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At least 235 records · Page 13

Adaptive Control of a Transport Aircraft Using Differential Thrust

The paper presents an adaptive control technique for a damaged large transport aircraft subject to unknown atmospheric disturbances such as wind gust or turbulence. It is assumed that the damage results in vertical tail loss with no rudder authority, which is replaced with a differential thrust input. The proposed technique uses the adaptive prediction based control design in conjunction with the time scale separation principle, based on the singular perturbation theory. The application of later is necessitated by the fact that the engine response to a throttle command is substantially slow that the angular rate dynamics of the aircraft. It is shown that this control technique guarantees the stability of the closed-loop system and the tracking of a given reference model. The simulation example shows the benefits of the approach.

Stepanyan, Vahram↗

Adaptive Control for Microgravity Vibration Isolation System

Most active vibration isolation systems that try to a provide quiescent acceleration environment for space science experiments have utilized linear design methods. In this paper, we address adaptive control augmentation of an existing classical controller that employs a high-gain acceleration feedback together with a low-gain position feedback to center the isolated platform. The control design feature includes parametric and dynamic uncertainties because the hardware of the isolation system is built as a payload-level isolator, and the acceleration Sensor exhibits a significant bias. A neural network is incorporated to adaptively compensate for the system uncertainties, and a high-pass filter is introduced to mitigate the effect of the measurement bias. Simulations show that the adaptive control improves the performance of the existing acceleration controller and keep the level of the isolated platform deviation to that of the existing control system.

Yang, Bong-Jun↗

Adaptive control robustness in flexible aircraft application

This paper presents the application of a model reference adaptive control (MRAC) algorithm to flexible aircraft flight control. The algorithm is an adaptive version of the Command Generator Tracking (CGT) control technique. This technique forces a dynamic system to follow a reduced-order model, allowing it to cope with the problem of unmodeled dynamics. The studies were made via simulation, using for the plant an aircraft dynamic model similar to the B1 bomber. This model is of a large aircraft with a reasonable amount of structural flexibility. In particular, flight configurations were analyzed where the influence of the flexible modes make it difficult to control the aircraft. The results indicate that the algorithm has good robustness properties vis-a-vis unmodeled dynamics and can force the flexible aircraft to follow rigid body responses.

Sanchez, E. N.↗

Some applications of direct adaptive control to large structural systems

Direct multivariable model reference adaptive control (DMMRAC) applications are considered with a representative example of a large structural system (LSS). Such applications have in the past been shown to be feasible for multivariable systems, provided that there exists a constant feedback gain matrix such that the resulting input-output transfer function is (simply) positive real.

Bar-Kana, I.↗

Adaptive control of a manipulator with a flexible link

An adaptive controller for a manipulator with one rigid link and one flexible link is presented. The performance and robustness of the controller are demonstrated by numerical simulation results. In the simulations, the manipulator moves in a gravitational field and a finite element model represents the flexible link.

Yang, Y. P.↗

Discrete-Time Model-Reference Adaptive Control

Paper discusses stability of digital model-reference adaptive control (MRAC) of robotic system or plant that operates at discrete time steps. Command-generator tracker (CGT) concept, originally proposed for continuous-time systems, is applied in discrete-time setting, enabling relaxation of some restrictive assumptions that guarantee stability of system controlled according to resulting algorithm. Likely applications include systems in which sensors and actuators not placed together.

Wen, John T.↗

Adaptive control: Stability, convergence, and robustness

The deterministic theory of adaptive control (AC) is presented in an introduction for graduate students and practicing engineers. Chapters are devoted to basic AC approaches, notation and fundamental theorems, the identification problem, model-reference AC, parameter convergence using averaging techniques, and AC robustness. Consideration is given to the use of prior information, the global stability of indirect AC schemes, multivariable AC, linearizing AC for a class of nonlinear systems, AC of linearizable minimum-phase systems, and MIMO systems decouplable by static state feedback.

