Feedforward Component Research Articles

Subcellular Dissection of a Simple Neural Circuit: Functional Domains of the Mauthner-Cell During Habituation.

Sensorimotor integration is a pivotal feature of the nervous system for ensuring a coordinated motor response to external stimuli. In essence, such neural circuits can optimize behavioral performance based on the saliency of environmental cues. In zebrafish, habituation of the acoustic startle response (ASR) is a simple behavior integrated into the startle command neurons, called the Mauthner cells. Whereas the essential neuronal components that regulate the startle response have been identified, the principles of how this regulation is integrated at the subcellular regions of the Mauthner cell, which in turn modulate the performance of the behavior, is still not well understood. Here, we reveal mechanistically distinct dynamics of excitatory inputs converging onto the lateral dendrite (LD) and axon initial segment (AIS) of the Mauthner cell by in vivo imaging glutamate release using iGluSnFR, an ultrafast glutamate sensing fluorescent reporter. We find that modulation of glutamate release is dependent on NMDA receptor activity exclusively at the AIS, which is responsible for setting the sensitivity of the startle reflex and inducing a depression of synaptic activity during habituation. In contrast, glutamate-release at the LD is not regulated by NMDA receptors and serves as a baseline component of Mauthner cell activation. Finally, using in vivo calcium imaging at the feed-forward interneuron population component of the startle circuit, we reveal that these cells indeed play pivotal roles in both setting the startle threshold and habituation by modulating the AIS of the Mauthner cell. These results indicate that a command neuron may have several functionally distinct regions to regulate complex aspects of behavior.

Magnetic Control of a Flexible Needle in Neurosurgery.

Minimally invasive neurosurgery does not require large incisions and openings in the skull to access the desired brain region, which often results in a faster recovery with fewer complications than traditional open neurosurgery. For disorders treated by the implantation of neurostimulators and thermocoagulation probes, current procedures incorporate a straight rigid needle, which restricts surgical trajectories and limits the number of possible targets and degrees of freedom at the respective target. A steerable needle with a flexible body could overcome these limitations. In this paper, we present a flexible needle steering system with magnetic and fluoroscopic guidance for neurosurgical procedures. A permanent magnet at the proximal end of a flexible needle is steered by an external magnetic field, and the resultant tip-deflection angle bends the flexible body like a bevel-tip needle. We implemented a kinematic model for the magnetic needle derived from a nonholonomic bicycle model and a closed-loop control strategy with feed-forward and feed-back components using a chained-form transformation. The proposed needle steering method was investigated through in vitro and ex vivo experiments.

Trajectory/Path-Following Controller Based on Nonlinear Jerk-Level Error Dynamics

This study proposes a novel, nonlinear trajectory/path-following controller based on jerk-level error dynamics. Therefore, at first the nonlinear acceleration-based kinematic equations of motion of a dynamic system are differentiated with respect to time to obtain a representation connecting the translation jerk with the (specific) force derivative. Furthermore, the path deviation, i.e., the difference between the planned and the actual path, is formulated as nonlinear error dynamics based on the jerk error. Combining the derived equations of motion with the nonlinear error dynamics as well as employing nonlinear dynamic inversion, a control law can be derived that provides force derivative commands, which may be commanded to an inner loop for trajectory control. This command ensures an increased smoothness and faster reaction time compared to traditional approaches based on a force directly. Furthermore, the nonlinear parts in the error dynamic are feedforward components that improve the general performance due to their physical connection with the real dynamics. The validity and performance of the proposed trajectory/path-following controller are shown in an aircraft-related application example.

Networked model predictive control using a wavelet neural network

This study proposes using a wavelet neural network (WNN) with a feedforward component and a model predictive controller (MPC) for online nonlinear system identification and control over a communication network. The WNN performs the online identification of the nonlinear system. The MPC uses the model to predict the future outputs of the system over an extended prediction horizon and calculates the optimal future inputs by minimizing a controller cost function. A computationally efficient formulation for the controller is presented to reduce the computational complexity of the MPC for online implementation and Lyapunov theory is used to prove the stability of the MPC. The methodology is applied to the online identification and control of an unmanned autonomous vehicle. Simulation results show that the MPC with an extended prediction horizon can effectively control the system in the presence of fixed or random network delay.

Multivariate feed forward process control and optimization of an industrial, granulation based tablet manufacturing line using historical data.

