Rotorcraft Parameter Identification from Real Time Flight Data

Rotorcraft system identification involves the derivation of the model structure and parameters, such as aerodynamic stability and control derivatives, from flight-test results. Accurate rotorcraft system identification often requires higher order mathematical models. However, system identification becomes difficult and complicated as the number of modeling degrees of freedom are increased. In the present work, a new method for rotorcraft parameter estimation based on the application of a radial basis function network is proposed. The radial basis function networkbased technique does not require a mathematical model of the helicopter, and the rotorcraft parameters can be directly computed from the flight data. The radial basis function network is found to give results in the same range as obtained from conventional parameter estimation techniques, such as the maximum likelihood method. Results obtained using the radial basis function network approach are compared to published research on the BO 105 helicopter and found to be in good agreement

A direct adaptive neural command controller design for an unstable helicopter

This paper presents an off-line (finite time interval) and on-line learning direct adaptive neural controller for an unstable helicopter. The neural controller is designed to track pitch rate command signal generated using the reference model. A helicopter having a soft inplane four-bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is used for the simulation studies. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using backpropagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval) network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller (DANC) is compared with feedback error learning neural controller (FENC)

Neural Networks Based Identification of Helicopter Dynamics Using Flight Data

In this paper, identification of lateral and longitudinal dynamics of a helicopter using flight data is presented. Neural networks with linear filter also known as Narendra's model and recurrent neural networks with internal memory (memory neuron networks) are used for the purpose. These networks are used for identifying the rate and attitude response of the helicopter to a given longitudinal cyclic and lateral cyclic inputs. The relative effectiveness of these algorithms has been discussed

Design of a stability augmentation system for a helicopter using LQR control and ADS-33 handling qualities specifications

Purpose – This paper aims to present the design of a stability augmentation system (SAS) in the longitudinal and lateral axes for an unstable helicopter. Design/methodology/approach – The feedback controller is designed using linear quadratic regulator (LQR) control with full state feedback and LQR with output feedback approaches. SAS is designed to meet the handling qualities specification known as Aeronautical Design Standard (ADS-33E-PRF). A helicopter having a soft inplane four-bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is used for the simulation studies. In the simulation studies, the helicopter is trimmed at hover, low speeds and forward speeds flight conditions. The performance of the helicopter SAS schemes are assessed with respect to the requirements of ADS-33E-PRF. Findings – The SAS in the longitudinal axis meets the requirement of the Level 1 handling quality specifications in hover and low speed as well as for forward speed flight conditions. The SAS in the lateral axis meets the requirement of the Level 2 handling quality specifications in both hover and low speed as well as for forward speed flight conditions. The requirements of the inter axis coupling is also met and shown for the coupled dynamics case. The SAS in lateral axis may require an additional control augmentation system or adaptive control to meet the Level 1 requirements. Originality/value – The study shows that the design of a SAS using LQR control algorithm with full state and output feedbacks can be used to meet ADS-33 handling quality specifications

Environment Recognition for Path Generation in Autonomous Mobile Robot

An efficient algorithm for path generation in autonomous mobile robots using a visual recognition approach is presented. The proposal includes image filtering techniques by employing an inspecting camera to sense a cluttered environment. Template matching filters are used to detect several environment elements, such as obstacles, feasible terrain, the target location, and the mobile robot. The proposed algorithm includes the parallel evolutionary artificial potential field to perform the path planning for autonomous navigation of the mobile robot. Our problem to be solved for autonomous navigation is to safely take a mobile robot from the starting point to the target point employing the path with the shortest distance and which also contains the safest route. To find the path that satisfies this condition, the proposed algorithm chooses the best candidate solution from a vast number of different paths calculated concurrently. For achieving efficient autonomous navigation, the proposal employs a parallel computation approach for the evolutionary artificial potential field algorithm for path generation and optimization. Experimental results yield accuracy in environment recognition in terms of quantitative metrics. The proposed algorithm demonstrates efficiency in path generation and optimization

Design and characterization of diclofenac diethylamine transdermal patch using silicone and acrylic adhesives combination

<p>Abstract</p> <p>Background and purpose of the study</p> <p>The objective of the study was to develop and characterize Diclofenac Diethylamine (DDEA) transdermal patch using Silicone and acrylic adhesives combination.</p> <p>Methods</p> <p>Modified solvent evaporation method was employed for casting of film over Fluoropolymer coated polyester release liner. Initial studies included solubilization of drug in the polymers using solubilizers. The formulations with combination of adhesives were attempted to combine the desirable features of both the adhesives. The effect of the permeation enhancers on the drug permeation were studied using pig ear skin. All the optimized patches were subjected to adhesion, dissolution and stability studies. A 7-day skin irritancy test on albino rabbits and an in vivo anti-inflammatory study on wistar rats by carrageenan induced paw edema method were also performed.</p> <p>Results</p> <p>The results indicated the high percent drug permeation (% CDP-23.582) and low solubility nature (1%) of Silicone adhesive and high solubility (20%) and low% CDP (10.72%) of acrylic adhesive. The combination of adhesives showed desirable characteristics for DDEA permeation with adequate % CDP and sufficient solubility. Release profiles were found to be dependent on proportion of polymer and type of permeation enhancer. The anti-inflammatory study revealed the sustaining effect and high percentage inhibition of edema of C4/OLA (99.68%). The acute skin irritancy studies advocated the non-irritant nature of the adhesives used.</p> <p>Conclusion</p> <p>It was concluded that an ideal of combination of adhesives would serve as the best choice, for fabrication of DDEA patches, for sustained effect of DDEA with better enhancement in permeation characteristics and robustness.</p