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The paper presents a nonlinear dynamic model for DGMSCMGs, sometimes used as actuators to control the attitude of large satellites. The developed models describe the translation dynamics, the rotation dynamics of the Active Magnetic Bearing Rotor, as well as the rotation dynamics of the two mobile gimbals. Two control architectures are initially designed by using the dynamic inversion concept, proportional-integrator-derivative/proportional-derivative dynamic compensators, linear observers, and a neural network to compensate the effect of the dynamic inversion error. One also develops a similar adaptive control architecture consisting of a proportional-integrator dynamic compensator, a feed-forward neural network, and a linear observer. The latter system models two interconnected nonlinear servo-systems and controls the angular rates of the two mobile gimbals actuated by the attitude controller of the satellite. The validation of the novel control architectures is achieved in Matlab/Simulink, the obtained results proving a very good angular rate precision and the robustness of the control systems in relation to the external disturbances. [ABSTRACT FROM AUTHOR]

The paper presents a new autonomous adaptive system for the control of airplanes during landing in longitudinal plane. For the first stage of landing in longitudinal plane, the main variables to be controlled are the glide slope angle and longitudinal velocity; during the second main stage of landing in longitudinal plane, the vertical velocity error and the airplane's longitudinal velocity are controlled. The new robust control architecture has linear subsystems, for which the relative degrees are calculated; the new architecture will also consists of a dynamic compensator, a linear observer, and two reference models, their design being accomplished with respect to the calculated relative degrees. The signal estimated by the observer is useful for training a neural network – an adaptive subsystem of the architecture that provides the adaptive component of the control law. In the case of actuators having nonlinear dynamics, pseudo control hedging blocks are used to cancel the adapting difficulties of the neural networks. The new adaptive architecture is software implemented and validated by complex numerical simulations. [ABSTRACT FROM AUTHOR]

The paper presents three new autonomous systems for the control of lateral-directional motion of airplane during landing. The geometry of airplane motion in lateral-directional plane takes into account the wind having different velocities and directions. All the three control systems contain an adaptive control subsystem based on the concepts of dynamic inversion and neural networks; the reference models of the adaptive control systems receive the signals provided by Pseudo Control Hedging blocks, as additional signals, to cancel the neural networks’ adapting difficulties in the case of nonlinear actuators with time delay and saturation zones with respect to the velocity and displacement. Two of the three automatic control systems have proportional–derivative/proportional–integral– derivative controllers for the control of the lateral deviation of airplane with respect to the runway. The new designed adaptive architectures have been software implemented and validated by complex numerical simulations; the obtained results prove the new architectures’ stability and small overshoots. [ABSTRACT FROM AUTHOR]

This paper presents two new automatic landing systems (ALSs) for aircraft motion in longitudinal plane; the model of the landing geometry determines the flight trajectory and the aircraft calculated altitude; the flight trajectory during landing consists of two parts: the glide slope and the flare. Both designed ALSs have an adaptive system (ACS) for the aircraft output's control; for the first ALS, the output vector consists of the flying altitude and the longitudinal velocity, while, for the second ALS, the output variables are the pitch angle and the longitudinal velocity of aircraft. The second variant of ALS also contains an altitude controller providing the calculated pitch angle. The calculated altitude (for the first ALS), the calculated pitch angle (for the second ALS), and the desired flight velocity are provided to the ACS by means of a block consisting of two reference models. ACS is based on the dynamic inversion concept and contains an adaptive controller which includes a linear dynamic compensator, a state observer, a neural network, and a Pseudo Control Hedging block. The paper is focused both on the design of the two ALSs and on their complex software implementation and validation. [ABSTRACT FROM AUTHOR]

The paper presents two new adaptive systems, for the attitude׳s control of the micro-aerial vehicles (MAV׳s) – insect type. The dynamic model describing the motion of MAV׳s 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 MAV׳s) 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. [ABSTRACT FROM AUTHOR]

The paper focuses on the automatic control of aircraft in the lateral-directional plane during the landing approach phase, taking into account crosswind and sensor errors. Two new automatic landing systems are designed using the H-inf control, dynamic inversion, optimal observers, and reference models. To validate them, we use the dynamics associated with the landing of a light airplane, we software implement the theoretical results, and analyze the accuracy of the results and the precision standards' achievement with respect to the requirements of the Federal Aviation Administration for lateral error during the lateral-directional motion of the aircraft during the landing approach phase. [ABSTRACT FROM AUTHOR]

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 system׳s 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 architecture׳s stability and its small overshoots. [ABSTRACT FROM AUTHOR]

The paper focuses on the aircraft automatic control in longitudinal plane, during landing, taking into account the sensor errors and disturbances. Aircraft auto-landing is achieved by combining the mixed H 2 / H ∞ and the dynamic inversion control techniques and by using an optimal observer, the geometry of landing, and two reference models. The general control law will consist of a guidance component for the commanded landing trajectory (calculated by using the dynamic inversion approach) and an optimal component which will be designed by means of a mixed H 2 / H ∞ technique for the cancel of the sensor errors and other disturbances in the system. The theoretical results are validated by numerical simulations for the landing of a Boeing 747; the simulation results are very good, Federal Aviation Administration accuracy requirements for best category being met. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

The paper deals with the control of the satellite attitude by means of double-gimbal magnetically suspended control moment gyros (DGMSCMGs). In its first part, the paper presents the dynamics of the DGMSCMGs and the dynamics of the satellite equipped with a DGMSCMG actuator that creates a total satellite applied gyroscopic torque which is generated by the angular rate of the satellite and the angular rates applied to the gyroscopic gimbals. Then, an adaptive satellite attitude control system is designed with actuator and angular rate sensor (both DGMSCMG type), based on the use of the dynamic inversion control technique and a neural network; the latter models an adaptive component of the control law to compensate the negative influence of the dynamic inversion error. The attitude controller mainly consists of a dynamic compensator, a neural network, and a linear observer (for the estimation of the neural network training vector). The novel attitude control architecture is validated in Matlab/Simulink; the analysis of both theoretical concepts and obtained results prove the cancel of the convergence errors, an excellent angular rate precision, as well as robustness to external disturbances. [ABSTRACT FROM AUTHOR]