Reliability and sensitivity analysis of loop-designed security and stability control system in interconnected power systems

Security and stability control system(SSCS)in power systems involves collecting information and sending the decision from/to control stations at different layers;the tree structure of the SSCS requires more levels.Failure of a station or channel can cause all the execution stations(EXs)to be out of control.The randomness of the controllable capacity of the EXs increases the difficulty of the reliability evaluation of the SSCS.In this study,the loop designed SSCS and reliability analysis are examined for the interconnected systems.The uncertainty analysis of the controllable capacity based on the evidence theory for the SSCS is proposed.The bidirectional and loop channels are introduced to reduce the layers and stations of the existing SSCS with tree configuration.The reliability evaluation and sensitivity analysis are proposed to quantify the controllability and vulnerable components for the SSCS in different configurations.By aiming at the randomness of the controllable capacity of the EXs,the uncertainty analysis of the controllable capacity of the SSCS based on the evidence theory is proposed to quantify the probability of the SSCS for balancing the active power deficiency of the grid.

Robust control of a class of non-affine nonlinear systems by state and output feedback

Robust control design is presented for a general class of uncertain non-affine nonlinear systems. The design employs feedback linearization, coupled with two high-gain observers: the first to estimate the feedback linearization error based on the full state information and the second to estimate the unmeasured states of the system when only the system output is available for feedback. All the signals in the closed loop are guaranteed to be uniformly ultimately bounded(UUB) and the output of the system is proven to converge to a small neighborhood of the origin. The proposed approach not only handles the difficulty in controlling non-affine nonlinear systems but also simplifies the stability analysis of the closed loop due to its linear control structure. Simulation results show the effectiveness of the approach.

Controlling Entropic Uncertainty in the Presence of Quantum Memory by Non-Markovian Effects and Atom-Cavity Couplings

Based on the time-convolutionless master-equation approach, the entropic uncertainty in the presence of quantum memory is investigated for a two-atom system in two dissipative cavities. We find that the entropic uncertainty can be controlled by the non-Markovian effect and the atom-cavity coupling. The results show that increasing the atom-cavity coupling can enlarge the oscillating frequencies of the entropic uncertainty and can decrease the minimal value of the entropic uncertainty. Enhancing the non-Markovian effect can reduce the minimal value of the entropic uncertainty. In particular, if the atom-cavity coupling or the non-Markovian effect is very strong, the entropic uncertainty will be very dose to zero at certain time points, thus Bob can minimize his uncertainty about Alice＇s measurement outcomes,

Robust adaptive control for dynamic systems with mixed uncertainties

The control problem for single-input single-output（SISO） systems in the presence of mixed uncertainties, both stochastic and deterministic uncertainties, is considered. The stochastic uncertainties are modeled as exogenous noises, while the deterministic uncertainties are time invariant and appear as the unknown parameters which lie in a bounded interval. Based on a subdivision for the continuous interval, a robust adaptive controller is designed. The controller can not only realize the system output to track the desired output, but also learn a more accurate interval which contains the true value of the unknown parameter with a learning error given in advance. An example is given finally to demonstrate the effectiveness of the proposed method.

Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance

The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously developed tools,including the norm-bounding technique for relaxing the disturbance-related constraint handling,the dynamic output feedback law,the notion of quadratic boundedness for specifying the closed-loop stability,and the ellipsoidal state estimation error bound for guaranteeing the recursive feasibility,are merged in the control design.Some previous approaches are shown to be the special cases.An example of continuous stirred tank reactor(CSTR) is given to show the effectiveness of the proposed approaches.

Robust H_∞ directional control for a sampled-data autonomous airship

A robust H∞ directional controller for a sampled-data autonomous airship with polytopic parameter uncertainties was proposed. By input delay approach, the linearized airship model was transformed into a continuous-time system with time-varying delay. Sufficient conditions were then established based on the constructed Lyapunov-Krasovskii functional, which guarantee that the system is mean-square exponentially stable with H∞ performance. The desired controller can be obtained by solving the obtained conditions. Simulation results show that guaranteed minimum H∞ performance γ=1.4037 and fast response of attitude for sampled-data autonomous airship are achieved in spite of the existence of parameter uncertainties.

A Robust Model Following Control for Plant withUncertain Parameters Based on Genetic Algorithm

In this paper, an integral-type robust mode following control for Plants with uncertainparameters and nonlinear factors is introduced. An genetic algorithm is also designed for obtaining thecontrol gains. Finally, some numerical examples are provided to illustrate the validity and efficiency ofthe proposed method.

Modeling and Robust Discrete LQ Repetitive Control of Electrically Driven Robots

Discrete linear quadratic control has been efciently applied to linear systems as an optimal control.However,a robotic system is highly nonlinear,heavily coupled and uncertain.To overcome the problem,the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks.This modeling motivates us to develop an optimal repetitive control.The contribution of this paper is twofold.For the frst time,it presents discrete linear quadratic repetitive control for electrically driven robots using the mentioned model.The proposed control approach is based on the voltage control strategy.Second,uncertainty is efectively compensated by employing a robust time-delay controller.The uncertainty can include parametric uncertainty,unmodeled dynamics and external disturbances.To highlight its ability in overcoming the uncertainty,the dynamic equation of an articulated robot is introduced and used for the simulation,modeling and control purposes.Stability analysis verifes the proposed control approach and simulation results show its efectiveness.