Validation and Verification of Aircraft Control Software for Control Improvement

Validation and Verification are important processes used to ensure software safety and reliability. The Cooper-Harper Aircraft Handling Qualities Rating is one of the techniques developed and used by NASA researchers to verify and validate control systems for aircrafts. Using the Validation and Verification result of controller software to improve controller\u27s performance will be one of the main objectives of this process. Real user feedback will be used to tune PI controller in order for it to perform better. The Cooper-Harper Aircraft Handling Qualities Rating can be used to justify the performance of the improved system

Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA

This paper presents a statistical study on a neuro-inspired spike-based implementation of the Vector-Integration-To-End-Point motor controller (SVITE) and compares its deterministic neuron-model stream of spikes with a proposed modification that converts the model, and thus the controller, in a Poisson like spike stream distribution. A set of hardware pseudo-random numbers generators, based on a Linear Feedback Shift Register (LFSR), have been introduced in the neuron-model so that they reach a closer biological neuron behavior. To validate the new neuron-model behavior a comparison between the Inter-Spikes-Interval empirical data and the Exponential and Gamma distributions has been carried out using the Kolmogorov–Smirnoff test. An in-hardware validation of the controller has been performed in a Spartan6 FPGA to drive directly with spikes DC motors from robotics to study the behavior and viability of the modified controller with random components. The results show that the original deterministic spikes distribution of the controller blocks can be swapped with Poisson distributions using 30-bit LFSRs. The comparative between the usable controlling signals such as the trajectory and the speed profile using a deterministic and the new controller show a standard deviation of 11.53 spikes/s and 3.86 spikes/s respectively. These rates do not affect our system because, within Pulse Frequency Modulation, in order to drive the motors, time length can be fixed to spread the spikes. Tuning this value, the slow rates could be filtered by the motor. Therefore, this SVITE neuro-inspired controller can be integrated within complex neuromorphic architectures with Poisson-like neurons

The multiple-function multi-input/multi-output digital controller system for the AFW wind-tunnel model

A real time multiple-function digital controller system was developed for the Active Flexible Wing (AFW) Program. The digital controller system (DCS) allowed simultaneous execution of two control laws: flutter suppression and either roll trim or a rolling maneuver load control. The DCS operated within, but independently of, a slower host operating system environment, at regulated speeds up to 200 Hz. It also coordinated the acquisition, storage, and transfer of data for near real time controller performance evaluation and both open- and closed-loop plant estimation. It synchronized the operation of four different processing units, allowing flexibility in the number, form, functionality, and order of control laws, and variability in the selection of the sensors and actuators employed. Most importantly, the DCS allowed for the successful demonstration of active flutter suppression to conditions approximately 26 percent (in dynamic pressure) above the open-loop boundary in cases when the model was fixed in roll and up to 23 percent when it was free to roll. Aggressive roll maneuvers with load control were achieved above the flutter boundary. The purpose here is to present the development, validation, and wind tunnel testing of this multiple-function digital controller system

MIMO First and Second Order Discrete Sliding Mode Controls of Uncertain Linear Systems under Implementation Imprecisions

The performance of a conventional model-based controller significantly depends on the accuracy of the modeled dynamics. The model of a plant's dynamics is subjected to errors in estimating the numerical values of the physical parameters, and variations over operating environment conditions and time. These errors and variations in the parameters of a model are the major sources of uncertainty within the controller structure. Digital implementation of controller software on an actual electronic control unit (ECU) introduces another layer of uncertainty at the controller inputs/outputs. The implementation uncertainties are mostly due to data sampling and quantization via the analog-to-digital conversion (ADC) unit. The failure to address the model and ADC uncertainties during the early stages of a controller design cycle results in a costly and time consuming verification and validation (V&V) process. In this paper, new formulations of the first and second order discrete sliding mode controllers (DSMC) are presented for a general class of uncertain linear systems. The knowledge of the ADC imprecisions is incorporated into the proposed DSMCs via an online ADC uncertainty prediction mechanism to improve the controller robustness characteristics. Moreover, the DSMCs are equipped with adaptation laws to remove two different types of modeling uncertainties (multiplicative and additive) from the parameters of the linear system model. The proposed adaptive DSMCs are evaluated on a DC motor speed control problem in real-time using a processor-in-the-loop (PIL) setup with an actual ECU. The results show that the proposed SISO and MIMO second order DSMCs improve the conventional SISO first order DSMC tracking performance by 69% and 84%, respectively. Moreover, the proposed adaptation mechanism is able to remove the uncertainties in the model by up to 90%.Comment: 10 pages, 11 figures, ASME 2017 Dynamic Systems and Control Conferenc

Flutter suppression for the active flexible wing: Control system design and experimental validation AIAA-92-2097

The synthesis and experimental validation of a control law for13; an actiqe flutter suppression system for the Active Flexible13; Wing wind-tunnel model is presenied. The design was13; accomplished with traditional root locus and Nyquist methods13; using interactive computer graphics tools and with extensive use13; of simulation-based analysis. The design approach relied on a13; fundamental understanding of the flutter mechanism to13; formulate a simple control law structure. Experimentally, the13; flutter suppression controller succeeded in simultaneous13; suppression of two flutter modes, significantly increasing the13; flutter dynamic pressure despite errors in the design model. The13; flutter suppression controller was also successfully operated in13; combination with a rolling maneuver controller to perform13; flutter suppression during rapid rolling maneuvers

Adaptive control of large space structures using recursive lattice filters

The use of recursive lattice filters for identification and adaptive control of large space structures is studied. Lattice filters were used to identify the structural dynamics model of the flexible structures. This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures is control engaged. This type of validation scheme prevents instability when the overall loop is closed. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods. The method uses the Linear Quadratic Guassian/Loop Transfer Recovery (LQG/LTR) approach to ensure stability against unmodeled higher frequency modes and achieves the desired performance