Kalmanje Krishnakumar

Flight Dynamics and Hybrid Adaptive Control of Damaged Aircraft

This paper presents a recent study to investigate flight dynamics and adaptive control methods for stability and control recovery of a damaged generic transport aircraft. Aerodynamic modeling of damage effects is performed using an aerodynamic code to assess changes in the stability and control derivatives of the damaged aircraft. Flight dynamics for a general aircraft are developed to account for changes in aerodynamics, mass properties, and the center of gravity that can compromise the stability of the damaged aircraft An iterative trim analysis is developed to compute incremental trim states. A neural network hybrid direct-indirect adaptive flight control is developed for the stability augmentation control of the damaged aircraft. The proposed method performs an online estimation of damaged plant dynamics to improve the command tracking performance in conjunction with a direct adaptive controller. The plant estimation is based on two approaches: 1) an indirect adaptive law derived from the Lyapunov stability theory to ensure that the tracking error is bounded, and 2) a recursive least-squares method that minimizes the modeling error. Simulations show that the hybrid adaptive controller can provide a significant improvement in the tracking performance over a direct adaptive controller working alone.

Dynamics and Adaptive Control for Stability Recovery of Damaged Asymmetric Aircraft

This paper presents a recent study of a damaged generic transport model as part of a NASA research project to investigate adaptive control methods for stability recovery of damaged aircraft operating in offnominal flight conditions under damage and or failures. Aerodynamic modeling of damage effects is performed using an aerodynamic code to assess changes in the stability and control derivatives of a generic transport aircraft. Certain types of damage such as damage to one of the wings or horizontal stabilizers can cause the aircraft to become asymmetric, thus resulting in a coupling between the longitudinal and lateral motions. Flight dynamics for a general asymmetric aircraft is derived to account for changes in the center of gravity that can compromise the stability of the damaged aircraft. An iterative trim analysis for the translational motion is developed to refine the trim procedure by accounting for the effects of the control surface deflection. A hybrid direct-indirect neural network, adaptive flight control is proposed as an adaptive law for stabilizing the rotational motion of the damaged aircraft. The indirect adaptation is designed to estimate the plant dynamics of the damaged aircraft in conjunction with the direct adaptation that computes the control augmentation. Two approaches are presented: 1) an adaptive law derived from the Lyapunov stability theory to ensure that the signals are bounded, and 2) a recursive least-square method for parameter identification. A hardware-inthe-loop simulation is conducted and demonstrates the effectiveness of the direct neural network adaptive flight control in the stability recovery of the damaged aircraft. A preliminary simulation of the hybrid adaptive flight control has been performed and initial data have shown the effectiveness of the proposed hybrid approach. Future work will include further investigations and high-fidelity simulations of the proposed hybrid adaptive flight control approach.

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.

MRAC Revisited: Guaranteed performance with reference model modification

This paper presents modification of the conventional model reference adaptive control (MRAC) architecture in order to achieve guaranteed transient performance both in the output and input signals of an uncertain system. The proposed modification is based on the tracking error feedback to the reference model. It is shown that approach guarantees tracking of a given command and the ideal control signal (one that would be designed if the system were known) not only asymptotically but also in transient by a proper selection of the error feedback gain. The method prevents generation of high frequency oscillations that are unavoidable in conventional MRAC systems for large adaptation rates. The provided design guideline makes it possible to track a reference command of any magnitude form any initial position without re-tuning. The benefits of the method are demonstrated in simulations.

Intelligent engine control using an adaptive critic

Neural networks (NNs) have been successfully used for implementing control architectures for different applications. In this paper, we examine NN augmented intelligent control of a turbo-fan engine toward the goal of minimizing a performance measure on-line. This architecture utilizes an adaptive critic to estimate the engine performance, which is then used to train an NN demand generator for minimizing the performance measure. The present architecture is implemented on a nonlinear model that was provided by General Electric. The model simulates a changed engine by changing the flow and efficiency scalars of the various components of the engine. Results of using the adaptive critic-based performance seeking control architecture show excellent improvement in performance over time.

