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\"Situation Assessment in Aviation focuses on new aspects of soft computing technologies for evaluation and assessment of situations in aviation scenarios. It considers technologies emerging from multisensory data fusion (MSDF), Bayesian networks (BN), and fuzzy logic (FL) to assist pilots in their decision-making. Studying MSDF, BN, and FL from the perspective of their applications to the problem of situation assessment, the book discusses the development of certain soft technologies that can be further used for devising more sophisticated technologies for a pilot's decision-making when performing certain tasks: airplane monitoring, pair formation, attack, and threat. It explains the concepts of situation awareness, data fusion, decision fusion, Bayesian networks, fuzzy logic type 1, and interval type 2 fuzzy logic. The book also presents a hybrid technique by using BN and FL and a unique approach to the problem of situation assessment, beyond visual range and air-to-air combat, by utilizing building blocks of artificial intelligence (AI) for future development of more advanced automated systems, especially using commercial software.The book is intended for aerospace R&D engineers, systems engineers, aeronautical engineers, and aviation training professionals. It will also be useful for aerospace and electrical engineering students taking courses in Air Traffic Management, Aviation Management, Aviation Operations, and Aviation Safety Systems\"-- Provided by publisher.

•Model-free VRFT applied to ADRC combined with fuzzy control is proposed.•Least-squares algorithm specific to VRFT is replaced with Grey Wolf Optimizer.•The fuzzy control system stability is employed in the design approaches.•Model-free optimal tuning of controllers for tower crane systems is done.•Experimentally validated model-free controllers are offered. This paper proposes the Virtual Reference Feedback Tuning (VRFT) of a combination of two control algorithms, Active Disturbance Rejection Control (ADRC) as a representative data-driven (or model-free) control algorithm and fuzzy control, in order to exploit the advantages of data-driven control and fuzzy control. The combination of Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control (PDTSFC) tuned by Virtual Reference Feedback Tuning results in two novel data-driven algorithms referred to as hybrid data-driven fuzzy ADRC algorithms. The main benefit of this combination is the automatic optimal tuning in a model-free manner of the parameters of the combination of Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control called ADRC-PDTSFC. The second benefit is that the suggested combination is time saving in finding the optimal parameters of the controllers. However, since Virtual Reference Feedback Tuning generally works with linear controllers to solve a certain optimization problem and the fuzzy controllers are essentially nonlinear, this paper replaces the least-squares algorithm specific to Virtual Reference Feedback Tuning with a metaheuristic optimization algorithm, i.e. Grey Wolf Optimizer. The fuzzy control system stability is guaranteed by including a limit cycle-based stability analysis approach in Grey Wolf Optimizer algorithm to validate the next solution candidates. The hybrid data-driven fuzzy ADRC algorithms are validated as controllers in terms of real-time experiments conducted on three-degree-of-freedom tower crane system laboratory equipment. To determine the efficiency of the new hybrid data-driven fuzzy ADRC algorithms, their performance is compared experimentally with that of two control algorithms, namely Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control, whose parameters are optimally tuned by Grey Wolf Optimizer in a model-based manner using the nonlinear process model. [Display omitted]

This paper addressed an issue regarding adaptive fuzzy backstepping for a class of stochastic nonlinear system with an event-triggered input. The presence of unknown nonlinear functions can be evaluated through fuzzy logic systems. With the aid of dynamic surface control, an adaptive tracking scheme is suggested, which can eliminate the “explosion of complexity.”An improved event-triggered co-design control scheme is proposed, whose threshold is a variable function combining tracking error, so that resources are saved. A tracking controller can guarantee that the signals of the closed-loop system are uniform. The tracking errors toward a small neighborhood of the origin. Simulations are conducted to validate the proposed method.

Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations. Graphical abstract

In this paper, the concentration is on a tracking control issue for a class of deferred time-varying output-constrained pure-feedback systems subject to unknown external disturbance. In the control scheme design procedure, the nonlinearities are approximated by fuzzy logic systems. To obtain a virtual term, the mean value theorem is applied to separate the non-affine component of pure-feedback systems. A fuzzy compensation disturbance observer is proposed to further weaken the impact of disturbances on the system. An auxiliary system is designed to ensure that the compensation term is related to the error between the ideal and actual signals. The shifting function and the fractional barrier function are combined to achieve deferred constraints on the system output. The proposed strategy can be used to determine the boundary parameters of the barrier function immediately and to avoid the need for prior knowledge of initial conditions. Two simulations are performed to confirm the validity of the presented strategy.

