Feedforward Approach Research Articles

Dynamics and control of a turbocharged solid oxide fuel cell system

• A cold bypass valve control can comply with the fuel cell system constraints. • Four controllers were tested on the system during load variation scenarios. • The cascade controllers with feed-forward approaches are the most promising. • A proportional-integrative cannot control the system properly for quick transients. The purpose of this paper regards the design and testing of control systems for a 30-kW turbocharged solid oxide fuel cell system fuelled with biogas. The adoption of a turbocharger, instead of a micro gas turbine, for the fuel cell stack pressurisation, is an innovative solution that is expected to decrease the capital cost of such systems and to facilitate their penetration into the energy market. However, not being connected to an electric generator, the turbocharger rotational speed, and thus the air mass flow, cannot be directly controlled as in microturbines. The control of turbocharged solid oxide fuel cell systems is a novel topic, characterised by many technical challenges that have not been addressed before. To regulate the stack temperature, a cold bypass valve is included, connecting the compressor outlet to the turbine inlet. A dynamic model of this system was developed in Matlab-Simulink® to analyse the response of the turbocharged solid oxide fuel cell system to a cold bypass valve opening step change. System information obtained from this analysis was used to design and tune four controllers: a conventional proportional integral controller and three different cascade controllers. The controller performance was evaluated under two different scenarios, considering quite aggressive power ramps. The best results were obtained with a cascade controller, where the feedback loop was complemented by a feed-forward contribution based on power demand. This analysis demonstrated that such a control system effectively tracks the fuel cell maximum temperature target, complying with all the system operative constraints.

Peritoneal Metastases From Colorectal Cancer: Defining and Addressing the Challenges.

The presence of peritoneal metastases (PM) in patients with colorectal cancer (CRC) is associated with an extremely poor prognosis. The diagnosis of PM is challenging, resulting in an underestimation of their true incidence. While surgery can be curative in a small percentage of patients, effective treatment for non-operable PM is lacking, and clinical and pre-clinical studies are relatively sparse. Here we have defined the major clinical challenges in the areas of risk assessment, detection, and treatment. Recent developments in the field include the application of organoid technology, which has generated highly relevant pre-clinical PM models, the application of diffusion-weighted MRI, which has greatly improved PM detection, and the design of small clinical proof-of-concept studies, which allows the efficient testing of new treatment strategies. Together, these developments set the stage for starting to address the clinical challenges. To help structure these efforts, a translational research framework is presented, in which clinical trial design is based on the insight gained from direct tissue analyses and pre-clinical (organoid) models derived from CRC patients with PM. This feed-forward approach, in which a thorough understanding of the disease drives innovation in its clinical management, has the potential to improve outcome in the years to come.

FPGA-Based Active Cancellation of the EMI of a Boost Power Factor Correction (PFC) by Injecting Modulated Sine Waves

Boost power factor corrections tend to be considerable sources of electromagnetic disturbances. To ensure the proper functionality of susceptible systems in the vicinity, the emissions must be reduced. Passive filters, as a conventional solution to reduce conducted emissions, usually suffer from their high volume, weight and costs. Active filters can help to mitigate this problem, but the known solutions are systematically limited by inherent time constants and signal propagation delays since they derive the cancellation signal directly from a measured quantity by a feedforward and/or feedback approach. To resolve this issue, each harmonic can be individually suppressed by an artificially synthesized sine wave that is synchronized to the disturbances. By adjusting the amplitudes and phases of the suppressing sine waves, bothersome time constants and delay times can be compensated. In this work, it is shown that the switching harmonics of a power factor correction can be interpreted as modulated sine waves. By modulating the suppressing sine waves simultaneously, a very high performance can be achieved for the active cancellation system. The hardware and the algorithm are described, optimum parameters for the algorithm are identified and an FPGA implementation is applied to a real PFC.

