The issue of formation rearrangement for a troop of cooperative unmanned vertical takeoff and landing (VTOL) aircrafts in an obstacle-loaded atmosphere is figured out using a purposed backstepping based proportional-integral-derivative controller (PID). The designed controller is developed to regulate every unmanned quadrotor within the troop in an exceedingly localized manner guaranteeing the reserving of the required geometric formation. The backstepping technique could be a promising control technique for nonlinear and coupled multivariable systems. The essential contribution in this paper concentrates on resolving the formation issue for a troop of cooperative pilotless VTOL airplanes in a decentralized manner via backstepping PID regulator. The designed decentralized controller guarantees the success of the required mission of the swarming troop. The simulation results declare the successes of the proposed controller in guaranteeing the stability of the system and reserving of the desired geometric formation either within the existence or absence of obstacles. I. Introduction In recent decenniums, unmanned aerial vehicles (UAVs) have charged a mature concern with their success among achieving heaps of progress in several applications in each military and civilian scopes [1-3]. The UAV is characterized by its capacity to accomplish its assignments in alleged "D-cube" operations (Dull-Dangerous-Dirty) atmosphere with no risk for manned pilots resources [4, 5], easy to preserve and low worth. Therefore, UAV has attained growing concern from scientists, researchers, and engineers. UAVs may be thought about as a hopeful alternative for numerous pilotless military and civilian exercises [6-8]. The auspicious results of a single UAV in executing varied applications persuade the utilization of multiple UAVs cooperating collectively to meet the required tasks [9-11]. Cooperative UAVs guarantees the success of the desired missions with better performance compared with single UAV [12-15]. Certain strategies are needed for multiple cooperative UAVs to cooperate collectively to fulfill the required goals. These strategies which defined by the cooperative UAVs attributes are known as UAVs tactics. These tactics can be classified into the swarming, mission duty, structure rearrangement, and active blockade [16, 17]. Formation rearrangement is outlined by the power of the multiple cooperative UAVs to preserve a desired geometric structure [12], and reconfigure to a different formation per the surrounding circumstances guaranteeing the success of the required application [16, 18]. Each member in the cooperative UAV troop must respect Reynold's rules of flocking during its formation [19-21]. Each UAV member has to match its velocity and separating distance from its neighbors and avoid colliding with its neighbors or obstacles [22]. There are several varieties of controls in the formation rearrangement of multiple cooperative UAVs domain. The scope of these control techniques steadily growing fast last decade including hybrid ...
Formation reconfiguration is one of the most important tactics used in the field of cooperative Unmanned Air Vehicles. In this paper, formation reconfiguration for a team of vertical takeoff and landing quadrotors is managed by a classical approach of proportional-integral-derivative (PID) controller. PID controller is designed to regulate the attitude and the altitude for every quadrotor of a cooperative team respecting the separating span and velocity constraints. PID controller results are compared with a backstepping controller developed for the same system. The mathematical model of the propositioned system is derived initially, and then a PID controller using simplex and genetic algorithms is designed qualifying the cooperative quadrotors to track the desired trajectories. Simulation results present the assessment of PID control strategy along with backstepping control strategy in different scenarios including proposal flight mission in obstacle-free surroundings, and obstacle-laden surroundings. Noise attenuation and disturbance rejection are examined for both controllers to check the robustness of the system.
High precision control is desirable for future weapon systems. In this paper, several control design methodologies are applied to a weapon system to assess the applicability of each control design method and to characterize the achievable performance of the gun-turret system in precision control. The design objective of the gun-turret control system is to achieve a rapid and precise tracking response with respect to the turret motor command from the fire control system under the influences of disturbances, nonlinearities, and modeling uncertainties. A fuzzy scheme is proposed for control of multi-body, multi-input and multioutput nonlinear systems with joints represented by a gun turret-barrel model which consists of two subsystems: two motors driving two loads (turret and barrel) coupled by nonlinear dynamics. Fuzzy control schemes are employed for compensation and nonlinear feedback control laws are used for control of nonlinear dynamics. Fuzzy logic control (FLC) provides an effective means of capturing the approximate, inexact nature of the real world, and to address unexpected parameter variations and anomalies. Viewed in this perspective, the essential part of the FLC is a set of linguistic control rules related by the dual concepts of fuzzy implication and the compositional rule of inference. In essence, the FLC provides an algorithm which can convert the linguistic control strategy based on expert knowledge into an automatic control strategy. Accordingly, the design must be robust, adaptive, and, hopefully, intelligent in order to accommodate these uncertainties. Simulation results verify the desired system tracking performance.
This paper presents an approach to an Advanced Fast Disturbance Rejection Proportional-Integral (AFDR-PI) control system based on a benchmark plant model description. The controller is designed for a balance between performance (response time) and robustness (stability margins). The proposed algorithm associated with the mathematical model has been carried out under MATLAB/SIMULINK environment. A comparison between the Proportional-Integral (PI) controller and AFDR-PI controller has been introduced with applied step input disturbance to the input of the control system. The conclusion is that the AFDR-PI controller has the same tracking performance (time response requirements) as the PI controller, and succeeds to reject the disturbance signal higher than the PI controller (robustness requirements).
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