Quadrotor unmanned aerial vehicles (UAVs) have been highly regarded in various military, research, scientific and entertainment fields due to their vast applications. In this paper, the design control of the direct reference model for updating the control parameters in the presence of noise and disturbance has been analyzed to find the optimal and desired amount in these micro-UAVs. Then, by choosing a Lyapunov function, the systematic stability of the system is examined and after proof of stability, an adaptive law has been extracted to update the systemic parameters. The simulations results show the controller proper function to reduce the error. Utilizing the mean square error method, a comparison was made between the results of the proposed scheme and the results contained in another references, which shows an improvement in controller performance of the proposed scheme.

Unfalsified adaptive control is a new approach in supervisory control that ensures the selection of a stabilizing controller from a control set based on the system input-output data. A prerequisite for ensuring stability is the existence of a pre-designed controller set that contains a stabilizing controller. The supervisor selects the controller based on the cost function calculated with the system input-output data. In this method, the control system performance is restricted to the controllers of the control set. In this paper, the controller set update is performed by introducing the concept of performance falsification along with the stability falsification of the active controller. To falsify the performance of the controller set, the structure of the model reference is proposed to evaluate the performance of the control system. In case of performance falsification, a new controller is designed and added to the controller set based on system data and without using any model. To design the controller, a linear matrix inequality problem is solved. In this paper, no system model is used, and the presented method is completely model-free and data-oriented. The simulation results show the performance improvement of the proposed method compared to other methods in a standard robust adaptive benchmark system.

The nonlinear nature and model uncertainties in electric power system show the importance of designing a suitable controller to operate under different operating conditions. In this paper, the problem of designing controller for UPFC is carried out with two objectives of easy implementation and operation under different loading conditions. In this regard, model reference adaptive system is proposed for UPFC control and this MRAS is improved by using PSO method. Besides, in order to limitation of input signal and also robustness, the normalization technique is used. The performance of the proposed algorithm is studied under nominal and heavy loading conditions and simulation results are presented to show robust performance of the proposed method. Also, the results of the proposed algorithm are compared with a PI type controller which is tuned by using PSO. Simulation results show the ability and effectiveness of the proposed method in comparison with the classical method.

This article presents the design of an adaptive Proportional-Integral-Derivative (PID) controller for a Mass-Spring-Damper mechanical system. The modeling of the system is conducted using Newton's second law, from which the transfer function is derived. Given the uncertainties associated with the system parameters, an adaptive method based on a reference model is employed to adjust the controller coefficients. The adaptation laws are formulated based on the MIT rule. The results from simulations and applications of the designed controller demonstrate its effectiveness in tracking the reference input. Under both nominal conditions and in the presence of uncertainties, the system output follows the reference input without steady-state error and at an appropriate speed

The noisy environment of the underwater nonlinear under-actuated dynamic system of the Autonomous Underwater Vehicle (AUV) turns out the design of the self-tuning controller, more challenging. In this paper, the Model Reference Adaptive Control (MRAC) along with the Artificial Neural Network (ANN) compensator of the 6 Degree of Freedom (DOF) AUV is illustrated.4 Input-6 Output (4I6O) nonlinear under-actuated dynamic system is divided into first, 4 subsystems and the partial or inverse linearization technique and the coupled linearized model are employed for each one. The stability of the closed- loop subsystems, and hence the complete controlled model is insured according to the Lyapounve's stability theory. To increase the robustness of the closed loop system, an ANN compensator benefiting online backpropagation learning algorithm to tune the network's parameters is incorporated with each controllers. The results of the simulations of the hybrid MRAC along with ANN compensator in Matlab Simulink environment, clearly indicates the outperformance of the ANN compensated control method versus its non-ANN compensated counterpart in terms of increasing the robustness as well as more accurate trajectory tracking performance of the control system subjected to the continual applied noises for both coupled and decoupled dynamical systems.

In current study, an integrated guidance and control system is assessed for a projectile having dual spin motion in the cascade control structure. In this way, a nonlinear seven degrees of freedom model is used and coupling effects of pitch and yaw channels is taken into consideration. Based on cascaded control structure, a three-loops controller is designed, which in inner and middle loops the dynamic inversion method is used, and in the outer loop, an adaptive model based on PID controller is designed. The dynamic pressure contribution in acceleration command is also taken into account. The performance of the designed guidance and control system is assessed in presence of uncertainties via Monte Carlo statistical simulations. The results of suggested algorithm are compared with that of a cascade control having a classic PID controller in its outer loop. The results show the superior performance of the proposed algorithm in achieving the target and control effort.

Due to instability and specific configuration of tailless aircraft, there should be a controller with capability of stabilizing the aircraft in various maneuvers and flight conditions and also to have desired robustness against different parametric and non-parametric uncertainties. In this paper, we design a multi input-multi output combined model-reference adaptive controller for a tailless aircraft. Coordinated turn is itself an unstable maneuver. The addition of the instability of this maneuver and the instability of a tailless aircraft causes a highly unstable situation. Combined model-reference adaptive control benefits the aggregation of the tracking error and prediction error. Combining these two sources of errors alleviates the transient response characteristics, and a combined model-reference adaptive controller has better performance compared to the classical model-reference adaptive controllers (with tracking error as the only source of parameter estimation error), and this property could be useful in highly unstable systems like tailless aircraft. Here, after extracting the equations of motion of an aircraft and exerting the conditions of coordinated turn maneuver, we design a combined model-reference adaptive controller for a tailless aircraft. The simulation results show accuracy of the designed controller in stabilizing the aircraft during the coordinated turn maneuver and its robustness against uncertainties.