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The purpose of this paper is to present a novel optimization approach to design a robust H-infinity controller.

To use a modified particle swarm optimization (PSO) algorithm and to search for the optimal parameters of the weighting functions under the circumstance of the given structures of three weighting matrices in the H-infinity mixed sensitivity design.

This constrained multi-objective optimization is a non-convex, non-smooth problem which is solved by a modified PSO algorithm. An adaptive mutation-based PSO (AMBPSO) algorithm is proposed to improve the search accuracy and convergence of the standard PSO algorithm. In the AMBPSO algorithm, the inertia weights are modified as a function with the gradient descent and the velocities and positions of the particles.

The AMBPSO algorithm can efficiently solve such an optimization problem that a satisfactory robust H-infinity control performance can be obtained.

The purpose of this paper is to conduct the design, development and testing of a controller for an autonomous small‐scale helicopter.

The hardware in the loop simulation (HILS) platform is developed based on the nonlinear model of JR Voyager G‐260 small‐scale helicopter. Autonomous controllers are verified using the HILS environment prior to flight experiments.

The gains of the multi‐loop cascaded control architecture can be effectively optimized within the HILS environment. Various autonomous flight operations are achieved and it is demonstrated that the prediction from the simulations is in a good agreement with the result from the flight test.

The synthesized controller is effective for the particular test‐bed. For other small‐scale helicopters (with different size and engine specifications), the controller gains must be tuned again.

This work represents a practical control design and testing procedures for an autonomous small‐scale helicopter flight control. The autonomous helicopter can be used for various missions ranging from film making, agriculture and volcanic surveillance to power line inspection.

The research addresses the need for systematic design, development and testing of controller for a small‐scale autonomous helicopter by utilizing HILS environment.

This paper aims to present the design of a stability augmentation system (SAS) in the longitudinal and lateral axes for an unstable helicopter.

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.

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.

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.

IT is intended to show in this article the magnitude of the weight saving which can be achieved on simple struts. It is necessary for any comparison to derive the weight and shape of the solid or hollow strut theoretically most efficient. This can be done most conveniently using the Calculus of Variations and it will be assumed in all cases that the strut is of such a length that bending stresses predominate at the instant of failure and that the ends of the strut are rotationally free.

To provide a progress report into research conducted to establish guidelines for the development of guidance vision aids.

The first stage of the research is to establish a coherent engineering basis for the methods of (visual) motion perception and control to inform the design of pilot aids that will support flight in degraded visual conditions, particularly when close to the ground. The next stage will then be to construct and evaluate synthetic displays that recover the visual cues necessary to allow flight in degraded visual conditions for a range of manoeuvres using the flight simulation facilities at the University of Liverpool (UoL). The research is guided by tau (time to contact) theory from the field of ecological psychology.

The closure of spatial gaps for a number of aircraft manoeuvres are presented in the tau domain. Analysis of the landing flare manoeuvre suggest that both a constant rate of change of tau strategy and an intrinsic tau‐guidance strategy will yield benefits in terms of touchdown descent rate if presented as display symbology.

Results are presented from trials where only one professional pilot was used. Results from a wider population of pilots need to be analysed to ensure that the observed trends are generic.

The reported results are being used in the next phase of the research project to inform the design of a guidance vision‐aid for the flare manoeuvre. These displays will be tested in flight simulation trials.

The research takes a theory of motion perception and applies it to aircraft guidance display technology.

The purpose of this paper is to elaborate and develop an automatic system for automatic flight control system (AFCS) performance evaluation. Consequently, the developed AFCS algorithm is implemented and tested in a virtual environment on one of the mission task elements (MTEs) described in Aeronautical Design Standard 33 (ADS-33) performance specification.

Control algorithm is based on the Linear Quadratic Regulator (LQR) which is adopted to work as a controller in this case. Developed controller allows for automatic flight of the helicopter via desired three-dimensional trajectory by calculating iteratively deviations between desired and actual helicopter position and multiplying it by gains obtained from the LQR methodology. For the AFCS algorithm validation, the objective data analysis is done based on specified task accomplishment requirements, reference trajectory and actual flight parameters.

In the paper, a description of an automatic flight control algorithm for small helicopter and its evaluation methodology is presented. Necessary information about helicopter dynamic model is included. The test and algorithm analysis are performed on a slalom maneuver, on which the handling qualities are calculated.

Developed automatic flight control algorithm can be adapted and used in autopilot for a small helicopter. Methodology of evaluation of an AFCS performance can be used in different applications and cases.

In the paper, an automatic flight control algorithm for small helicopter and solution for the validation of developed AFCS algorithms are presented.

This paper seeks to present a feedback error learning neuro‐controller for an unstable research helicopter.

Three neural‐aided flight controllers are designed to satisfy the ADS‐33 handling qualities specifications in pitch, roll and yaw axes. The proposed controller scheme is based on feedback error learning strategy in which the outer loop neural controller enhances the inner loop conventional controller by compensating for unknown non‐linearity and parameter uncertainties. The basic building block of the neuro‐controller is a nonlinear auto regressive exogenous (NARX) input neural network. For each neural controller, the parameter update rule is derived using Lyapunov‐like synthesis. An offline finite time training is used to provide asymptotic stability and on‐line learning strategy is employed to handle parameter uncertainty and nonlinearity.

The theoretical results are validated using simulation studies based on a nonlinear six degree‐of‐freedom helicopter undergoing an agile maneuver. The neural controller performs well in disturbance rejection is the presence of gust and sensor noise.

The neuro‐control approach presented in this paper is well suited to unmanned and small‐scale helicopters.

The study shows that the neuro‐controller meets the requirements of ADS‐33 handling qualities specifications of a helicopter.