Engineering topics
Soloway, Donald
Publications and source records attributed to Soloway, Donald.
Initial Evaluations of LoC Prediction Algorithms Using the NASA Vertical Motion Simulator
Flying near the edge of the safe operating envelope is an inherently unsafe proposition. Edge of the envelope here implies that small changes or disturbances in system state or system dynamics can take the system out of the safe envelope in a short time and could result in loss-of-control events. This study evaluated approaches to predicting loss-of-control safety margins as the aircraft gets closer to the edge of the safe operating envelope. The goal of the approach is to provide the pilot aural, visual, and tactile cues focused on maintaining the pilot's control action within predicted loss-of-control boundaries. Our predictive architecture combines quantitative loss-of-control boundaries, an adaptive prediction method to estimate in real-time Markov model parameters and associated stability margins, and a real-time data-based predictive control margins estimation algorithm. The combined architecture is applied to a nonlinear transport class aircraft. Evaluations of various feedback cues using both test and commercial pilots in the NASA Ames Vertical Motion-base Simulator (VMS) were conducted in the summer of 2013. The paper presents results of this evaluation focused on effectiveness of these approaches and the cues in preventing the pilots from entering a loss-of-control event.
Neural Generalized Predictive Control: A Newton-Raphson Implementation
An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.
Results of telerobotic hand controller study using force information and rate control
To increase quantified information about the effectiveness and subjective workload of force information relayed through manipulator input control devices, a space related task was performed by eight subjects with kinesthetic force feedback and/or local force accommodation through three different input control devices (i.e., hand controllers) operating in rate control mode. Task completion time, manipulator work, and subjective responses were measured. Results indicated a difference among the hand controllers. For the Honeywell six degree-of-freedom hand controller, the overall task completion times were shortest, the amount of work exerted was the least, and was the most preferred by test subjects. Neither force accommodation with or without reflection resulted in shorter task completion times or reduced work although those conditions were better than no force information for some aspects. Comparisons of results from previous studies are discussed.
Extrapolation limitations of multilayer feedforward neural networks
The limitations of backpropagation used as a function extrapolator were investigated. Four common functions were used to investigate the network's extrapolation capability. The purpose of the experiment was to determine whether neural networks are capable of extrapolation and, if so, to determine the range for which networks can extrapolate. The authors show that neural networks cannot extrapolate and offer an explanation to support this result.
Application of structured analysis to a telerobotic system
The analysis and evaluation of a multiple arm telerobotic research and demonstration system developed by the NASA Intelligent Systems Research Laboratory (ISRL) is described. Structured analysis techniques were used to develop a detailed requirements model of an existing telerobotic testbed. Performance models generated during this process were used to further evaluate the total system. A commercial CASE tool called Teamwork was used to carry out the structured analysis and development of the functional requirements model. A structured analysis and design process using the ISRL telerobotic system as a model is described. Evaluation of this system focused on the identification of bottlenecks in this implementation. The results demonstrate that the use of structured methods and analysis tools can give useful performance information early in a design cycle. This information can be used to ensure that the proposed system meets its design requirements before it is built.
A lattice controller for telerobotic systems
A model for a lattice control structure for telerobotic systems (Critter) has been developed and prototyped. The Critter hierarchical lattice structure, node potentiation, and weighted feedback are described, and the implementation of the Critter model on a VAX architecture is addressed with regard to node processes, lattice structure, node potentiation, and network activation. The implementation environment is considered and the Critter model attributes which are desirable in a telerobotic system are discussed. Future research directions on the use of this concept for telerobotic control are examined.