Data‐driven parameter tuning for rational feedforward controller: Achieving optimal estimation via instrumental variable

Feedforward control has been widely used to improve the tracking performance of precision motion systems. This paper develops a new data-driven feedforward tuning approach associated with rational basis functions. The aim is to obtain the global optimum with optimal estimation accuracy. First, the instrumental variable is employed to ensure the unbiased estimation of the global optimum. Then, the optimal instrumental variable which leads to the highest estimation accuracy is derived, and a new refined instrumental variable method is exploited to estimate the optimal instrumental variable. Moreover, the estimation accuracy of the optimal parameter is further improved through the proposed parameter updating law. Simulations are conducted to test the parameter estimation accuracy of the proposed approach, and it is demonstrated that the global optimum is unbiasedly estimated with optimal parameter estimation accuracy in terms of variance with the proposed approach. Experiments are performed and the results validate the excellent performance of the proposed approach for varying tasks.

Feed-forward Modulation Technique for more Accurate Operation of Modular Multilevel Converters

Modular multilevel converters have become the prominent topology for medium- and high-voltage applications. The performance of these converters highly depends on the accuracy of the used modulation approach, for which the capacitor voltage of submodules (SM) are usually assumed to be equal. This article exhibits that ignoring the capacitor voltage differences among SMs adversely affects the system performance. This becomes more obvious the larger the capacitor voltage differences are. Hence, this article proposes a more accurate feedforward modulation approach that takes into account either the instantaneous capacitor voltage value and the real output voltage in the modulation stage. As a result, in applications where larger SM voltage differences are expected, the current distortion and control performance are improved. Particularly, switching–saving approaches benefit from this method as it enables their operation with reduced switching losses without the downsides of increased distortion due to capacitor voltage differences. The proposed approach is analyzed and compared with the nearest-level modulation and with the level-shift PWM. Simulations and experimental validation are presented to confirm the effectiveness of the proposed technique.

Increasing Trajectory Tracking Accuracy of Industrial Robots Using SINDYc

Abstract In this work a feedforward control approach based on SINDYc (Sparse Identification of Nonlinear Dynamics with Control) is proposed for increasing the trajectory tracking accuracy of industrial robots. Initially, the dynamic relationship between the desired and the actual trajectory is sparsely identified using polynomial basis functions. Then a new trajectory is created from the desired trajectory using a feedforward controller based on the inverse of the sparsely identified dynamic model. The effectiveness of the proposed approach is evaluated by a simulation study in which 4 different KUKA robots were tasked to follow 16 distinct trajectories based on ISO 9283 standard. The obtained results show that the proposed method successfully models the dynamic relationship between the desired and the actual trajectory with accuracies above 98.09% when all of the robots are considered. Moreover, the developed feedforward controller improves the trajectory tracking accuracy of industrial robots by at least 91.1% and 94.5% for position and orientation tracking, respectively while providing parsimonious models.

Effect of Feed-Forward Approach and Students Achievement in Physics at Secondary School Level in Uganda

Effect of Feed-Forward Approach and Students Achievement in Physics at Secondary School Level in Uganda

A feedforward approach to mid-ranging control

Mid-ranging control is a control strategy that is used when there are more than one manipulated variable available to control a process variable. Mid-ranging control handles the redundancy by coordinating the roles of the different manipulated variables. The most common approach is to introduce valve position controllers (VPC) that control the relations between the manipulated variables. In this paper, some drawbacks of the VPC approach are pointed out, and a new control strategy that overcomes these drawbacks is presented. Instead of using valve position controllers, the new strategy uses feedforward control to coordinate the manipulated variables. Design methods for both the new strategy and the VPC controller are presented in the paper.

A Robust Lyapunov’s Demodulator for Tracking of Single-/Three-Phase Grid Voltage Variables

This article proposes a robust Lyapunov’s demodulator (LD) based on an orthogonal signal generation (OSG) approach for single-/three-phase applications. The LD is not normally capable of rejecting the dc-offset in the grid signal, and an additional estimation loop is, therefore, required, which may affect the dynamic performance. Nevertheless, an application of a harmonically polluted grid voltage signal to the LD may severely affect the steady-state performance of the parameters estimated. These issues are addressed by proposing an enhanced LD-based-OSG in which a moving average filter is incorporated into the LD-OSG structure. The rapid rejection of dc-offset and harmonics is, therefore, easily achieved without any additional loop. The proposed structure can accurately estimate the fundamental in-phase and quadrature components. However, these orthogonal components may suffer from amplitude imbalance and errors in the phase information under off-nominal frequency conditions. Nevertheless, the errors in the amplitude and the phase information are eliminated using an open-loop frequency deviation detector and a feed-forward curve-fitting approach. The dynamic performance of the proposed scheme has been validated by means of numerical and hardware studies whose results show that, with the less sensitivity toward the phase angle jump and good immunity to the fundamental negative sequence, the proposed scheme is a potential technique with which to synchronize single-/three-phase grid-connected power electronic equipment.

