Note on feedforward inverses for linear sequential circuits
Linear sequential circuits feedforward inverse transfer function matrix existence condition and construction procedure
Olson, R. R.↗
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Feedforward-feedback control of distributed parameter systems
Distributed parameter systems under load disturbances regulatory control by feedforward feedback and state measures
Fan, L. T.↗
A novel feedforward compensation canceling input filter-regulator interaction
The interaction between the input and the control loop of switching regulators often results in deterimental effects, such as loop instability, degradation of transient response, and audiosusceptibility, etc. The concept of pole-zero cancelation is employed to mitigate some of these detrimental effects and is implemented using a novel feedforward loop, in addition to existing feedback loops of a buck regulator. Experimental results are presented which show excellent correlation with theory.
Kelkar, S. S.↗
Design Of Feedforward Controllers For Multivariable Plants
Controllers based on simple low-order transfer functions. Mathematical criteria derived for design of feedforward controllers for class of multiple-input/multiple-output linear plants. Represented by simple low-order transfer functions, obtained without reconstruction of states of commands and disturbances. Enables plant to track command while remaining unresponsive to disturbance in steady state. Feedback controller added independently to stabilize plant or to make control system less susceptible to variations in parameters of plant.
Seraji, Homayoun↗
Output Control Using Feedforward And Cascade Controllers
Report presents theoretical study of open-loop control elements in single-input, single-output linear system. Focus on output-control (servomechanism) problem, in which objective is to find control scheme that causes output to track certain command inputs and to reject certain disturbance inputs in steady state. Report closes with brief discussion of characteristics and relative merits of feedforward, cascade, and feedback controllers and combinations thereof.
Seraji, Homayoun↗
Stochastic Feedforward Control Technique
Class of commanded trajectories modeled as stochastic process. Advanced Transport Operating Systems (ATOPS) research and development program conducted by NASA Langley Research Center aimed at developing capabilities for increases in capacities of airports, safe and accurate flight in adverse weather conditions including shear, winds, avoidance of wake vortexes, and reduced consumption of fuel. Advances in techniques for design of modern controls and increased capabilities of digital flight computers coupled with accurate guidance information from Microwave Landing System (MLS). Stochastic feedforward control technique developed within context of ATOPS program.
Halyo, Nesim↗
Minimal-Inversion Feedforward-And-Feedback Control System
Recent developments in theory of control systems support concept of minimal-inversion feedforward-and feedback control system consisting of three independently designable control subsystems. Applicable to the control of linear, time-invariant plant.
Seraji, Homayoun↗
Sensitivity of feedforward neural networks to weight errors
An analysis is made of the sensitivity of feedforward layered networks of Adaline elements (threshold logic units) to weight errors. An approximation is derived which expresses the probability of error for an output neuron of a large network (a network with many neurons per layer) as a function of the percentage change in the weights. As would be expected, the probability of error increases with the number of layers in the network and with the percentage change in the weights. The probability of error is essentially independent of the number of weights per neuron and of the number of neurons per layer, as long as these numbers are large (on the order of 100 or more).
Stevenson, Maryhelen↗
A comparative robustness evaluation of feedforward neurofilters
A comparative performance and robustness analysis is provided for feedforward neurofilters trained with back propagation to filter additive white noise. The signals used in this analysis are simulated pitch rate responses to typical pilot command inputs for a modern fighter aircraft model. Various configurations of nonlinear and linear neurofilters are trained to estimate exact signal values from input sequences of noisy sampled signal values. In this application, nonlinear neurofiltering is found to be more efficient than linear neurofiltering in removing the noise from responses of the nominal vehicle model, whereas linear neurofiltering is found to be more robust in the presence of changes in the vehicle dynamics. The possibility of enhancing neurofiltering through hybrid architectures based on linear and nonlinear neuroprocessing is therefore suggested as a way of taking advantage of the robustness of linear neurofiltering, while maintaining the nominal performance advantage of nonlinear neurofiltering.
Troudet, Terry↗
Accelerated Training for Large Feedforward Neural Networks
In this paper we introduce a new training algorithm, the scaled variable metric (SVM) method. Our approach attempts to increase the convergence rate of the modified variable metric method. It is also combined with the RBackprop algorithm, which computes the product of the matrix of second derivatives (Hessian) with an arbitrary vector. The RBackprop method allows us to avoid computationally expensive, direct line searches. In addition, it can be utilized in the new, 'predictive' updating technique of the inverse Hessian approximation. We have used directional slope testing to adjust the step size and found that this strategy works exceptionally well in conjunction with the Rbackprop algorithm. Some supplementary, but nevertheless important enhancements to the basic training scheme such as improved setting of a scaling factor for the variable metric update and computationally more efficient procedure for updating the inverse Hessian approximation are presented as well. We summarize by comparing the SVM method with four first- and second- order optimization algorithms including a very effective implementation of the Levenberg-Marquardt method. Our tests indicate promising computational speed gains of the new training technique, particularly for large feedforward networks, i.e., for problems where the training process may be the most laborious.