M-MRAC for nonlinear systems with bounded disturbances

This paper presents design and performance analysis of a modified reference model MRAC (M-MRAC) architecture for a class of multi-input multi-output uncertain nonlinear systems in the presence of bounded disturbances. M-MRAC incorporates an error feedback in the reference model definition, which allows for fast adaptation without generating high frequency oscillations in the control signal, which closely follows the certainty equivalent control signal. The benefits of the method are demonstrated via a simulation example of an aircrafts wing rock motion.

Stability and Performance Metrics for Adaptive Flight Control

This paper addresses the problem of verifying adaptive control techniques for enabling safe flight in the presence of adverse conditions. Since the adaptive systems are non-linear by design, the existing control verification metrics are not applicable to adaptive controllers. Moreover, these systems are in general highly uncertain. Hence, the systems characteristics cannot be evaluated by relying on the available dynamical models. This necessitates the development of control verification metrics based on the systems input-output information. For this point of view, a set of metrics is introduced that compares the uncertain aircrafts input-output behavior under the action of an adaptive controller to that of a closed-loop linear reference model to be followed by the aircraft. This reference model is constructed for each specific maneuver using the exact aerodynamic and mass properties of the aircraft to meet the stability and performance requirements commonly accepted in flight control. The proposed metrics are unified in the sense that they are model independent and not restricted to any specific adaptive control methods. As an example, we present simulation results for a wing damaged generic transport aircraft with several existing adaptive controllers.

Implementation and Evaluation of Multiple Adaptive Control Technologies for a Generic Transport Aircraft Simulation

Presented here is the evaluation of multiple adaptive control technologies for a generic transport aircraft simulation. For this study, seven model reference adaptive control (MRAC) based technologies were considered. Each technology was integrated into an identical dynamic-inversion control architecture and tuned using a methodology based on metrics and specific design requirements. Simulation tests were then performed to evaluate each technology’s sensitivity to time-delay, flight condition, model uncertainty, and artificially induced cross-coupling. The resulting robustness and performance characteristics were used to identify potential strengths, weaknesses, and integration challenges of the individual adaptive control technologies. I. Introduction HE Integrated Resilient Aircraft Control (IRAC) project is a part of the Aviation Safety Program under the Aeronautics Research Mission Directorate (ARMD) at NASA. A key focus of this project is to research the use of adaptive control technologies as a risk-mitigating tool for off-nominal aircraft flight. In a traditional gainscheduled design approach, the flight controller is designed by treating the aircraft’s flight envelope as a discrete space. Controls engineers then use traditional linear control techniques to shape the handling qualities of the aircraft at each of these discrete locations. In an off-nominal scenario, this design approach may breakdown as a result of the

Adaptive Control with Reference Model Modification

This paper presents a modification of the conventional model reference adaptive control (MRAC) architecture in order to improve transient performance of the input and output signals of uncertain systems. A simple modification of the reference model is proposed by feeding back the tracking error signal. It is shown that the proposed approach guarantees tracking of the given reference command and the reference control signal (one that would be designed if the system were known) not only asymptotically but also in transient. Moreover, it prevents generation of high frequency oscillations, which are unavoidable in conventional MRAC systems for large adaptation rates. The provided design guideline makes it possible to track a reference commands of any magnitude from any initial position without re-tuning. The benefits of the method are demonstrated with a simulation example

Estimating Loss-of-Control: a Data-Based Predictive Control Approach

Loss-of-control is a major contributor to aircraft fatalities. Recent work has been done to develop quantitative criteria for determining loss-of-control from accident time history data. This work proposes an approach to estimating boundaries on control actions to provide information to pilots and/or control systems to assist in avoiding loss-of-control scenarios. Data-based predictive control theory is used to develop an algorithm that finds the minimum control input that will result in the aircraft exceeding a safe operating envelope at various minimum time estimates. The calculated minimum control inputs become a boundary of a set of safe control inputs. With this information, a pilot could change flying strategy or an autonomous system could schedule controller gains to prevent the vehicle from exceeding the envelope.