Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its a...
This thesis presents flight test results for a new neuroadaptive architecture: Deep Neural Network b...
In light of the growing applicability of Artificial Neural Network (ANN) in the signal processing fi...
In this paper we explore a Direct Adaptive Control scheme for stabilizing a non-linear, physics base...
Utilizing the universal approximation property of neural networks, we develop several novel approac...
Neural network-based adaptive output feedback approaches that augment a linear control design are de...
A new model reference adaptive control design method with guaranteed transient performance using neu...
Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated fo...
Loss of control is a serious problem in aviation that primarily affects General Aviation. Technologi...
Inherent stability inside the flight envelope must be guaranteed in order to safely introduce privat...
Aircraft operate over a wide range of conditions including atmospheric, weight, and center ofgravity...
PhD ThesisModelling and control of non-linear systems are not easy, which are now being solved by t...
This thesis describes the development and implementation of an on-line optimal predictive controller...
The Neural Virtual Machine (NVM) is a novel neurocomputational architecturedesigned to emulate the f...
L1 adaptive control for a turbofan commercial aircraft engine is developed and applied in the presen...
Model Reference Adaptive Control (MRAC) is a widely studied adaptive control methodology that aims t...
This thesis presents flight test results for a new neuroadaptive architecture: Deep Neural Network b...
In light of the growing applicability of Artificial Neural Network (ANN) in the signal processing fi...
In this paper we explore a Direct Adaptive Control scheme for stabilizing a non-linear, physics base...
Utilizing the universal approximation property of neural networks, we develop several novel approac...
Neural network-based adaptive output feedback approaches that augment a linear control design are de...
A new model reference adaptive control design method with guaranteed transient performance using neu...
Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated fo...
Loss of control is a serious problem in aviation that primarily affects General Aviation. Technologi...
Inherent stability inside the flight envelope must be guaranteed in order to safely introduce privat...
Aircraft operate over a wide range of conditions including atmospheric, weight, and center ofgravity...
PhD ThesisModelling and control of non-linear systems are not easy, which are now being solved by t...
This thesis describes the development and implementation of an on-line optimal predictive controller...
The Neural Virtual Machine (NVM) is a novel neurocomputational architecturedesigned to emulate the f...
L1 adaptive control for a turbofan commercial aircraft engine is developed and applied in the presen...
Model Reference Adaptive Control (MRAC) is a widely studied adaptive control methodology that aims t...
This thesis presents flight test results for a new neuroadaptive architecture: Deep Neural Network b...
In light of the growing applicability of Artificial Neural Network (ANN) in the signal processing fi...
In this paper we explore a Direct Adaptive Control scheme for stabilizing a non-linear, physics base...