy of the electro-hydraulic proportional systems of a 23-ton robotic excavator. Comparisons with existing proportional integral differential (PID) and feedforward compensation controllers (FC) highlight the superiority of the proposed adaptive controller in terms of trajectory accuracy and robustness. In sinusoidal trajectory tracking experiments, the AC method can achieve the highest tracking performance among the three controllers, and the maximum tracking error is only 11.56 mm. Compared to the FC controller, the control accuracy with the AC controller can be improved by 43.38 %. Experiments and simulations have shown that the friction compensation controller can effectively mitigate friction and other disturbances. As a result, the phenomena of crawling and flat peak phenomena can be eliminated.

Sliding mode observer-based model predictive tracking control for Mecanum-wheeled mobile robot

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method.

Robustness of LiDAR-assisted controller towards measurement uncertainty

Many studies have concluded that LiDAR-assisted control can be used to increase annual energy production (AEP) as well as reduce extreme loads and fatigue damage on blades, tower, and main bearing components. However, most studies assume perfect LiDAR measurements and do not consider large measurement uncertainties on LiDAR measurements. In this study we evaluate the potential benefits in terms of fatigue load reduction from using a simple robust feedforward controller. In addition, we evaluate the impact of measurement uncertainties exemplified by a time shift between the expected time the wind hits the rotor and the actual time it hits the rotor as well as a mean wind speed measurement bias. Results shows that fatigue loads can be reduced up to 7% on the tower sections and up to 4% on other main components. However, these results only applies when the LiDAR is assumed to give perfect information about what is hitting the rotor plane. The load reduction degrades with the magnitude of a time shift of the arriving LiDAR wind measurements. A delay up to 2.5 seconds still gives reasonable load reductions. Furthermore, a positive wind speed bias does not affect the performance of the LiDAR-assisted controller, whereas a negative bias significantly deteriorates performance.

Current ripple optimization design of DC/DC converter

Abstract Renewable energy has the advantages of renewability and high temperature and has great potential for development, but its instability limits its large-scale promotion. Aiming at the stable operation of the new energy power system and ensuring the safe and reliable operation of the power grid, this project defines the basic topology of the variable multiple DC/DC converter according to the idea of switching circuit parallel factor or reducing ripple under each duty cycle. Secondly, the system structure under the boost mode is modeled, and the system is optimized by the combination of a single closed-loop current balance and duty cycle feedforward control. Finally, the model is simulated, and the results show that the method is correct and reasonable.

Integral sliding mode-based event-triggered optimal fault tolerant tracking control of continuous-time nonlinear systems

In this paper, the integral sliding mode-based event-triggered optimal fault tolerant tracking control of continuous-time nonlinear systems is investigated via adaptive dynamic programming. The developed control scheme consists of two parts, i.e., integral sliding mode control and event-triggered optimal tracking control. For the first part, an integral sliding mode controller is designed to eliminate the affect of actuator fault and the dynamics of nominal nonlinear systems is obtained. For the second part, a novel quadratic cost function with respect to the tracking error and its dynamics is developed such that the feedforward control law or the discount factor is not required, which reduces the complexity of the control method and guarantees the tracking performance. Moreover, a critic-only structure is established to obtain the solution of tracking Hamilton–Jacobi–Bellman equation. It should be noted that the optimal tracking control law is updated only at triggering moments in order to preserve computing and communication resources. Finally, the effectiveness of the present approach is demonstrated through simulation examples of a robotic arm system and a Van der Pol circuit system.

Performance similarities between standard and retrofit LiDAR-assisted control for wind turbines

LiDAR-assisted control has proven to be a highly effective method for mitigating rotor speed deviations and reducing loads in wind turbines. This effectiveness stems from the ability of feedforward controllers to utilize incoming wind speed information obtained from LiDARs, enabling advanced blade pitch actions before the wind disturbs the turbine. However, the standard implementation of LiDAR-assisted control often necessitates modifications to the existing feedback controller, where the feedforward pitch rate is typically integrated into the feedback controller. This process can be challenging in terms of accessibility and may be limited to specific stakeholders, such as turbine manufacturers.A retrofit design provides an ideal solution, where the retrofit LiDAR-assisted controller modifies the rotor speed measurement to induce pitch actions without requiring alterations to the existing feedback controller. This paper aims to demonstrate the performance similarities between the standard LiDAR-assisted controller and its retrofit counterpart. Specifically, we establish that the retrofit LiDAR-assisted controller, with appropriate tuning, is equivalent to its standard counterpart. This equivalence implies that architecturally dissimilar controllers can yield the same performance in terms of rotor speed deviations and tower load reductions. The presented findings are supported by results obtained from high-fidelity closed-loop turbine simulations.