The purpose of this work was to understand the variability in disintegration time and tableting yield of high drug load (>60%) tablets prepared by batch-wise high shear wet granulation. The novelty of the study is the use of multivariate methods (Batch Evolution Models - BEMs and Batch Level Models - BLMs) to enhance process control, with a feed forward component, using prediction models built from a historical dataset acquired for 95 industrial scale batches. Time dependent process variables and significant influences on investigated parameters were identified. Prediction of output from input was tested with Partial Least Squares (PLS) and Artificial Neural Network (ANN) modeling. A reliable prediction ability was achieved for granulation water amount (±2 kg in a 16-31kg range), tableting speed (±5000 tablets/h in a 23,000-72,500 tabl./h range) and disintegration time of cores (±100s; in a 250-900 s range). Offsets from the optimal process evolution and certain raw material properties were correlated with differences observed in the output variables. Improvement options were identified for 80% of the batches with high disintegration time. Hence, the trained models can be applied for systematic process improvement, enabling feed forward control.

CC‐ROGI‐FLL based control for grid‐tied photovoltaic system at abnormal grid conditions

This study presents a single stage grid-interfaced solar photovoltaic (SPV) system using a multifunctional complex coefficient reduced ordered generalised integrator (CC-ROGI) based frequency-locked loop (FLL) control. The main contributions of this study, include (i) the extrication of fundamental active components from load currents using CC-ROGI-FLLbased control to generate the reference grid currents for voltage source converter (VSC) switching, (ii) VSC works as a distribution static compensator, which performs multi-functions like harmonics mitigation, reactive power compensation, unity power factor operation and grid currents balancing, (iii) this CC-ROGI-FLL also extricates the fundamental grid voltages component even under grid voltage harmonic distortion and grid voltages unbalancing. Moreover, SPV power feed-forward component is assimilated in this control to reduce the oscillations in grid currents under variable irradiance. Simulated and experimental results show satisfactory behaviour of the system at different conditions like load unbalancing, variable solar irradiance and abnormal grid conditions. The comparative performance of CC-ROGI-FLL control with other FLL and phaselocked loop controls are shown in this study. This CC-ROGI-FLL control has manifested a good steady state performance and dynamic response. The total harmonic distortions of grid currents are found well within the limits of the IEEE-519 standard, even under abnormal grid conditions.

Three-phase grid-tied photovoltaic system with an adaptive current control scheme in active power filter

ABSTRACT In this article, an adaptive normalized least mean fourth (NLMF) control scheme is employed to generate the anti-harmonic currents of photovoltaic (PV) integrated with grid system for harmonic elimination and power factor (PF) correction (PFC). In addition, a fuzzy tuned PID-based adaptive dc voltage regulator is integrated in the proposed scheme to regulate the reference voltage of the dc bus. The proposed FPID-based NLMF control scheme attains the features of power quality (PQ) related issues elimination such as PFC and harmonics elimination with an extraction of photovoltaic (PV) generated maximum power through the grid interfacing converter (VSC) at a unity PF. Moreover, the PV system’s feed-forward (FF) component is included in the control strategy to enrich the aggressive behavior of the system. The NLMF current controller is employed to measure the three-phase calculated currents by extracting the fundamental weight constituents of the load currents for harmonic mitigation in the presence of point of common coupling (PCC). The proposed control topology is developed on MATLAB/SIMULINK software platform under different load and grid transient conditions. A dSPACE based real-time laboratory platform is developed to justify the proposed scheme under different conditions such as steady-state, load dynamic, removed and unbalanced, grid voltage distorted, unbalanced, swell and sag conditions. The presented control scenario’s results are obtained satisfactory and the grid current’s THD are in the IEEE-519 regular limits.

A New PV-Open-UPQC Configuration for Voltage Sensitive Loads Utilizing Novel Adaptive Controllers

In this article, a novel topology for photovoltaic (PV) fed open unified power quality conditioner has been proposed focusing on the requirement of voltage sensitive loads. It has incorporated the advantages of open unified power quality conditioner (UPQC) and grid connected PV systems by providing a possible solution towards the demand of different power quality levels at end user. Increasing the system robustness for various voltage and current distortions, adaptive controllers have been implemented for series and shunt compensators of UPQC. Adaptive notch filter based improved Syncronous Reference Frame (SRF) scheme has been utilized for the control of series compensator. An adaptive logarithmic absolute algorithm has been implemented for shunt part of proposed system. The utilization of moving average filter (MAF) instead of low pass filter (LPF) for dc bus voltage regulation has neglected the chance of high as well as low frequency ripples. A feed forward component has been added to the shunt controller to regulate the grid current at constant level in presence of grid disturbances. The proposed controllers are verified on a developed prototype of proposed system in the laboratory. The system performance is found satisfactory under various grid conditions like voltage sags, voltage swells, voltage unbalance, voltage harmonics, sudden load addition and removal, and variation in solar irradiation.