Stepniewski, Slawomir W.↗
The Role of the Confluence Matrix in Exponential Tracking Convergence of Overparametrized Adaptive Feedforward Systems
It is shown that persistent excitation (PE) conditions are overly stringent for ensuring exponential convergence of the tracking error in overparametrized adaptive feedforward systems.
Persistent Excitation Exponentia Convergence↗
Wavefront tilt feedforward for the formation interferometer testbad (FIT)
Separated spacecraft interferometry is a candidate architecture for several future NASA missions. The Formation Interferometer Testbed (FIT) is a ground based testbed dedicated to the validation of this key technology for a formation of two spacecraft. In separated spacecraft interferometry, the residual relative motion of the component spacecraft must be compensated for by articulation of the optical components. In this paper, the design of the FIT interferometer pointing control system is described. This control system is composed of a metrology pointing loop that maintains an optical link between the two spacecraft and two stellar pointing loops for stabilizing the stellar wavefront at both the right and left apertures of the instrument. A novel feedforward algorithm is used to decouple the metrology loop from the left side stellar loop. Experimental results from the testbed are presented that verify this approach and that fully demonstrate the performance of the algorithm.
formation flying↗
Exponential Tracking Error Convergence Bounds for Overparametrized Adaptive Feedforward Systems
Many practical adaptive feedforward systems are overparametrized and for this reason will not satisfy persistent excitation (PE) conditions. For these systems, a weaker PE condition is proposed under which it is shown that the tracking error (the error between the desired and estimated outputs) converges exponentially.
confluence↗
Mine Discrimination Using Multispectral Imagery With Feedforward Neural Networks
Simulated mine detection was performed on a polarimetric hyperspectral imaging dataset collected by using an acousto-optic tunable filter camera. A feedforward artificial neural network was programmed to recognize predefined spectral "templates." The simulation results are provided along with the preprocessing steps and window sizes leading to mine detection without false alarms.
polarimetric↗
Feedforward Control For Extreme Gusts with Remote Sensing: Pilot Study [Slides]
Our objective is to demonstrate feed-forward controller leveraging far-field sensing (i.e., whole-farm sensing as opposed to near-turbine sensing from lidar) that reduces likelihood of premature failure during extreme wind events as per DLCs.
17 WIND ENERGY↗
Feedforward, high density, programmable read only neural network based memory system
Neural network-inspired, nonvolatile, programmable associative memory using thin-film technology is demonstrated. The details of the architecture, which uses programmable resistive connection matrices in synaptic arrays and current summing and thresholding amplifiers as neurons, are described. Several synapse configurations for a high-density array of a binary connection matrix are also described. Test circuits are evaluated for operational feasibility and to demonstrate the speed of the read operation. The results are discussed to highlight the potential for a read data rate exceeding 10 megabits/sec.
Daud, Taher↗
Cascading a systolic array and a feedforward neural network for navigation and obstacle avoidance using potential fields
A technique is developed for vehicle navigation and control in the presence of obstacles. A potential function was devised that peaks at the surface of obstacles and has its minimum at the proper vehicle destination. This function is computed using a systolic array and is guaranteed not to have local minima. A feedfoward neural network is then used to control the steering of the vehicle using local potential field information. In this case, the vehicle is a trailer truck backing up. Previous work has demonstrated the capability of a neural network to control steering of such a trailer truck backing to a loading platform, but without obstacles. Now, the neural network was able to learn to navigate a trailer truck around obstacles while backing toward its destination. The network is trained in an obstacle free space to follow the negative gradient of the field, after which the network is able to control and navigate the truck to its target destination in a space of obstacles which may be stationary or movable.
Plumer, Edward S.↗
Combined trellis coding and feedforward processing for MSS applications
The idea of using a multiple (more than two) symbol observation interval to improve error probability performance is applied to differential detection of trellis coded MPSK over a mobile satellite (fading) channel. Results are obtained via computer simulation. It is shown that only a slight increase (e.g., one symbol) in the length of the observation interval will provide a significant improvement in bit error probability performance both in AWGN and fading environments.
Divsalar, D.↗