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With the expanding utility base of unmanned aerial vehicles (UAVs), there comes a strong need for dedicated research towards effective control strategies for these aircraft. This paper presents a feedback control scheme for the trajectory tracking and flight control problem for quadrotor-type UAVs. In particular, a robust fractional-order sliding mode control (FOSMC) scheme based on a state observer approach, consisting of position tracking and an attitude control strategy, is proposed. The basic idea behind choosing the state observer technique is to prevent the chattering phenomenon in this highly non-linear and under-actuated dynamic structure, and preserve the robustness and finite time reachability properties of Sliding Mode controllers. The stability of the proposed control law is proved using Lyapunov's theorem. Furthermore, a detailed performance analysis of the proposed method is presented in comparison to the existing control algorithms from the recent literature. The obtained results are found promising. [ABSTRACT FROM AUTHOR]

This paper proposes a Distance and Levy-flight based Crow Search Algorithm (DLCSA) for optimization of three Fractional-order controllers: Fractional Order Proportional-Integral-Derivative (FOPID), Fractional Order Proportional-Integral (FOPI) and Fractional Order Proportional-Derivative (FOPD) controllers. Novelties of the proposed method are fourfold; (a) distance-based Flight length (Dfl)to improve the convergence rate, (b) Levy-flight based evasion to improve the efficiency of the algorithm, (c)adaptive awareness probability(AP) to set a tradeoff between local and global search and (d) Fuzzy based adaptive objective function to adjust the solution weights automatically. With such adaptations, the new approach preserves solution diversity and improves the convergence to global optima. To demonstrate the effectiveness of the contributions, proposed methods: DLCSA-FOPID, DLCSA-FOPI, and DLCSA-FOPD are implemented on an Automatic Voltage Regulator system and results are compared with few other well-established techniques like Zeigler–Nichols (Z-N), Artificial Bee Colony (ABC) Optimization (ACO), Multi-Objective Extremal Optimization (MOEO), Genetic Algorithm (GA), Chaos Ant Swarm (CAS) algorithm, Real Coded Extremal Optimization (RCEO), etc. Further to investigate robustness of proposed methods, the results are obtained for set-point tracking, noise suppression, load disturbance rejection, control effort, and modeling errors. The results demonstrate the better performance of the proposed method as compared to other state of the art techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a new control law for designing an optimal proportional‐integral‐derivative (PID) controller for flexible automatic voltage regulator (AVR) system using modified neural network algorithm (MNNA). First, the exploration capability of neural network algorithm (NNA) is enhanced by addition of a learning factor, α in MNNA. Then, to evaluate the performance of MNNA in terms of its exploration and exploitation capabilities, extensive statistical analysis has been carried out on 23 benchmark functions consisting of unimodal, multimodal and fixed dimension multimodal functions against 12 state of the art algorithms. The results are encouraging and marks the superiority of MNNA against NNA as well as 11 other state of the art techniques. It is followed by application of Kharitonov theorem as a design tool to derive the Interval AVR system. Next, NNA and MNNA have been used for tuning of PID controller parameters in such a way that maximum value of the closed loop eigen values of K‐extreme polynomials is minimized. Further to show the robustness of the proposed methods, the results are taken on set point tracking, noise suppression, load disturbance rejection, and minimum controller effort utilized by these controllers and compared against seven state of the art techniques namely PSO, GA, ABC, MOEO, NSGA‐II, FSA and variants of constrained GA. The results demonstrate the high performance of the PID controller optimized using MNNA for control of I‐AVR and guarantees stability for a wider range of system parameter uncertainty. [ABSTRACT FROM AUTHOR]