Flight test experience with adaptive control systems
Flight test experience with adaptive control systems
Thompson, M. O.↗
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Design of Low Complexity Model Reference Adaptive Controllers
Flight research experiments have demonstrated that adaptive flight controls can be an effective technology for improving aircraft safety in the event of failures or damage. However, the nonlinear, timevarying nature of adaptive algorithms continues to challenge traditional methods for the verification and validation testing of safety-critical flight control systems. Increasingly complex adaptive control theories and designs are emerging, but only make testing challenges more difficult. A potential first step toward the acceptance of adaptive flight controllers by aircraft manufacturers, operators, and certification authorities is a very simple design that operates as an augmentation to a non-adaptive baseline controller. Three such controllers were developed as part of a National Aeronautics and Space Administration flight research experiment to determine the appropriate level of complexity required to restore acceptable handling qualities to an aircraft that has suffered failures or damage. The controllers consist of the same basic design, but incorporate incrementally-increasing levels of complexity. Derivations of the controllers and their adaptive parameter update laws are presented along with details of the controllers implementations.
Hanson, Curt↗
Direct adaptive control of a PUMA 560 industrial robot
The implementation and experimental validation of a new direct adaptive control scheme on a PUMA 560 industrial robot is described. The testbed facility consists of a Unimation PUMA 560 six-jointed robot and controller, and a DEC MicroVAX II computer which hosts the Robot Control C Library software. The control algorithm is implemented on the MicroVAX which acts as a digital controller for the PUMA robot, and the Unimation controller is effectively bypassed and used merely as an I/O device to interface the MicroVAX to the joint motors. The control algorithm for each robot joint consists of an auxiliary signal generated by a constant-gain Proportional plus Integral plus Derivative (PID) controller, and an adaptive position-velocity (PD) feedback controller with adjustable gains. The adaptive independent joint controllers compensate for the inter-joint couplings and achieve accurate trajectory tracking without the need for the complex dynamic model and parameter values of the robot. Extensive experimental results on PUMA joint control are presented to confirm the feasibility of the proposed scheme, in spite of strong interactions between joint motions. Experimental results validate the capabilities of the proposed control scheme. The control scheme is extremely simple and computationally very fast for concurrent processing with high sampling rates.
Seraji, Homayoun↗
Engine identification for adaptive control
An attempt to obtain a dynamic model for a turbofan gas turbine engine for the purpose of adaptive control is described. The requirements for adaptive control indicate that a dynamic model should be identified from data sampled during engine operation. The dynamic model identified was of the form of linear differential equations with time varying coefficients. A turbine engine is, however, a highly nonlinear system, so the identified model would be valid only over a small area near the operating point, thus requiring frequent updating of the coefficients in the model. Therefore it is necessary that the identifier use only recent information to perform its function. The identifier selected minimized the square of the equation errors. Known linear systems were used to test the characteristics of the identifier. It was found that the performance was dependent on the number of data points used in the computations and upon the time interval over which the data points were obtained. Preliminary results using an engine deck for the quiet, clean, shorthaul experimental engine indicate that the identified model predicts the engine motion well when there is sufficient dynamic information, that is when the engine is in transient operation.
Leonard, R. G.↗
Augmented Adaptive Control of a Wind Turbine in the Presence of Structural Modes
Wind turbines operate in highly turbulent environments resulting in aerodynamic loads that can easily excite turbine structural modes, potentially causing component fatigue and failure. Two key technology drivers for turbine manufacturers are increasing turbine up time and reducing maintenance costs. Since the trend in wind turbine design is towards larger, more flexible turbines with lower frequency structural modes, manufacturers will want to develop methods to operate in the presence of these modes. Accurate models of the dynamic characteristics of new wind turbines are often not available due to the complexity and expense of the modeling task, making wind turbines ideally suited to adaptive control. In this paper, we develop theory for adaptive control with rejection of disturbances in the presence of modes that inhibit the controller. We use this method to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine operating in Region 3. The objective of the adaptive pitch controller is to regulate generator speed, accommodate wind gusts, and reduce the interference of certain structural modes in feedback. The control objective is accomplished by collectively pitching the turbine blades. The adaptive pitch controller for Region 3 is compared in simulations with a baseline classical Proportional Integrator (PI) collective pitch controller.
Frost, Susan A.↗
Adaptive control experiment with a large flexible structure
A large space antenna-like ground experiment structure has been developed for conducting research and validation of advanced control technology. A set of proof-of-concept adaptive control experiments for transient and initial deflection regulation with a small set of sensors and actuators were conducted. Very limited knowledge of the plant dynamics and its environment was used in the design of the adaptive controller so that performance could be demonstrated under conditions of gross underlying uncertainties. High performance has been observed under such stringent conditions. These experiments have established a baseline for future studies involving more complex hardware and environmental conditions, and utilizing additional sets of sensors and actuators.