Sastry, Shankar↗

A Nonlinear Dynamic Inversion Predictor-Based Model Reference Adaptive Controller for a Generic Transport Model

Presented here is a Predictor-Based Model Reference Adaptive Control (PMRAC) architecture for a generic transport aircraft. At its core, this architecture features a three-axis, non-linear, dynamic-inversion controller. Command inputs for this baseline controller are provided by pilot roll-rate, pitch-rate, and sideslip commands. This paper will first thoroughly present the baseline controller followed by a description of the PMRAC adaptive augmentation to this control system. Results are presented via a full-scale, nonlinear simulation of NASA s Generic Transport Model (GTM).

Campbell, Stefan F.↗

Direct model reference adaptive control of a flexible robotic manipulator

Quick, precise control of a flexible manipulator in a space environment is essential for future Space Station repair and satellite servicing. Numerous control algorithms have proven successful in controlling rigid manipulators wih colocated sensors and actuators; however, few have been tested on a flexible manipulator with noncolocated sensors and actuators. In this thesis, a model reference adaptive control (MRAC) scheme based on command generator tracker theory is designed for a flexible manipulator. Quicker, more precise tracking results are expected over nonadaptive control laws for this MRAC approach. Equations of motion in modal coordinates are derived for a single-link, flexible manipulator with an actuator at the pinned-end and a sensor at the free end. An MRAC is designed with the objective of controlling the torquing actuator so that the tip position follows a trajectory that is prescribed by the reference model. An appealing feature of this direct MRAC law is that it allows the reference model to have fewer states than the plant itself. Direct adaptive control also adjusts the controller parameters directly with knowledge of only the plant output and input signals.

Meldrum, D. R.↗

Decentralized adaptive control of manipulators - Theory, simulation, and experimentation

The author presents a simple decentralized adaptive-control scheme for multijoint robot manipulators based on the independent joint control concept. The control objective is to achieve accurate tracking of desired joint trajectories. The proposed control scheme does not use the complex manipulator dynamic model, and each joint is controlled simply by a PID (proportional-integral-derivative) feedback controller and a position-velocity-acceleration feedforward controller, both with adjustable gains. Simulation results are given for a two-link direct-drive manipulator under adaptive independent joint control. The results illustrate trajectory tracking under coupled dynamics and varying payload. The proposed scheme is implemented on a MicroVAX II computer for motion control of the three major joints of a PUMA 560 arm. Experimental results are presented to demonstrate that trajectory tracking is achieved despite coupled nonlinear joint dynamics.

Seraji, Homayoun↗

Neural network based adaptive control of nonlinear plants using random search optimization algorithms

This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.

Boussalis, Dhemetrios↗

Dual adaptive control: Design principles and applications

The design of an actively adaptive dual controller based on an approximation of the stochastic dynamic programming equation for a multi-step horizon is presented. A dual controller that can enhance identification of the system while controlling it at the same time is derived for multi-dimensional problems. This dual controller uses sensitivity functions of the expected future cost with respect to the parameter uncertainties. A passively adaptive cautious controller and the actively adaptive dual controller are examined. In many instances, the cautious controller is seen to turn off while the latter avoids the turn-off of the control and the slow convergence of the parameter estimates, characteristic of the cautious controller. The algorithms have been applied to a multi-variable static model which represents a simplified linear version of the relationship between the vibration output and the higher harmonic control input for a helicopter. Monte Carlo comparisons based on parametric and nonparametric statistical analysis indicate the superiority of the dual controller over the baseline controller.

Mookerjee, Purusottam↗

Design of model-reference adaptive control systems using Liapunov functions.

This paper considers the design of model-reference adaptive control systems using Liapunov functions. An adaption rule is developed analytically. This adaption rule results from the use of a Liapunov function which contains several positive semidefinite terms not included in previous references. The inclusion of these terms results in an adaption rule which is a function of the error, the plant states and the derivative and integral of these quantities. The adaption rule is applied to a simplified model of the Space Shuttle vehicle. Simulation results show that the maximum response error in the system is reduced by using this adaption rule.

Sutherlin, D. W.↗

An analysis of the multiple model adaptive control algorithm

Qualitative and quantitative aspects of the multiple model adaptive control method are detailed. The method represents a cascade of something which resembles a maximum a posteriori probability identifier (basically a bank of Kalman filters) and a bank of linear quadratic regulators. Major qualitative properties of the MMAC method are examined and principle reasons for unacceptable behavior are explored.

Greene, C. S.↗