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A novel intelligent control strategy based on a brain emotional learning (BEL) algorithm is investigated in the application of the attitude control of a small unmanned aerial vehicle (UAV) in this study. The BEL model imitates the emotional learning process in the amygdalaorbitofrontal (A-O) system of mammalian brains. Here it is used to develop th...
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Citations
... Simulations demonstrate that using intelligent control system based on brain emotional learning may present better results in attitude control of UAVs than traditional methodology [71]. An intelligent control system based on ant colony and fuzzy self-adaptive PID control was proposed for mine hoists automation [53]. ...
Reconfigurable systems have evolved as a more comprehensive and better known area in the last years. Reconfigurability is strictly related to the ability to change: the more flexible a system is, the greater is its reconfigurability. Reconfiguration can provide the systems characteristics as self-adaptation, allowing their resources to be used according to the environment in which they are found and, consequently, extracting a better use of these resources. Unmanned Aerial Vehicles (UAVs), mine hoists, mobile robots, and balloon systems are some applications where self-adaptation and reconfiguration are important. Some reconfigurable systems are able to plan their reconfiguration at runtime, i.e., the system sets its new configuration while running. These systems are called Dynamically Reconfigurable Systems (DRSs). This paper aims to investigate DRSs seeking to answer four specific questions: (i) how the different kinds of DRSs are classified in the literature and what is their definitions; (ii) what are the hardware and software platforms, methodologies and techniques engaged in DRSs; (iii) what are the domains of application of DRSs; and, (iv) which countries lead the number of publications in DRSs. To do that, a systematic literature review was conducted, where, at the end, 85 articles between 1995 and 2017 were completely read.
... In recent years, some advanced control theories are gradually introduced into the design of attitude control system for UAVs with the development of computer technology [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In [1], the output feedback control method was used to design the attitude control system for UAV. ...
... The multiobjective genetic algorithm is used to mechanize the optimal determination of fuzzy logic controller parameters based on an efficient cost function that comprises undershoot, overshoot, rise time, settling time, steady state error, and stability. In [10,11], a novel intelligent control strategy based on a brain emotional learning (BEL) algorithm was investigated in the application of attitude control of UAV. Time-delay phenomenon and sensor saturation are very common in practical engineering control and is frequently a source of instability and performance deterioration [18][19][20]. ...
Attitude dynamic model of unmanned aerial vehicles (UAVs) is multi-input multioutput (MIMO), strong coupling, and nonlinear. Model uncertainties and external gust disturbances should be considered during designing the attitude control system for UAVs. In this paper, feedback linearization and model reference adaptive control (MRAC) are integrated to design the attitude control system for a fixed wing UAV. First of all, the complicated attitude dynamic model is decoupled into three single-input single-output (SISO) channels by input-output feedback linearization. Secondly, the reference models are determined, respectively, according to the performance indexes of each channel. Subsequently, the adaptive control law is obtained using MRAC theory. In order to demonstrate the performance of attitude control system, the adaptive control law and the proportional-integral-derivative (PID) control law are, respectively, used in the coupling nonlinear simulation model. Simulation results indicate that the system performance indexes including maximum overshoot, settling time (2% error range), and rise time obtained by MRAC are better than those by PID. Moreover, MRAC system has stronger robustness with respect to the model uncertainties and gust disturbance.
A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is non-model based and has a simplified fuzzy neural network structure and a novel bidirectional brain emotional learning (BBEL) algorithm. It is applied to control all six degrees-of-freedom (6DOF) of a QUAV for accurate trajectory tracking and to handle the payload uncertainties in realtime. The trajectory tracking performance and the ability to handle the payload uncertainties are experimentally demonstrated on an QUAV. The experimental results show a superior performance and rapid adaptation capability of the proposed BFBEL controller. The proposed BFBEL controller can be used for the commercial drone applications
Recently, a computational model of Amygdala based on the brain emotional learning is presented by psychologists. This brain emotional learning model (BELM) that has a neuro‐inspired architecture is utilized to train the weights which are in Amygdala and Orbitofrontal. In this paper, unknown parameters of dynamic systems are estimated by developing the normalized BELM (NBELM). To this end, after proving the stability of the model output, the sufficient condition for weights convergence is extracted while the sensitivity analysis is applied for this model. In order to evaluate the performance of NBELM, in the first example, the matrices of a twin rotor MIMO system are estimated and compared with the equation error method (EEM). In the second example, the nonlinear model of a servomotor is utilized as a case study. In the third example, the performance of the NBELM in experimental systems is validated using a reaction wheel with a DC motor. An important feature of the brain emotional system is its fast response, leading the NBELM to have a high speed performance in estimating the parameters of dynamic systems. A few number of adjustable parameters and low computing complexity also cause the NBELM to be an appropriate method for online estimation of the unknown parameters of dynamic systems.
To date unmanned aerial system (UAS) technologies have attracted more and more attention from countries in the world. Unmanned aerial vehicles (UAVs) play an important role in reconnaissance, surveillance, and target tracking within military and civil fields. Here one briefly introduces the development of UAVs, and reviews its various subsystems including autopilot, ground station, mission planning and management subsystem, navigation system and so on. Furthermore, an overview is provided for advanced design methods of UAVs control system, including the linear feedback control, adaptive and nonlinear control, and intelligent control techniques. Finally, the future of UAVs flight control techniques is forecasted. ©, 2015, Nanjing University of Aeronautics an Astronautics. All right reserved.
