جستجوی مقالات مرتبط با کلیدواژه "adaptive control" در نشریات گروه "ریاضی"

This paper presents an optimal robust adaptive technique for controlling a certain class of uncertain nonlinear affine systems‎. ‎The proposed approach combines sliding mode control‎, ‎a linear quadratic regulator for optimality, and gradient descent as an adaptive controller‎. ‎ The convergence of the sliding mode control process is proven using two theorems based on the Lyapunov function. Simulation results for pendulum and inverted pendulum systems demonstrate that the proposed method outperforms both the linear quadratic regulator technique and ‎the‎ ‎sliding‎ ‎mode‎ ‎control regarding reduced chattering and improved reaching time‎.

This paper introduces a novel adaptive nonsingular fast terminal sliding mode approach that benefits from an interval type-2 fuzzy logic estimator and a gain for control and synchronization of chaotic systems in the presence of uncertainty. The nonsingular fast terminal sliding mode controller is developed to increase the convergence rate and remove the singularity problem of the system. Using the proposed method, the finite-time convergence has been ensured. To eliminate the chattering phenomenon in the conventional sliding mode controller, the discontinuous sign function is estimated using an interval type-2 fuzzy inference system (FIS) based on the center of sets type reduction followed by defuzzification. By adding the proportionate gain to the interval type-2 FIS, the robustness and speed of the controller system is enhanced. An appropriate Lyapunov function is utilized to ensure the closed-loop stability of the control system. The performance of the controller is evaluated for a nonlinear time-varying second-order magnetic space-craft chaotic system with different initial conditions in the presence of uncertainty. The simulation results show the efficacy of the proposed approach for the tracking control problems. The time and frequency domain analysis of the control signal demonstrates that the chattering phenomenon is successfully diminished.

‎In this paper‎, ‎we investigate an improved method for stabilizing a class of uncertain chaotic nonlinear dynamical system‎. ‎Our approach follows techniques of optimal principle for time-delayed feedback control and adaptive tracking control theory for stabilizing unstable periodic orbits in a chaotic bounded attractor‎. ‎The uncertain parameters expressed in the system can be separated‎. ‎Analysis and proof are presented using the Lyapunov stability theorem‎. ‎In particular‎, ‎we use the adaptive control theory to design an adaptive law for the estimation of uncertain time-delayed controlled chaotic nonlinear dynamical systems‎. ‎The predictions are presented by numerical simulation through the Rossler system to demonstrate theoretical results‎.

Drones are among the most valuable and versatile technologies in the world‎, ‎with applications in a vast number of ‎‎‎fields such as traffic control‎, ‎agriculture‎, ‎firefighting and‎ ‎rescue‎, ‎and filmmaking‎, ‎to name a few‎. ‎As the development of unmanned aerial vehicles (UAVs) accelerates‎, ‎the‎ ‎safety of UAVs becomes increasingly important‎. ‎In this paper‎, ‎a robust adaptive controller is designed to improve the safety of a hexa-rotor UAV‎, ‎and a robust adaptive controller is developed to control our system‎. ‎In doing so‎, ‎the wind parameters from the aerodynamic forces and moments acting on the hexa-rotor are estimated using an observer with the adaptive algorithm‎. ‎This proposed controller guarantees stability and reliable function in the midst of parametric and non-parametric uncertainties‎. ‎The process's global stability and tracking convergence are investigated using the Lyapunov theorem‎. ‎The performance and effectiveness of the proposed controller are tested through two simulation studies‎, ‎which take into account external disturbances that are a function of time.

This paper studies the fixed-time synchronization problem of fuzzy stochastic cellular neural networks (FSCNNs) with discrete and distributed delay. Compared with the finite-time synchronization in the existing literature, the fixed-time synchronization of FSCNNs is studied for the first time, and the convergence time obtained does not depend on the upper bound of the initial value of the system. In addition, two kinds of control are designed, one is feedback control and the other is adaptive control. Besides, it is the first time to achieve fixed-time synchronization of FSCNNs via adaptive control. Finally, two numerical examples are also proposed to illustrate the practicability and validity of the results we proposed.

In this paper, we introduce a new hyperchaotic complex T-system. This system has complex nonlinear behavior which we study its dynamical properties including invariance, equilibria and their stability, Lyapunov exponents, bifurcation, chaotic behavior and chaotic attractors as well as necessary conditions for this system to generate chaos. We discuss the synchronization with certain and uncertain parameters via adaptive control. For synchronization, we use less controllers than the dimension of the proposed system. Also, we prove that the error system is asymptotically stable by using a Lyapunov function. Numerical simulations are computed to check the analytical expressions.