A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface

With the growing demand for clean and economically feasible renewable energy, solar photovoltaic (PV) system usage has increased. Among many factors, the tilt and azimuth angles are of great importance and influence in determining the photovoltaic panel’s efficiency to generate electricity. Although much research was conducted related to solar PV panels’ performance, this work critically determined the tilt and azimuth angles for PV panels in all countries worldwide. The optimum tilt and azimuth angles are estimated worldwide by the photovoltaic geographic information system (PVGIS). Also, annual and average daily solar irradiation incident on the tilted and oriented plate optimally (AR1 and DR1) are calculated. Besides, annual and average daily solar irradiation incident on plate tilt optimally and oriented because of the south in the northern hemisphere and because of the north in the southern hemisphere (AR2 and DR2) are estimated. PVGIS is also used to calculate the annual and average daily solar irradiation incident on the horizontal plate (AR3 and DR3). The data collected from PVGIS are used to develop an efficient and accurate artificial neural network model based on feed-forward neural network approach. This model is an essential subpart that can be used in an embedded system or an online system for further PV system analysis and optimization. The developed neural model reflected very high accuracy in predicting the PV panels’ optimal tilt and azimuth angles worldwide. The benefit of tilting is generally increased by increasing the latitude. As the latitude increases, the tilt factor (F) increases because of the increase in the optimum tilt angle by increasing the latitude. The optimal orientation is due to the north in the southern hemisphere and due to the south in the northern hemisphere for most cities worldwide. In sum, it can be concluded that the optimum tilt angle is equal to or greater than the latitude until the latitude 30°. The optimum tilt angle becomes less than the latitude, and the difference is increased until it reaches more than 20°. Hence in this study the aim is to develop a simple neural network model which can accurately predict the annual radiation and optimum tilt and azimuth angle in any region of the world and can be easily implemented in a low-cost microcontroller.

Power Control during Remote Laser Welding Using a Convolutional Neural Network.

The increase in complex workpieces with changing geometries demands advanced control algorithms in order to achieve stable welding regimes. Usually, many experiments are required to identify and confirm the correct welding parameters. We present a method for controlling laser power in a remote laser welding system with a convolutional neural network (CNN) via a PID controller, based on optical triangulation feedback. AISI 304 metal sheets with a cumulative thickness of 1.5 mm were used. A total accuracy of 94% was achieved for CNN models on the test datasets. The rise time of the controller to achieve full penetration was less than 1.0 s from the start of welding. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was used to further understand the decision making of the model. It was determined that the CNN focuses mainly on the area of the interaction zone and can act accordingly if this interaction zone changes in size. Based on additional testing, we proposed improvements to increase overall controller performance and response time by implementing a feed-forward approach at the beginning of welding.

A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder.

Autism disorder, generally known as Autism Spectrum Disorder (ASD) is a brain disorder characterized by lack of communication skills, social aloofness and repetitions in the actions in the patients, which is affecting millions of the people across the globe. Accurate identification of autistic patients is considered a challenging task in the domain of brain disorder science. To address this problem, we have proposed a three-stage feature selection approach for the classification of ASD on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI Dataset. In the first stage, a large neural network which we call a “Teacher ” was trained on the correlation-based connectivity matrix to learn the latent representation of the input. In the second stage an autoencoder which we call a “Student” autoencoder was given the task to learn those trained “Teacher” embeddings using the connectivity matrix input. Lastly, an SFFS-based algorithm was employed to select the subset of most discriminating features between the autistic and healthy controls. On the combined site data across 17 sites, we achieved the maximum 10-fold accuracy of 82% and for the individual site-wise data, based on 5-fold accuracy, our results outperformed other state of the art methods in 13 out of the total 17 site-wise comparisons.

