Event-sampled direct adaptive neural network control of uncertain strict-feedback system with application to quadrotor unmanned aerial vehicle

Neural networks (NNs) are utilized in the backstepping approach to design a control input by approximating unknown dynamics of the strict-feedback nonlinear system with event-sampled inputs. The system state vector is assumed to be unknown and an observer is used to estimate the state vector. By using the estimated state vector and backstepping design approach, an event-sampled controller is introduced. As part of the controller design, first, input-to-state-like stability (ISS) for a continuously sampled controller that has been injected with bounded measurement errors is demonstrated and, subsequently, an event-execution control law is derived such that the measurement errors are guaranteed to remain bounded. Lyapunov theory is used to demonstrate that the tracking errors, the observer estimation errors, and the NN weight estimation errors for each NN are locally uniformly ultimately bounded (UUB) in the presence bounded disturbances, NN reconstruction errors, as well as errors introduced by event-sampling. Simulation results are provided to illustrate the effectiveness of the proposed controllers. Subsequently, the output-feedback neural network (NN) controller that was presented above is considered for an underactuated quadrotor UAV application. The flexibility for the control of a quadrotor UAV is extended by incorporating notions of event-sampling and by designing an appropriate event-execution law. First, the continuously sampled controller is considered in the presence of bounded measurement errors and it is shown that the system generates a local ISS-like Lyapunov function. Next, by designing an appropriate event-execution law, the measurement errors that result from event-sampling are shown to be bounded for all time. Finally, the effectiveness of the proposed event-sampled controller is demonstrated with simulation results --Abstract, page iv

Event-Sampled Control of Quadrotor Unmanned Aerial Vehicle using Neural Networks

In this paper, an event-sampled output-feedback neural network (NN) controller for a quadrotor unmanned aerial vehicle (UAV) is considered. First an observer design is presented, allowing the need for a full knowledge of the state-vector to be avoided. Next, a kinematic controller is designed in order to find a desired translational velocity; the information provided by the kinematic controller will be used in the design of a virtual controller wherein a desired rotational velocity will be determined such that the UAV\u27s orientation converges to its desired value. Finally, the information from the observer, the kinematic controller, and the virtual controller are used in the design of a dynamic controller where NNs will be implemented to approximate uncertainties in the UAV\u27s dynamics; the signals generated by the dynamic controller will ensure that the desired lift velocity and the desired rotational velocities are tracked. In all these designs, the effects of sampling errors are highlighted. Next, by designing an appropriate event-execution law, the sampling errors are shown to be bounded during the inter-event period. Finally, the effectiveness of the proposed event-sampled controller will be demonstrated with simulation results

Event-Sampled Direct Adaptive NN Output-and State-Feedback Control of Uncertain Strict-Feedback System

In this paper, a novel event-triggered implementation of a tracking controller for an uncertain strict-feedback system is presented. Neural networks (NNs) are utilized in the backstepping approach to design a control input by approximating unknown dynamics of the strict-feedback nonlinear system with event-sampled inputs. The system state vector is assumed to be unknown and an NN observer is used to estimate the state vector. By using the estimated state vector and backstepping design approach, an event-sampled controller is introduced. As part of the controller design, first, input-to-state-like stability for a continuously sampled controller that has been injected with bounded measurement errors is demonstrated, and subsequently, an event-execution control law is derived, such that the measurement errors are guaranteed to remain bounded. Lyapunov theory is used to demonstrate that the tracking errors, the observer estimation errors, and the NN weight estimation errors for each NN are locally uniformly ultimately bounded in the presence bounded disturbances, NN reconstruction errors, as well as errors introduced by event sampling. Simulation results are provided to illustrate the effectiveness of the proposed controllers

Event-Sampled Direct Adaptive NN State-Feedback Control of Uncertain Strict-Feedback System

In this paper, neural networks (NNs) are utilized in the backstepping approach to design a control input by approximating unknown dynamics of the strict-feedback nonlinear system with event-sampled inputs. The system state vector is assumed to be measurable. As part of the controller design, first, local input-to-state-like stability (ISS) for a continuously sampled controller that has been injected with bounded measurement errors is demonstrated and, subsequently, an event-execution control law is derived such that the measurement errors are guaranteed to remain bounded. Lyapunov theory is used to demonstrate that the tracking errors and the NN weight estimation errors for each NN are locally uniformly ultimately bounded (UUB) in the presence bounded disturbances, NN reconstruction errors, as well as errors introduced by event-sampling. Simulation results are provided to illustrate the effectiveness of the proposed controller

Automatic Pill Identification from Pillbox Images

There is a vital need for fast and accurate recognition of medicinal tablets and capsules. Efforts to date have centered on automatic segmentation, color and shape identification. Our system combines these with preprocessing before imprint recognition. Using the National Library of Medicine Pillbox database, regression analysis applied to automatic color and shape recognition allows for successful pill identification. Measured errors for the subtasks of segmentation and color recognition for this database are 1.9% and 2.2%, respectively. Imprint recognition with optical character recognition (OCR) is key to exact pill ID, but remains a challenging problem, therefore overall recognition accuracy is not yet known