Design, Modeling, and Control of a Compact SMA-Actuated MR-Conditional Steerable Neurosurgical Robot

The letter presents a compact shape memory alloy (SMA)-actuated magnetic resonance (MR)-conditional neurosurgical robotic system. It consists of a 2-degree of freedom (DoF) cable-driven steerable end effector, an antagonistic SMA springs-based actuation module, and a quick-connect module, packaged into a single integrated device measuring 305 mm length × 76 mm diameter. The system is also highly adaptable, such that it could operate a cable-driven end effector up to a maximum of 4-DoFs and its length can be easily modified due to the acrylic plate-based modular construction. In addition to the kinematics of the robotic end effector and the SMA constitutive model, we also present the antagonistic SMA springs model under the known tension and cable displacement from the robotic end effector. We performed extensive characterization experiments to obtain SMA model parameters and integrated a feedforward component in our controller to achieve improved tracking of a sinusoidal reference up to 80° bending angle amplitude and 100 s period. Lastly, proof-of-concept robot demonstrations were performed in a gel phantom and in the MRI that confirmed the robot motion capability in the brain and MRI compatibility of the robot.

Low Voltage Ride-Through Capability of a Novel Grid Connected Inverter Suitable for Transformer-Less Solar PV–Grid Interface

In order to face the challenges due to the large-scale integration of photovoltaic (PV) inverters on the distribution side, the grid-connected PV inverters are expected to provide certain ancillary services. These ancillary services include reactive power support, low voltage ride through (LVRT), and harmonic compensation, just to name a few. In this article, the LVRT capability of a $\ddot{\text{C}}$ uk-derived novel inverter, 6sw- $\ddot{\text{C}}$ uk derived transformer-less inverter (6sw-CDTI), suitable for transformer-less grid–PV interface, is explored. The transformer-less inverter, 6sw-CDTI, considered in this work can provide the desired ancillary services more efficiently and at a lesser cost as compared to the conventional inverters. Reactive power support to the grid can be provided without distorting the injected current by 6sw-CDTI with no dc injection due to symmetrical operation in both half cycles. Besides this, the leakage current elimination is not affected because the topological structure is not altered. Experimental results obtained from a 200-W laboratory prototype illustrate how additional control features can be integrated in the inverter control scheme for LVRT. Single-phase instantaneous p-q theory is used for inverter control, and a feed-forward component is added in the control scheme for improved dynamic response.

Sparse identification of contrast gain control in the fruit fly photoreceptor and amacrine cell layer

The fruit fly’s natural visual environment is often characterized by light intensities ranging across several orders of magnitude and by rapidly varying contrast across space and time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process visual scenes and provide the resulting signal to downstream targets. Here, we model the first step of visual processing in the photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling the computation taking place in the photoreceptor-amacrine cell layer. The DNP explicitly models the photoreceptor feedforward and temporal feedback processing paths and the spatio-temporal feedback path of the amacrine cells. We then formally characterize the contrast gain control of the DNP and provide sparse identification algorithms that can efficiently identify each the feedforward and feedback DNP components. The algorithms presented here are the first demonstration of tractable and robust identification of the components of a divisive normalization processor. The sparse identification algorithms can be readily employed in experimental settings, and their effectiveness is demonstrated with several examples.

