Purpose This paper aims to present a nonlinear mathematical model of a small-scale turbojet aeroengine and also a speed controller design that is conducted for the constructed nonlinear mathematical model. Design/methodology/approach In the nonlinear mathematical model of the turbojet engine, temperature, rotational speed, mass flow, pressure and other parameters are generated using thermodynamic equations (e.g. mass, energy and momentum conservation laws) and some algebraic equations. In calculation of the performance parameters, adaptive neuro fuzzy inference system (ANFIS) method is preferred in related components. All calculated values from the mathematical model are then compared with the cycle data of the turbojet engine. Because of the single variable control need and effect of noise factor, modified proportional–integral–derivative (PID) controller is treated for speed control. For whole operation envelope, various PID structures are designed individually, according to the operating points. These controller structures are then combined via gain-scheduling approach and integrated to the nonlinear engine model. Simulations are performed on MATLAB/Simulink environment for design and off-design operating points between idle to maximum thrust levels. Findings The cascade structure (proposed nonlinear engine aero-thermal model and speed controller) is simulated and tested at various operating points of the engine and for different transient conditions. Simulation results show that the transitions between the operating points are found successfully. Furthermore, the controller is effective for steady-state load changes. It is suggested to be used in real-time engine applications. Research limitations/implications Because of limited data, only speed control is treated and simulated. Practical implications It can be used as an application in the industry easily. Originality/value First point of novelty in the paper is in calculation of the performance parameters of compressor and turbine components. ANFIS method is preferred to predict performance parameters in related components. Second novelty in the paper can be seen in speed controller design part. Because of the single variable control need and effect of noise factor, modified PID is treated.
PurposeThe purpose of this paper is to generate residuals which can be used to detect fault and isolate on a vertical takeoff and landing (VTOL) aircraft dynamic model.Design/methodology/approachIn the proposed approach, a generalized observer scheme method based on an unknown input observer is used for residual generation and applied to detect and isolate a faulty sensor. A bank of robust unknown input observers estimates the state variables of the system and gathers necessary information for fault detection and isolation purposes.FindingsA sinus signal is considered as a non‐linear disturbance in simulations. A failure simulation was prepared in different times. In this situation an unknown input observer should be designed which could predict the states of the system against the disturbances or unknown inputs. In the real world, there exist unknown inputs such as system non‐linearities, noise and disturbances. The paper shows that the system based on UIO is robust for unknown inputs mentioned above.Originality/valueIt is simulated on a VTOL dynamic model using MATLAB/Simulink. Any single sensor fault could be detected and isolated correctly. This kind of observer is also robust and flexible.
Proportional integral derivative controllers are widely used in industrial processes because of their simplicity and effectiveness for linear and nonlinear systems. The fuzzy controller is the most suitable for the human decision-making mechanism, providing the operation of an electronic system with decisions of experts. In addition, using the fuzzy controller for a nonlinear system allows for a reduction of uncertain effects in the system control. In this study, a proportional integral derivative controller and a fuzzy logic controller are designed and compared for a single-axis solar tracking system using an Atmel microcontroller. According to the angle of solar energy, a solar panel is oriented to the side where light intensity is greatest by being designed for the related supervisory controllers. Thus, the aim is to increase the energy obtained from solar panels by providing the specular reflection of the sun's rays to a solar panel. At the same time, a maximum efficient processing system has been determined by taking account of two controllers for the designed system.
Purpose The purpose of this paper is to present fault tolerant control of a quadrotor based on the enhanced proportional integral derivative (PID) structure in the presence of one or more actuator faults. Design/methodology/approach Mathematical model of the quadrotor is derived by parameter identification of the system for the simulation of the UAV dynamics and flight control in MATLAB/Simulink. An improved PID structure is used to provide the stability of the nonlinear quadcopter system both for attitude and path control of the system. The results of the healty system and the faulty system are given in simulations, together with motor dynamics. Findings In this study, actuator faults are considered to show that a robust controller design handles the loss of effectiveness in motors up to some extent. For the loss of control effectiveness of 20 per cent in first and third motors, psi state follows the reference with steady state error, and it does not go unstable. Motor 1 and Motor 3 respond to given motor fault quickly. When it comes to one actuator fault, steady state errors remain in some states, but the system does not become unstable. Originality/value In this paper, an enhanced PID controller is proposed to keep the quadrotor stable in case of actuator faults. Proposed method demonstrates the effectiveness of the control system against motor faults.
Purpose Condition monitoring and health management of an aircraft engine is of importance due to engine’s critical position in aircraft. Missions require uninterrupted and safer conditions during the flight or taxi operations. Hence, the deviations, abnormal situations or failures have to be under control. This paper aims to propose a cascade connected approach for an aircraft engine fault tolerant control. Design/methodology/approach The cascade connected structure includes a full-order unknown input observer for fault detection and eliminating the unknown disturbance effect on system, a generalized observer scheme for fault isolation and a Boolean logic mechanism for decision-making in reconfiguration process, respectively. This combination is simulated on a linear turbojet engine model in case of unknown input disturbance and under various sensor failure scenarios. Findings The simulation results show that the suggested fault detection isolation reconfiguration (FDIR) approach works effectively for multiple sensor failures with various amplitudes. Originality/value Different from other studies, the proposed model is sensitive to unknown input disturbance and failures that have unknown amplitudes. One another notable feature of suggested FDIR approach is adaptability of structure against multiple sensor failures. Here, it is assumed that only a single fault is to be detected and isolated at a time. The simulation results show that the proposed structure can be suggested for linear models especially for physical redundancy-based real-time applications easily, quickly and effectively.
Purpose This study aims to present a method for the conceptual design and simulation of an aircraft flight control system. Design/methodology/approach The design methodology is based on particle swarm optimization (PSO). PSO can be used to improve the performance of conventional controllers. The aim of the present study is threefold. First, it attempts to detect and isolate faults in an aircraft model. Second, it is to design a proportional (P) controller, a proportional derivative (PD) controller, a proportional-integral (PI) controller and a fuzzy controller for an aircraft model. Third, it is to design a PD controller for an aircraft using a PSO algorithm. Findings Conventional controllers, an intelligent controller and a PD controller-based PSO were investigated for flight control. It was seen that the P controller, the PI controller and the PD controller-based PSO caused overshoot. These overshoots were 18.5, 87.7 and 2.6 per cent, respectively. Overshoot was not seen using the PD controller or fuzzy controller. Steady state errors were almost zero for all controllers. The PD controller had the best settling time. The fuzzy controller was second best. The PD controller-based PSO was the third best, but the result was close to the others. Originality/value This study shows the implementation of the present algorithm for a specified space mission and also for study regarding variation of performance parameters. This study shows fault detection and isolation procedures and also controller gain choice for a flight control system. A comparison between conventional controllers and PD-based PSO controllers is presented. In this study, sensor fault detection and isolation are carried out, and, also, root locus, time domain analysis and Routh–Hurwitz methods are used to find the conventional controller gains which differ from other studies. A fuzzy controller is created by the trial and error method. Integral of squared time multiplied by squared error is used as a performance function type in PSO.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.