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 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.