Near-optimal neural-network robot control with adaptive gravity compensation

Adaptive nonlinear optimal control methods, as proposed in the literature, give rise to some questions around practical implementation in robotics, especially how to find a solution in a reasonable time and how to deal with gravity. This paper proposes a method to solve these problems by using a neural network with local basis-function domains, specifically the Cerebellar Model Articulation Controller (CMAC). The algorithm uses the local domains in order to keep track of the value of local cost-functionals. In this way, it can freeze the learning of the network’s weights in a feedforward-like component in the CMAC when the bias has been overcome identified by using an error-based cost-functional e.g. automatic gravity compensation in a robot. After the feedforward component has been established, the algorithm then starts to learn another set of weights which contribute to feedback-like terms in the CMAC and these weights get frozen when they no longer reduce a cost-functional that includes additional control effort e.g. in a robot the control effort beyond that needed to compensate for gravity is penalized. Lyapunov methods guarantee uniformly ultimately bounded signals and ensure weight drift and bursting do not occur. One advantage is that the training time for finding a near-optimal control does not increase over previous neural-adaptive methods. Another advantage is that penalizing the control effort in a search for optimization does result in any steady-state error due to gravity. Simulations show that the proposed method significantly outperforms a standard adaptive-CMAC control using e-modification, without increasing control effort or training time. An experimental flexible-joint robot verifies that the adaptive method significantly outperforms a linear quadratic regulator.

Model reference adaptive tracking control for hydraulic servo systems with nonlinear neural-networks

It is well known that hydraulic systems typically suffer from heavy disturbances including parametric uncertainties and unknown disturbances. In order to attain high performance tracking control, this paper proposes a composite design of nonlinear neural-networks (NN) and continuous robust integral of the sign of the error (RISE) feedback controller. The control development incorporates a NN feedforward component to have a compensation for unknown state-dependent disturbances and to further improve the accuracy of feedforward compensation, meanwhile input parameter is updated online. To achieve asymptotic stability, a novel RISE term with NN-based feedforward component is developed for the first time to enable the incorporation of model reference adaptive control structure where acceleration signal is not employed. The proposed controller guarantees controlled hydraulic system a semi-global asymptotic stability. For the experimental results, the prescribed transient performance is tested under rectangular trajectory and the steady state performance is tested under sinusoidal trajectory.

6-DOF formation keeping control for an invariant three-craft triangular electromagnetic formation

Spacecraft relative motion with inter-craft electromagnetic force has distinct advantages, and its invariant shapes that are convenient for formation keeping ensure some potential significant applications. However, the electromagnetic actuators affect both the relative trajectory and attitude motion, complicating related researches on issues of invariant shape design and formation control. In this paper, the formation keeping problem for an invariant three-craft triangular electromagnetic formation is investigated on the basis of a 6-DOF full nonlinear dynamic model. Moreover, a combined control scheme consisting of feed-forward and feed-back control components is proposed to handle the high nonlinearity, strong coupling, model uncertainties and external disturbances. The feed-forward component is obtained through desired invariant shape design which is complicated by the coupling and superposition effects of any two distinct magnetic dipoles, and the feed-back component is developed with a combination of linear feed-back controller by the LQR method and active disturbance rejection by the extended state observer. Finally, the numerical simulation is presented to verify the feasibility and validity of the proposed 6-DOF combined control scheme.

Formation control for a string of interconnected second-order systems via target feedback

This paper investigates the formation control of interconnected second-order systems. Each agent is assumed to be capable of measuring its own absolute velocity and the relative positions with respect to its neighboring agents, whereas the target formation is described by absolute positions of all agents in a global coordinate. For such formation control problems, no distributed control policy was reported in existing literature. This paper focuses on the string connection structure of the agents and proposes a distributed control policy that takes the form of purely state feedback without incorporating any feed-forward component. The closed-loop system equation is characterized by an oscillation matrix whose entries are the feedback controller gains. Formation control is accomplished by formulating the agents’ target positions as feedback controller gains. Moreover, it is shown that for agent models described by double integrators, each of the agents located at the two endpoints of the string structure should know its own absolute position. For a class of agent models where each agent’s acceleration depends on its own position, the control laws do not need to use the absolute position. For both system models, the target formations that are asymptotically reachable by the proposed control laws are specified explicitly. Numerical simulations have been conducted to illustrate the effectiveness of the theoretical results.

A Spike Time-Dependent Online Learning Algorithm Derived From Biological Olfaction.

We have developed a spiking neural network (SNN) algorithm for signal restoration and identification based on principles extracted from the mammalian olfactory system and broadly applicable to input from arbitrary sensor arrays. For interpretability and development purposes, we here examine the properties of its initial feedforward projection. Like the full algorithm, this feedforward component is fully spike timing-based, and utilizes online learning based on local synaptic rules such as spike timing-dependent plasticity (STDP). Using an intermediate metric to assess the properties of this initial projection, the feedforward network exhibits high classification performance after few-shot learning without catastrophic forgetting, and includes a none of the above outcome to reflect classifier confidence. We demonstrate online learning performance using a publicly available machine olfaction dataset with challenges including relatively small training sets, variable stimulus concentrations, and 3 years of sensor drift.