Intellectual assessment of amyotrophic lateral sclerosis using deep resemble forward neural network

ALS (Amyotrophic Lateral Sclerosis) is a neurodegenerative disorder causing profound physical disability that severely impairs a patient's life expectancy and quality of life. It also leads to muscular atrophy and progressive weakness of muscles due to insufficient nutrition in the body. At present, there are no disease-modifying therapies to cure ALS, and there is a lack of preventive tools. The general clinical assessments are based on symptom reports, neurophysiological tests, neurological examinations, and neuroimaging. But, these techniques possess various limitations of low reliability, lack of standardized protocols, and lack of sensitivity, especially in the early stages of disease. So, effective methods are required to detect the progression of the disease and minimize the suffering of patients. Extensive studies concentrated on investigating the causes of neurological disease, which creates a barrier to precise identification and classification of genes accompanied with ALS disease. Hence, the proposed system implements a deep RSFFNNCNN (Resemble Single Feed Forward Neural Network-Convolutional Neural Network) algorithm to effectively classify the clinical associations of ALS. It involves the addition of custom weights to the kernel initializer and neutralizer ‘k’ parameter to each hidden layer in the network. This is done to increase the stability and learning ability of the classifier. Additionally, the comparison of the proposed approach is performed with SFNN (Single Feed NN) and ML (Machine Learning) based algorithms, namely, NB (Naïve Bayes), XGBoost (Extreme Gradient Boosting) and RF (Random Forest), to estimate the efficacy of the proposed model. The reliability of the proposed algorithm is measured by deploying performance metrics such as precision, recall, F1 score, and accuracy.

An event triggered control scheme for enhanced production of Escherichia coli and biomass concentration during fed-batch cultivation

Control of a bioprocess is a challenging task mainly due to the nonlinearity of the process, the complex nature of microorganisms, and variations in critical parameters such as temperature, pH, and agitator speed. Generally, the optimum values chosen for critical parameters during Escherichia coli (E.coli) K-12fed-batch fermentation are37 ᵒC for temperature, 7 for pH, and 35 % for Dissolved Oxygen (DO). The objective of this research is to enhance biomass concentration while minimizing energy consumption. To achieve this, an Event-Triggered Control (ETC) scheme based on feedback-feed forward control is proposed. The ETC system dynamically adjusts the substrate feed rate in response to variations in critical parameters. We compare the performance of classical Proportional Integral (PI) controllers and advanced Model Predictive Control (MPC) controllers in terms of bioprocess yield. Initially, the data are collected from a laboratory-scaled 3L bioreactor setup under fed-batch operating conditions, and data-driven models are developed using system identification techniques. Then, classical Proportional Integral (PI) and advanced Model Predictive Control (MPC) based feedback controllers are developed for controlling the yield of bioprocess by manipulating substrate flow rate, and their performances are compared. PI and MPC-based Event Triggered Feed Forward Controllers are designed to increase the yield and to suppress the effect of known disturbances due to critical parameters. Whenever there is a variation in the value of a critical parameter, it is considered an event, and ETC initiates a control action by manipulating the substrate feed rate. PI and MPC-based ETC controllers are developed in simulation, and their closed-loop performances are compared. It is observed that the Integral Square Error (ISE) is notably minimized to 4.668 for MPC with disturbance and 4.742 for MPC with Feed Forward Control. Similarly, the Integral Absolute Error (IAE) reduces to 2.453 for MPC with disturbance and 0.8124 for MPC with Feed Forward Control. The simulation results reveal that the MPC-based ETC control scheme enhances the biomass yield by 7 %, and this result is verified experimentally. This system dynamically adjusts the substrate feed rate in response to variations in critical parameters, which is a novel approach in the field of bioprocess control. Also, the proposed control schemes help reduce the frequency of communication between controller and actuator, which reduces power consumption.

