Mihai Lungu

Automatic Control of Aircraft in Longitudinal Plane During Landing

Automatic control of aircraft during landing is discussed and a new structure of automatic landing system (ALS) is designed using the dynamic inversion concept and proportional-integral-derivative (PID) controllers in conventional and fuzzy variants. Theoretical results are validated by numerical simulations in the absence or presence of wind shears and sensor errors.

Reduced Order Observer for Linear Time-Invariant Multivariable Systems with Unknown Inputs

This paper presents the design of a new reduced order observer to estimate the state for a class of linear time-invariant multivariable systems with unknown inputs. The proposed design approach is a combination of the approaches proposed by Hou and Muller (IEEE Trans. Autom. Control 37:871–875, 1992) and Boubaker (Int. J. Autom. Control Syst. Eng. 5:45–51, 2005); matrix decompositions, state transformations, and substitutions based on coordinate changes are used. It is shown that the problem of reduced order observers for linear systems with unknown inputs can be reduced to a standard one (the unknown input vector will not interfere in the observer equations). The effectiveness of the suggested design algorithm is illustrated by a numerical example (aircraft lateral motion), and, for the same aircraft dynamics, we compare our new observer with other already existing observers from the existence conditions and dynamic characteristics point of view; the superiority of the new designed observer is demonstrated.

ALSs with Conventional and Fuzzy Controllers Considering Wind Shear and Gyro Errors

This paper presents the automatic control of the aircraft in the longitudinal plane during the landing process, taking into account the wind shear and sensor errors. Two automatic landing systems (ALSs) are designed. The former uses an instrument landing system (ILS), whereas the latter controls flight altitude using the state vector. Both systems have a subsystem for the control of longitudinal velocity that is based on the dynamic inversion theory. The subsystems for pitch-angle control use proportional-derivative (PD) control laws or a law based on the dynamic inversion theory and a proportional-integral-derivative (PID) controller. The slope and flare controllers are a PD controller and a PID controller, respectively. The controllers are designed in both classical and fuzzy-logic approaches. Theoretical results are validated by numerical simulations in the absence or presence of wind shear and sensor errors. Analysis of the time evolution of the main ALS parameter leads to conclusions regarding the superiority of the dynamic qualities for the ALS with fuzzy controllers.

Automatic control of aircraft lateral-directional motion during landing using neural networks and radio-technical subsystems

The paper presents a new automatic architecture for the control of aircraft lateral-directional motion during landing; the system controls the lateral angular deviation of aircraft longitudinal axis with respect to the runway, by using a classical controller, a radio-navigation system, a system for the calculation of the distances between aircraft and the runway radio-markers, and an adaptive controller mainly used for the control of aircraft roll angle and its deviation with respect to the runway. The adaptive control system uses the dynamic inversion concept, a dynamic compensator, a neural network trained by the systems estimated error vector (signal provided by a linear observer), and a Pseudo Control Hedging block. The new designed adaptive architecture is software implemented and validated by complex numerical simulations; the obtained characteristics are very good and prove the new architectures stability and its small overshoots.

Automatic landing control using H-inf control and dynamic inversion

The paper presents the automatic control of the aircraft in the longitudinal plane during landing, taking into account the sensor errors and the wind shears. The H-inf control provides robust stability with respect to the uncertainties caused by different disturbances and noise type signals, while the dynamic inversion provides good precision tracking. These techniques are combined and a robust automatic landing system is obtained; by adding an optimal observer and two reference models providing the desired altitude and velocity, one obtained a new automatic landing system which is very suited for landing control in the longitudinal plane. The optimal control law is calculated in two ways, this improving the generality, applicability, and simplicity degree of the automatic landing system. The theoretical results are validated by numerical simulations for a Boeing 747 landing; the simulation results are very good (Federal Aviation Administration accuracy requirements for Category III are met) and show the robustness of the algorithm even in the presence of wind shears and sensor errors. Moreover, the designed control law has the ability to reject the sensor measurement noises, wind gust, and wind shears with low intensity.

Adaptive Control of Helicopter Pitch Angle and Velocity

AbstractThis paper discusses flying objects’ adaptive control with direct application to the flight of helicopters. Two new automatic adaptive control systems are suggested: the former is used for pitch angle control, while the latter is used for control of helicopter pitch angle and velocity; this second system is an extension of the first one. The adaptive control is based on the dynamic inversion principle and the use of neural networks. The two adaptive control systems have reference models, linear dynamic compensators, linear observers, and neural networks. The adaptive components of the automatic control laws compensate for the approximation errors of the dynamic model’s nonlinear functions. The used actuators are linear or nonlinear. To eliminate the neural networks’ adapting difficulties, a pseudo-control hedging (PCH) block is inserted in the adaptive system; it limits the adaptive pseudo-control by means of a component that represents the estimation error of the actuator dynamics. Thus, the PCH ...

Micro-electromechanical systems gyro performance improvement through bias correction over temperature using an adaptive neural network-trained fuzzy inference system

This article presents a new method to obtain a miniaturized intelligent gyro sensor. The proposed method uses a fuzzy logic controller to realize an online correction of the error due to the gyro sensor’s bias variation with temperature. In the first phase, gyro experimental testing is performed for various speeds ranging from −150 to 150°/s and temperatures between −10 °C and 70 °C, and the results obtained with classical compensation methods are evaluated. The fuzzy logic controller is developed via a fuzzy inference system (FIS), generated from the gyro testing experimental results. Further, to optimize the membership function parameters of its input–output variables, the FIS is trained with a neuro-fuzzy network. Validation by the assessment of errors shows that the proposed method gives better results than the classical algorithms based on the least squares method.

Adaptive flight control law based on neural networks and dynamic inversion for micro-aerial vehicles

The paper presents two new adaptive systems, for the attitudes control of the micro-aerial vehicles (MAVs) - insect type. The dynamic model describing the motion of MAVs with respect to the Earth tied frame is nonlinear and the design of the new adaptive control system is based on the dynamic inversion technique. The inversion error is calculated with respect to the control law and two matrices (inertia and dynamic damping matrices) which express the deviation of the estimated matrices relative to the calculated ones (the matrices from the nonlinear dynamics of MAVs) in conditions of absolute stability in closed loop system by using the Lyapunov theory. To completely compensate this error, an adaptive component (output of a neural network) is added in the control law. The system also includes a second order reference model which provides the desired attitude vector and its derivative. The two variants of the new adaptive control system are validated by complex numerical simulations.

Reduced-Order Multiple Observer for Aircraft State Estimation during Landing

The paper presents a new reduced-order multiple observer which can achieve the finite-time reconstruction of the system’s state associated to a multiple-model. This observer is a combination of a reduced-order observer and a full-order multiple observer. The design of the new observer involves the usage of the Lyapunov theory, the solving of a linear matriceal inequality, and a variables’ change. The steps of the design procedure have been software implemented in order to validate the new reduced-order multiple observer for the case of an aircraft motion during landing.

Precision improvement for a tunneling accelerometer by using an intelligent controller to close its loop

The paper presents a way to improve the transient regime of a tunnelling accelerometer by using a fuzzy controller to close its loop. The fuzzy controller replaces an electronic amplification and filtering block of proportional-derivative (PD) type, used on the feedback path of the classical architecture. For the proposed controller, proportional-derivative variant is chosen, and its input-output mapping is derived. The membership functions both for inputs and output are triangular-shaped. To define the rules, a Mamdani fuzzy model is chosen. Finally, a comparative numerical study between the classical and the proposed accelerometer architectures is made.