In this paper, an adaptive fuzzy output feedback position tracking constraint control method is proposed for permanent magnet synchronous motors (PMSM) system. Fuzzy logic systems are used to approximate unknown nonlinearities. For the cases of the immeasurable states, a state observer is proposed to solve the immeasurable states problem. In the unified framework of adaptive backstepping control design and utilizing the barrier Lyapunov function method, an observer-based adaptive fuzzy output feedback tracking constraint control scheme is developed. The simulation results are given to illustrate the effectiveness of the proposed control method for the PMSM.

Unknown hysteresis cannot be ignored in containment control, but the problem of prescribed-time containment for uncertain nonlinear multi-agent systems with unknown hysteresis remains unsolved. This paper mainly investigates this problem. At first, to achieve prescribed-time convergence of containment errors and reduce overshoot of containment errors, a prescribed-time convergence technique is proposed. Then, considering unknown nonlinear function and completely unknown hysteresis in uncertain nonlinear multi-agent systems, by introducing Nussbaum function, the fuzzy logic systems and backstepping technique, we propose a novel distributed adaptive fuzzy control method to ensure all containment errors converge to a predefined zone in a prescribed time. Finally, stability analysis and simulation results confirm the proposed control method is effective.

This study presents an adaptive perturb and observe (P&O)-fuzzy control maximum power point tracking (MPPT) for photovoltaic (PV) boost dc–dc converter. P&O is known as a very simple MPPT algorithm and used widely. Fuzzy logic is also simple to be developed and provides fast response. The proposed technique combines both of their advantages. It should improve MPPT performance especially with existing of noise. For evaluation and comparison analysis, conventional P&O and fuzzy logic control algorithms have been developed too. All the algorithms were simulated in MATLAB-Simulink, respectively, together with PV module of Kyocera KD210GH-2PU connected to PV boost dc–dc converter. For hardware implementation, the proposed adaptive P&O-fuzzy control MPPT was programmed in TMS320F28335 digital signal processing board. The other two conventional MPPT methods were also programmed for comparison purpose. Performance assessment covers overshoot, time response, maximum power ratio, oscillation and stability as described further in this study. From the results and analysis, the adaptive P&O-fuzzy control MPPT shows the best performance with fast time response, less overshoot and more stable operation. It has high maximum power ratio as compared to the other two conventional MPPT algorithms especially with existing of noise in the system at low irradiance.

In this article, the problem of fuzzy adaptive fixed-time control is addressed for nonstrict-feedback nonlinear systems with input saturation and dead zone. The universal approximation properties of fuzzy logic systems are employed to model the unknown nonlinear functions. A command filter-based fixed-time adaptive fuzzy control strategy is presented based on the backstepping framework and fixed-time control theory. The command filter technique is presented to address the “computational explosion” problem inherent in the backstepping scheme, and an error compensation mechanism is adopted to reduce the errors arising from command filters. Meanwhile, the non-smooth input saturation and dead zone nonlinearities are approximated using a non-affine smooth function, and they are transformed into an affine form based on the mean-value theorem. The fixed-time convergence of the tracking error and the boundedness of the closed-loop signals are proved using the fixed-time stability theory. Finally, simulation was performed to demonstrate the effectiveness of the presented method.

This paper focuses on the decentralized finite-time prescribed performance control problem for a class of large-scale nonlinear interconnected systems with input dead zone using an adaptive fuzzy approach. Specifically, fuzzy logic systems are utilized to approximate unknown nonlinear system functions and a finite-time prescribed performance control scheme is designed by taking advantage of both the adaptive technique and backstepping scheme. By introducing two smooth functions and utilizing the command filter backstepping design, the ‘explosion of complexity’ problem inherent in the conventional backstepping control is overcome, while the associated problems due to unknown interconnections are solved. The proposed control scheme guarantees that all signals within the closed-loop controlled system are bounded and the output tracking error falls within a small range predefined by the prescribed performance within a finite time. Two simulation examples are given to verify the high effectiveness of the presented control approach.