Ih, Che-Hang Charles↗
Direct Adaptive Control of Systems with Actuator Failures: State of the Art and Continuing Challenges
In this paper, the problem of controlling systems with failures and faults is introduced, and an overview of recent work on direct adaptive control for compensation of uncertain actuator failures is presented. Actuator failures may be characterized by some unknown system inputs being stuck at some unknown (fixed or varying) values at unknown time instants, that cannot be influenced by the control signals. The key task of adaptive compensation is to design the control signals in such a manner that the remaining actuators can automatically and seamlessly take over for the failed ones, and achieve desired stability and asymptotic tracking. A certain degree of redundancy is necessary to accomplish failure compensation. The objective of adaptive control design is to effectively use the available actuation redundancy to handle failures without the knowledge of the failure patterns, parameters, and time of occurrence. This is a challenging problem because failures introduce large uncertainties in the dynamic structure of the system, in addition to parametric uncertainties and unknown disturbances. The paper addresses some theoretical issues in adaptive actuator failure compensation: actuator failure modeling, redundant actuation requirements, plant-model matching, error system dynamics, adaptation laws, and stability, tracking, and performance analysis. Adaptive control designs can be shown to effectively handle uncertain actuator failures without explicit failure detection. Some open technical challenges and research problems in this important research area are discussed.
Tao, Gang↗
Investigation of the Multiple Model Adaptive Control (MMAC) method for flight control systems
The application was investigated of control theoretic ideas to the design of flight control systems for the F-8 aircraft. The design of an adaptive control system based upon the so-called multiple model adaptive control (MMAC) method is considered. Progress is reported.
Source record↗
Adaptive Control for Linear Uncertain Systems with Unmodeled Dynamics Revisited via Optimal Control Modification
This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.
Unmodeled Dynamics↗
Robust Adaptive Control
Several concepts and results in robust adaptive control are are discussed and is organized in three parts. The first part surveys existing algorithms. Different formulations of the problem and theoretical solutions that have been suggested are reviewed here. The second part contains new results related to the role of persistent excitation in robust adaptive systems and the use of hybrid control to improve robustness. In the third part promising new areas for future research are suggested which combine different approaches currently known.
Narendra, K. S.↗
Dynamics and Adaptive Control for Stability Recovery of Damaged 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 off-nominal 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-in-the-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 Bight control approach.
Nguyen, Nhan↗
Continuation of theoretical and experimental research on digital adaptive control system, 16 September 1966 - 16 February 1967
Digital adaptive control system
DIGITAL COMPUTER↗
Adaptive control of a flexible beam using least square lattice filters
This paper presents an indirect adaptive control scheme for the control of flexible structures using recursive least square lattice filters. The identification scheme uses lattice filters which provide an on-line estimate of the number of modes, mode shapes and modal amplitudes. These modes are coupled and a transformation to decouple them in order to obtain the natural modes is presented. The decoupled modal amplitude time series are then used in an equation error identification scheme to identify the model parameters in an autoregressive moving average (ARMA) form. The control is based on modal pole placement scheme with the objective of vibration suppression. The control gains are calculated based on the identified ARMA parameters. Before using the identified parameters for control, detailed testing and validation procedures are carried out on the identified parameters. The full adaptive control scheme is demonstrated using the simulation for the 12 foot free-free beam apparatus at NASA Langley Research Center.
Sundararajan, N.↗
Case Study: Test Results of a Tool and Method for In-Flight, Adaptive Control System Verification on a NASA F-15 Flight Research Aircraft
Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.
Jacklin, Stephen A.↗
Survey of adaptive control using Liapunov design
A survey of the literature in which Liapunov's second method is used in determining the control law is presented, with emphasis placed on the model-tracking adaptive control problem. Forty references are listed. Following a brief tutorial exposition of the adaptive control problem, the techniques for treating reduction of order, disturbance and time-varying parameters, multivariable systems, identification, and adaptive observers are discussed. The method is critically evaluated, particularly with respect to possibilities for application.
Lindorff, D. P.↗
Effects of incomplete adaptation and disturbance in adaptive control.
In this paper consideration is given to the effects of disturbance and incomplete parameter adaptation on the performance of adaptive control systems in which Liapunov theory is used in deriving the control law. A design equation for the bounded error is derived. It is further shown that parameters in the adaptive controller may not converge in the presence of disturbance unless the input signal has a rich enough frequency constant. Design examples are presented.
Lindorff, D. P.↗
A fundamental aeroservoelastic study combining unsteady CFD with adaptive control
This paper describes a two-dimensional aeroservoelastic study in the time domain. The model, which is based on exact inviscid aerodynamics, correctly represents the large amplitude motions and the associated strong shock dynamics in the transonic regime. The aeroservoelastic system consists of a two degree-of-freedom airfoil with a trailing edge control surface. Using first-order actuator dynamics, a digital adaptive controller is applied to provide active flutter suppression. Comparisons between time-responses of the open-loop and closed loop systems show the ability of the trailing edge control surface to suppress non-linear transonic aeroelastic phenomena. A relation between actuator dynamics, sampling time-step and limits on the flap deflection angle to guarantee the effectiveness of the adaptive controller was demonstrated by the results generated.
Friedmann, P.↗
Flight Approach to Adaptive Control Research
The National Aeronautics and Space Administration's Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The testbed served as a full-scale vehicle to test and validate adaptive flight control research addressing technical challenges involved with reducing risk to enable safe flight in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research.
Pavlock, Kate Maureen↗