Comprehensive compensation of dynamic hysteresis and creep for piezoelectric actuator

This paper addresses the modeling of dynamic hysteresis and creep in piezoelectric actuators, and employs feedforward open-loop control based on inverse models to compensate for hysteresis and creep phenomena. The comprehensive model consists of quasi-dynamic and dynamic components. The quasi-dynamic model combines the quasi-dynamic Prandtl–Ishlinskii (PI) model with an PI-based linear time-invariant model, while the dynamic part utilizes the auto-regressive exogenous model. The model accurately describes creep and dynamic hysteresis with modeling errors of less than 0.01 μm and 0.14 μm, respectively. The inversion of the comprehensive model has been proven to exhibit unique convergence. Under inverse feedforward control, the improvement in dynamic hysteresis and hysteresis with creep can be achieved at 94% and 83%, respectively. The comprehensive model proposed in this paper accurately describes the dynamic hysteresis and creep phenomena in piezoelectric actuators and realizes open-loop compensation control, achieving precise actuation of piezoelectric actuators.

Optimizing wastewater treatment plant operational efficiency through integrating machine learning predictive models and advanced control strategies

This research optimizes wastewater treatment plant (WWTP) operational performance by integrating advanced control strategies and predictive modeling. Emphasizing the significance of machine learning (ML), feature extraction techniques (filter, wrapper, and embedded methods) were employed to develop robust prediction models. The random forest (RF) model was applied to predict target variables, effluent ammonia, and nitrogen concentrations. Integrating these predictive models into the WWTP’s control system is necessary for enhanced efficiency and pollution regulation. Benchmark Simulation Model 1 (BSM1) was used as the WWTP model. The two tested control strategies included a hybrid approach, combining feedforward and feedback control, resulting in an improved effluent quality index (EQI), a marginal increase in aeration energy (AE) and the operational cost index (OCI), and a significant decrease in effluent ammonia concentration. The second strategy utilized self-organizing fuzzy inference system (SOFIS) control, resulting in promising outcomes with improvements in EQI, ammonia, and nitrogen concentrations, with negligible increases in AE and OCI. The findings highlight the pivotal role of predicting effluent quality parameters and integrating the prediction into WWTP control systems. This integrated approach proves effective in optimizing pollutant regulation and overall system performance. The research provides insights into the practical implementation of ML-based control strategies in wastewater treatment. It offers future scope for exploring advanced ML algorithms and their real-time application in operational WWTPs. This research introduces a novel approach by integrating machine learning with the BSM1 weather dataset and sensor data for feature selection to predict effluent concentrations in a WWTP. Through the comparative analysis with the default proportional-integral (PI) control configuration, the research highlights the importance of integrating machine learning techniques into WWTP control systems.

Efficient feedforward sloshing suppression strategy for liquid transport

This paper introduces an advanced feedforward control method to suppress sloshing in high-speed liquid container transfers in robotic systems. Three key concepts are combined: (1.) A virtual pendulum tray is envisaged to suspend the liquid container. By moving only the virtual pivot, the resulting container motion robustly reduces sloshing. (2.) A geometric condition is found for the virtual tray/container assembly to fully suppress one specific sloshing mode, allowing rapid transfers in a way that the dominant sloshing dynamics is fully suppressed. (3.) An analytic feedforward trajectory control based on differential flatness is formulated to design fast and efficient rest-to-rest transport manoeuvres. Any sloshing of modelled higher modes which is excited during the manoeuvre is accurately stopped at the end of such a manoeuvre. The resulting control law is computationally simple and achieves excellent sloshing-suppression performance. The concept is validated via simulations, finite-element analyses, and experimental tests, proving excellent theoretical and real-world performance and showing high robustness even with variations in the liquid filling level.

Let-7 restrains an oncogenic epigenetic circuit in AT2 cells to prevent ectopic formation of fibrogenic transitional cell intermediates and pulmonary fibrosis.