Improving molecular design through a machine learning approach

We present a study for predicting 15 molecular properties through the combination of a quantum mechanical database, taken from the quantum chemistry QM9 database, and feed forward deep neural networks approaches. The aim of the work is to show that the combination of a priori computed ab-initio information and machine learning can support experimental work to speed up the discovery and formulation of novel compounds. The importance of this work also relies on the fact that through this computer-aided molecular design approach no approximate or heuristic contribution methods are needed for physical and thermodynamic properties information. We show that using proper hyper-parameters tuning of deep neural networks is possible, even with modest computational resources, to design the chemical structure of compounds matching target molecular properties making them feasible for practical industrial applications in diverse areas such as energy, water, food, health and transport economical sectors.

Improved curving performance of an innovative two-axle vehicle: a reasonable feedforward active steering approach

A mechatronic rail vehicle with reduced tare weight, two axles and only one level of suspension is proposed with the objective of reducing investment and maintenance costs. A wheelset to carbody connection frame in composite material will be used both as structural and as suspension element. Active control is introduced to steer the wheelsets and improve the curving performance. A feedforward control approach for active curve steering based on non-compensated lateral acceleration and curvature is proposed to overcome stability issues of a feedback approach. The feedforward approach is synthesised starting from the best achievable results of selected feedback approaches in terms of wheel energy dissipation and required actuation force. A set of 357 running cases (embracing 7 curves, 17 speeds per curve and 3 conicities) is used to design the controller. The controller is shown to perform well for conicity and track geometry variations and under the presence of track irregularities.

Theoretical and experimental investigations on modified LQ terminal control scheme of piezo-actuated compliant structures in finite time

This study focuses on the open-loop dynamic shape control to realize a rapid and smooth morphing process for a flexible plate which is actuated by a macro fiber composite patch. A finite element model of the system is established and verified for theoretical investigations. Steady-state and dynamic analyses are carried out to satisfy the desired rest-to-test morphing requirement. Spurious oscillations and large overshoots are observed while applying some common voltage signals. The results imply that the voltage profile of the actuator plays a key role in the dynamic morphing behavior of the system. Therefore, a quantitative criterion is proposed to determine the critical voltage slope for evaluating the boundary between quasi-static and dynamic processes. An optimal finite-time feedforward control approach named “modified linear quadratic (LQ) terminal controller” is proposed to optimize the voltage profiles by simultaneously considering the suppression of transient and residual vibration. The superiority of the proposed open-loop control scheme is appreciated when it is compared with the standard LQ terminal controller. An experimental setup is designed for verification. The effectiveness of the theoretical model is validated through modal analysis, static deformation, and dynamic responses. It is indicated by the experiment results that rapid and smooth dynamic shape morphing processes with negligible vibration within a short period of time can be achieved using the optimized voltage profile.

Asymmetries in the Disturbance Compensation Methods for the Stable and Unstable First Order Plants

This paper analyzes the first-order and first-order time-delayed systems control approaches, focusing mainly on unstable systems. First, it discusses asymmetries between the disturbance observer-based (DOB) control with decoupled tracking and the disturbance rejection responses, stressing applications to stable and unstable plants. The paper analyzes some DOB-based control solutions for unstable systems which do not use internal closed-loop stabilization. The novelty of the paper is thorough study accompanied with a comprehensive explanation of the differences between two distinct approaches: the transfer-function- and the closed-loop-based feedforward control approach from the point of view of control constraints. It is clearly illustrated that the main cause of instability of DOB-based approaches, applied to unstable systems, is given by their effort to impose on the system the unstable dynamics of the chosen nominal process model. It is also shown that the closed-loop stability of the DOB-based control, applied to the unstable systems, can be restored by using the supervising reference model control (RMC). The main novelty of the proposed approach is that its eliminates the mentioned stability problems while maintaining the full functionality of the chosen control structures. RMC has so far only been implemented for generating a setpoint feedforward signal. However, by generalization of this approach for disturbance rejection, the methodology of DOB design, based on nominal models, can be extended to the control of unstable systems. Without the use of disturbance reference models, the interactions of the master stabilizer with disturbance compensation cannot be eliminated. Without the internal stabilization, the stable transients can only be achieved by designing controllers based on stable models, instead of unstable ones. The existing modifications of DOB-based schemes for unstable plants, proposed in some references, are shown to lead to traditional Proportional-Integrative (PI) control, thus losing all the advantages over the PI controllers. In all the considered structures, the role of integrating models is also emphasized.

