Browse Topic: Adaptive control
Generalized Predictive Control (GPC) is an advanced form of an adaptive control algorithm that uses experimentally acquired data to determine the input-output relationship of complex systems through a process called system identification. GPC has historically been employed for stability augmentation and vibration reduction of dynamically-scaled tiltrotor aircraft wind-tunnel models since the complex nature of these dynamic systems does not lend itself well to traditional control approaches. The present research expands upon previous analytical and experimental work with wind-tunnel experiments that utilize improved GPC techniques. These techniques improved controller robustness such that a working controller was stable across a multitude of model configurations and wind-tunnel conditions and successfully suppressed vibration and vehicle flutter. Advanced GPC (AGPC) enables self-adaptation of a traditional GPC control law. AGPC was also investigated during the present research but was
The development of an adaptive pilot model for rotorcraft tracking tasks is useful to understand and replicate human pilot behavior under varying vehicle dynamics and environmental conditions. This paper presents a Model-Reference Adaptive Control (MRAC)-based pilot model designed to emulate the adaptability of human pilots during attitude and position tracking tasks. The model leverages wavelet analysis to characterize pilot behavior and employs a closed-loop system identification approach to derive baseline pilot parameters. MRAC methodology using state-feedback is implemented and validated through simulations involving time-varying vehicle dynamics, such as changes in control sensitivity and added phase delays. Results demonstrate the model's ability to maintain consistent tracking performance despite dynamic modifications, though discrepancies with human pilot data highlight the complexity of fully capturing adaptive human control strategies. The proposed model offers a framework
The aim of this study is to augment the uncertain dynamics of the helicopter in order to resemble the dynamics of a new kind of vehicle, the so called Personal Aerial Vehicle. To achieve this goal a two step procedure is proposed. First, the helicopter model dynamics is augmented with a PID-based dynamic controller. Such controller implements a model following on the nominal helicopter model without uncertainties. Then, anL1 adaptive controller is designed to restore the nominal responses of the augmented helicopter when variations in the identified parameters are considered. The performance of the adaptive controller is evaluated via Montecarlo simulations. The results show that the application of the adaptive controller to the augmented helicopter dynamics can significantly reduce the effects of uncertainty due to the identification of the helicopter model. For implementation reasons the adaptive controller was applied to a subset of the outputs of the system. However, the under
It is necessary to track system state during robotic manipulation. System state is defined to be manipulator and environment configuration. Without tracking, the robot is ignorant of the outcomes of its actions. State tracking enables the robot to respond to sub-task failures. Tracking system state is difficult for complex robots that incorporate hundreds of individual sensor signals. It is difficult to determine which signals are relevant and which are not, and to find low dimensional representations of system state.
A new technology has been developed for improving performance and stability of control systems. This method represents a significant advancement in the state-of-the-art of adaptive control technology. The present invention is a new type of adaptive control law, called optimal control modification, which blends two control technologies together: optimal control and adaptive control.
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