Backlash Fault Suppression Using LQ Optimal Control Based RST Controllers in Wind Turbine Systems Using Bond Graphs and Matlab/Simulink

The presence of backlash in wind turbines is a source of limitations as it introduces nonlinearities that reduce their efficiency in speed/torque control which affect the performance of the power quality. Because of production tolerances during rotation, the teeth contact is lost for a small angle; until it is re-established, it produces a backlash phenomenon. The desire to eliminate this phenomenon is often hard to realise due to the nonlinear dynamic behaviour, which arises with the presence of backlash fault in a system. Therefore, the goal of this study is to develop an LQ optimal control structure in a form of an R-S-T controller in order to reduce the disturbing torque transmitted inside the dead zone of a gearbox in the wind turbine system. The actual system is also developed to be used as a demonstration model at lectures or presentations. The efficacy of the proposed control is illustrated via simulations.

Constrained Dynamic Mean-Variance Portfolio Selection in Continuous-Time

This paper revisits the dynamic MV portfolio selection problem with cone constraints in continuous-time. We first reformulate our constrained MV portfolio selection model into a special constrained LQ optimal control model and develop the optimal portfolio policy of our model. In addition, we provide an alternative method to resolve this dynamic MV portfolio selection problem with cone constraints. More specifically, instead of solving the correspondent HJB equation directly, we develop the optimal solution for this problem by using the special properties of value function induced from its model structure, such as the monotonicity and convexity of value function. Finally, we provide an example to illustrate how to use our solution in real application. The illustrative example demonstrates that our dynamic MV portfolio policy dominates the static MV portfolio policy.

Suppression of ground resonance in rotorcraft using active landing gear

This article examines the fundamental aspects of controlling ground resonance in rotorcraft equipped with actively controlled landing gear. Ground resonance is a mechanical instability affecting rotorcraft on the ground. It occurs at certain rotor speeds, where the lead–lag motion of the rotor couples with the motion of fuselage creating a self-excited oscillation. Typically, passive or semi-active lag dampers are used to avoid instability; however, these are undesirable from a design and maintenance perspective. Innovations in active landing gear for rotorcraft, such as articulated robotic legs, have provided an alternate approach to avoid the instability, eliminating the need for lag dampers with respect to ground resonance. This article extends classic ground resonance to include movable landing gear and identifies key physical parameters affecting dynamic behavior. Applying LQ optimal control to this model, it is shown that ground resonance instability can be eliminated using active landing gear as the control mechanism, even when there is no lag damping present in the rotor. In addition, while superior performance is achieved when landing gear movement can occur both longitudinally and laterally, it is still possible to stabilize ground resonance with inputs in a single direction, albeit with reduced performance.

Linear-Quadratic Stackelberg Game for Mean-Field Backward Stochastic Differential System and Application

This paper is concerned with a new kind of Stackelberg differential game of mean-field backward stochastic differential equations (MF-BSDEs). By means of four Riccati equations (REs), the follower first solves a backward mean-field stochastic LQ optimal control problem and gets the corresponding open-loop optimal control with the feedback representation. Then the leader turns to solve an optimization problem for a 1×2 mean-field forward-backward stochastic differential system. In virtue of some high-dimensional and complicated REs, we obtain the open-loop Stackelberg equilibrium, and it admits a state feedback representation. Finally, as applications, a class of stochastic pension fund optimization problems which can be viewed as a special case of our formulation is studied and the open-loop Stackelberg strategy is obtained.