Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 5 Articles
This paper has numerically studied the dynamical behaviors of a fractional-order\nsingle-machine infinite-bus (FOSMIB) power system. Periodic motions, period doubling\nbifurcations and chaotic attractors are observed in the FOSMIB power system.\nThe existence of chaotic behavior is affirmed by the positive largest Lyapunov\nexponent (LLE). Based on the fractional-order backstepping method, an adaptive\ncontroller is proposed to suppress chaos in the FOSMIB power system. Numerical\nsimulation results demonstrate the validity of the proposed controller....
We focus on distributed model predictive control algorithm. Each distributed model predictive controller communicates with the\nothers in order to compute the control sequence. But there are not enough communication resources to exchange information\nbetween the subsystems because of the limited communication network. This paper presents an improved distributed model\npredictive control scheme with control planning set. Control planning set algorithm approximates the future control sequences\nby designed planning set, which can reduce the exchange information among the controllers and can also decrease the distributed\nMPC controller calculation demand without degrading the whole system performance much.The stability and system performance\nanalysis for distributed model predictive control are given. Simulations of the four-tank control problem and multirobot multitarget\ntracking problem are illustrated to verify the effectiveness of the proposed control algorithm....
Bottom-fixed vertical rotating devices are widely used in industrial and civilian fields.\nThe free upside of the rotor will cause vibration and lead to noise and damage during operation.\nMeanwhile, parameter uncertainties, nonlinearities and external disturbances will further deteriorate\nthe performance of the rotor. Therefore, in this paper, we present a rotor orientation control\nsystem based on an active magnetic bearing with L1 adaptive control to restrain the influence\nof the nonlinearity and uncertainty and reduce the vibration amplitude of the vertical rotor.\nThe boundedness and stability of the adaptive system are analyzed via a theoretical derivation.\nThe impact of the adaptive gain is discussed through simulation. An experimental rig based on\ndSPACE is designed to test the validity of the rotor orientation system. The experimental results show\nthat the relative vibration amplitude of the rotor using the L1 adaptive controller will be reduced\nto âË?¼50% of that in the initial state, which is a 10% greater reduction than can be achieved with\nthe nonadaptive controller. The control approach in this paper is of some significance to solve the\norientation control problem in a low-speed vertical rotor with uncertainties and nonlinearities....
This paper introduces the ELSS and analyses the structure and working principle of the ELSS. The numerical model of the\nelectric load simulation system is established in this paper. On the basis of it, an adaptive control system is designed to suppress the\nsurplus torque of the system. We obtain the adaptive laws of the system. Finally, through the numerical simulation results verify the\nvalidity of the control method....
This paper considers the artificial potential field method combined with rotational\nvectors for a general problem of multi-unmanned aerial vehicle (UAV) systems tracking\na moving target in dynamic three-dimensional environment. An attractive potential\nfield is generated between the leader and the target. It drives the leader to track the\ntarget based on the relative position of them. The other UAVs in the formation are\ncontrolled to follow the leader by the attractive control force. The repulsive force affects\namong the UAVs to avoid collisions and distribute the UAVs evenly on the spherical\nsurface whose center is the leader-UAV. Specific orders or positions of the UAVs are not\nrequired. The trajectories of avoidance obstacle can be obtained through two kinds of\npotential field with rotation vectors. Every UAV can choose the optimal trajectory to\navoid the obstacle and reconfigure the formation after passing the obstacle. Simulations\nstudy on UAV are presented to demonstrate the effectiveness of proposed\nmethod....
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