A Case Study in Multiperiod Portfolio Optimization: A Classic Problem Revisited

Conventional wisdom holds that multiperiod portfolio optimization problems are best, if not only, solved by dynamic programming. But dynamic programming suffers from the curse of dimensionality whereby optimization becomes intractable as time horizon and number of assets increase, thereby limiting its practical applications. In this paper I show for a classic multiperiod investment problem that a feed-forward, open-loop procedure, amenable to solution by conventional methods (e.g. calculus of variations) and not subject to the curse of dimensionality, generates `here and now' portfolios identical to those generated by the dynamic programming approach. The analytic results in this paper demonstrate that for this classic problem a feed forward approach is not inferior to the more common backward induction approach, suggesting that an `open-loop with recourse' process is a viable closed-loop approach for some practically useful multiperiod investment problems.

Signals Related to Color in the Early Visual Cortex.

Visual images can be described in terms of the illuminants and objects that are causal to the light reaching the eye, the retinal image, its neural representation, or how the image is perceived. Respecting the differences among these distinct levels of description can be challenging but is crucial for a clear understanding of color vision. This article approaches color by reviewing what is known about its neural representation in the early visual cortex, with a brief description of signals in the eye and the thalamus for context. The review focuses on the properties of single neurons and advances the general theme that experimental approaches based on knowledge of feedforward signals have promoted greater understanding of the neural code for color than approaches based on correlating single-unit responses with color perception. New data from area V1 illustrate the strength of the feedforward approach. Future directions for progress in color neurophysiology are discussed: techniques for improved single-neuron characterization, for investigations of neural populations and small circuits, and for the analysis of natural image statistics.

Feedforward Tuning by Fitting Iterative Learning Control Signal for Precision Motion Systems

In this article, a feedforward tuning approach by fitting iterative learning control (ILC) signal for precision motion systems is proposed. The idea is that, since ILC can achieve excellent tracking performance, ILC signal contains useful information about the plant dynamics and thus can be utilized for effective tuning of model-based feedforward controller. By this method, the high performance of ILC can be attained while the sensitivity of ILC to reference trajectory variation can be avoided. In the proposed algorithm, acceleration, jerk, and snap feedforward are tuned by fitting ILC signal through least squares first; then, the residual tracking error is compensated by an extra model-based feedforward controller, the structure of the controller is determined by Monte Carlo search and the parameters of the controller are also tuned by fitting ILC signal through least squares. Simulation and experiment on a precision motion system both validate the theoretical analysis and the proposed algorithm.

A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

In this paper, a RISE (Robust Integral of the Sign Error) controller with adaptive feedforward compensation terms based on Associative Memory Neural Network (AMNN) type B-Spline is proposed to regulate the positioning of a Delta Parallel Robot (DPR) with three degrees of freedom. Parallel Kinematic Manipulators (PKMs) are highly nonlinear systems, so the design of a suitable control scheme represents a significant challenge given that these kinds of systems are continually dealing with parametric and non-parametric uncertainties and external disturbances. The main contribution of this work is the design of an adaptive feedforward compensation term using B-Spline Neural Networks (BSNNs). They make an on-line approximation of the DPR dynamics and integrates it into the control loop. The BSNNs’ functions are bounded according to the extreme values of the desired joint space trajectories that are the BSNNs’ inputs, and their weights are on-line adjusted by gradient descend rules. In order to evaluate the effectiveness of the proposed control scheme with respect to the standard RISE controller, numerical simulations for different case studies under different scenarios were performed.