Design of State Feedback Current Controller for Fast Synchronization of DFIG in Wind Power Generation Systems

Doubly-fed induction generators (DFIGs) are widely used in wind energy conversion systems. The dynamic features of DFIGs make it important to focus on designing high-performance control schemes. However, the dynamic characteristics of such generators depend on nonlinear parameters, such as stator flux, stator current, and rotor current, which increase the overall system complexity. In addition, the DFIG Wind Energy Conversion Systems (WECSs) size is growing beyond 7 MW, which increases stress on both the mechanical drive train and the power circuits during connection to the grid. Such stress and dynamic features cannot be neglected. Therefore, robust controllers must be implemented which have the ability to support the dynamic frequencies of wind energy to ensure system stability in grid connection mode and during subsequent use. Conventional vector control configurations that use proportional-integral controllers have various drawbacks, such as parameter tuning difficulties, mediocre dynamic performance, and reduced robustness. In this study, we focused on improving DFIG synchronization to the grid by applying state feedback current controllers with a feedforward component to smooth the connection to the grid, as well as to improve the steady-state and transient characteristics of the controller. State feedback controllers are proposed to replace the proportional-integral controllers on both the rotor and grid sides. The proposed controller is designed using a multivariable system and feedforward control for input reference and incorporating disturbances into the control equations for fast synchronization and transient responses. To demonstrate the advantages of this controller, experimental studies are presented for both the transient and steady states.

Current controller design for DFIG‐based wind turbines using state feedback control

Doubly-fed induction generators (DFIGs) are widely used in wind energy conversion systems. The dynamic features of DFIGs make it important to focus on designing high-performance control schemes. However, the dynamic characteristics of such generators depend on nonlinear parameters, such as stator flux, stator current, and rotor current, which increase overall system complexity. Therefore, robust controllers must be implemented with the ability to support the dynamic frequencies of wind energy to ensure system stability. Conventional vector control configurations that use proportional-integral controllers have various drawbacks, such as parameter tuning difficulties, mediocre dynamic performance, and reduced robustness. In this study, improving DFIG dynamics by applying a multivariable state feedback current controller with a feedforward component to improve the steady-state and transient characteristics of the controller has been introduced and discussed. Multivariable state feedback controllers are proposed to replace the proportional-integral controllers on both the rotor and grid sides. The proposed controller is designed using a multivariable system and feedforward control for input reference and incorporating disturbances into the control equations for fast transient responses. To demonstrate the advantages of this controller, experimental studies are presented for both the transient and steady states.

Force ripple compensation in a PMLSM position servo system using periodic adaptive learning control

Force ripple deteriorates the performance of permanent magnet linear synchronous motor (PMLSM) servo systems. Using a model reference adaptive control and periodic adaptive learning control (MRAC–PALC) algorithm, this paper presents a novel compensation method to eliminate the influence of force ripple on the system performance of a position servo system under repetitive motion tasks. The key idea of the proposed method is to utilize the periodic characteristics of both force ripple and system motion. The controller consists of four components: a PD component, a feedforward component, a velocity feedback component and an MRAC–PALC compensator. The first three components are designed in a conventional way. The compensator is divided into two parts: in the 0th-iteration, an MRAC algorithm is employed to obtain the initial information, and in the ith-iteration (i≥ 1), a PALC algorithm is used to learn from the information obtained in the previous period and update the controller parameters for estimating force ripple. Moreover, a theoretical stability analysis is given via Lyapunov stability theorem, and some comparative results are provided through simulations and experiments.

Feedforward and Feedback Control of a Pharmaceutical Coating Process.

This work demonstrates the use of a combination of feedforward and feedback loops to control the controlled release coating of theophylline granules. Feedforward models are based on the size distribution of incoming granules and are used to set values for the airflow in the fluid bed processor and the target coat weight to be applied to the granules. The target coat weight of the granules is controlled by a feedback loop using NIR spectroscopy to monitor the progress of the process. By combining feedforward and feedback loops, significant variation in the size distributions and ambient conditions were accommodated in the fluid bed coating of the granules and a desired dissolution profile was achieved. The feedforward component of the control system was specifically tested by comparing the performance of the control system with and without this element by Monte Carlo simulation.