Analysis of lung alveolar type 2 (AT2) progenitor stem cells has highlighted fundamental mechanisms that direct their differentiation into alveolar type 1 cells (AT1s) in lung repair and disease. However, microRNA (miRNA) mediated post-transcriptional mechanisms which govern this nexus remain understudied. We show here that the let-7 miRNA family serves a homeostatic role in governance of AT2 quiescence, specifically by preventing the uncontrolled accumulation of AT2 transitional cells and by promoting AT1 differentiation to safeguard the lung from spontaneous alveolar destruction and fibrosis. Using mice and organoid models with genetic ablation of let-7a1/let-7f1/let-7d cluster (let-7afd) in AT2 cells, we demonstrate prevents AT1 differentiation and results in aberrant accumulation of AT2 transitional cells in progressive pulmonary fibrosis. Integration of enhanced AGO2 UV-crosslinking and immunoprecipitation sequencing (AGO2-eCLIP) with RNA-sequencing from AT2 cells uncovered the induction of direct targets of let-7 in an oncogene feed-forward regulatory network including BACH1/EZH2 which drives an aberrant fibrotic cascade. Additional analyses by CUT&RUN-sequencing revealed loss of let-7afd hampers AT1 differentiation by eliciting aberrant histone EZH2 methylation which prevents the exit of AT2 transitional cells into terminal AT1s. This study identifies let-7 as a key gatekeeper of post-transcriptional and epigenetic chromatin signals to prevent AT2-driven pulmonary fibrosis.

Deep Learning based Feed Forward Neural Network Models for Hyperspectral Image Classification

Introduction Traditional feed-forward neural networks (FFNN) have been widely used in image processing, but their effectiveness can be limited. To address this, we develop two deep learning models based on FFNN: the deep backpropagation neural network classifier (DBPNN) and the deep radial basis function neural network classifier (DRBFNN), integrating convolutional layers for feature extraction. Methods We apply a training algorithm to the deep, dense layers of both classifiers, optimizing their layer structures for improved classification accuracy across various hyperspectral datasets. Testing is conducted on datasets including Indian Pine, University of Pavia, Kennedy Space Centre, and Salinas, validating the effectiveness of our approach in feature extraction and noise reduction. Results Our experiments demonstrate the superior performance of the DBPNN and DRBFNN classifiers compared to previous methods. We report enhanced classification accuracy, reduced mean square error, shorter training times, and fewer epochs required for convergence across all tested hyperspectral datasets. Conclusion The results underscore the efficacy of deep learning feed-forward classifiers in hyperspectral image processing. By leveraging convolutional layers, the DBPNN and DRBFNN models exhibit promising capabilities in feature extraction and noise reduction, surpassing the performance of conventional classifiers. These findings highlight the potential of our approach to advance hyperspectral image classification tasks.

An Adjoint Based Data-Driven Inverse-Free Iterative Feedforward Control Method

Abstract Feedforward control is widely used in control systems since it can achieve high tracking performance by effectively compensating for known disturbances before they affect the system. For traditional feedforward control methods, the performance improvement highly depends on the model quality of the system model and the accuracy of the model-inversion. However, on one hand, in practice, the modeling error is inevitable, especially for precision motion systems with complex dynamics, on the other hand, the non-minimum phase (NPM) systems often lead to a problem that plant inversion is unstable and non- causal. Therefore, this paper proposes an approach that combines the bene?ts of data-driven feedforward control and the gradient descent method. This integrated approach aims to address challenges related to laborious model identi?cation and unstable plant inversion simultaneously. The main idea is to replace the model with dedicated experiments on the system and to avoid calculating plant inversion by applying the gradient descent method to the learning process. The simulation and experimental results show that the algorithm can achieve the optimal point-to-point tracking performance without relying on model-inversion.

Research on Stability Control of Distributed Drive Vehicle with Four-Wheel Steering

The four-wheel steering distributed drive vehicle is a novel type of vehicle with independent control over the four-wheel angle and wheel torque. A method for jointly controlling the distribution of the wheel angle and torque is proposed based on this characteristic. Firstly, the two-degrees-of-freedom model and ideal reference model of four-wheel steering vehicle are established; then, the four-wheel steering controller and torque distribution controller are designed. The rear wheel angle is controlled by the feedforward controller and the feedback controller. The feedforward controller takes the side slip angle of the center of mass as the control target, and the feedback controller takes the yaw angle as the control target. Torque is controlled by two control layers, the additional yaw moment of the upper layer is calculated by the vehicle motion state and fuzzy control theory, and the lower layer distributes wheel torque through the road adhesion coefficient and wheel load. Finally, a simulation platform is established to verify the effectiveness of the proposed control algorithm.