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This article is devoted to the global output‐feedback stabilization of nonlinear systems with unknown control directions and unmeasured states dependent growth whose rate is an unknown arbitrary function of output. In view of limited capability of continuous feedback in compensating system uncertainties and nonlinearities, a novel switching adaptive scheme is proposed. The scheme involves a logic‐based switching mechanism, based on which, a proper controller is selected (from a family of candidate controllers) to act on the system during its execution interval. The main differences between the proposed scheme and those in the related works are as follows. First, the set of candidate controllers is determined online, rather than specified in advance, which provides foundations for compensating arbitrary function‐of‐output growth rate. Second, an iterative function composed of system memory signals is introduced into the switching mechanism, aiming to evaluate the differences between the system states and observe states. Finally, when switching stops, only by introducing an extra transformation and adopting elaborate analysis, can the boundedness of the closed‐loop system signals be obtained, rather than those in the related works where the boundedness can be naturally established once switching stops. The constructed switching adaptive controller guarantees that all the signals of the resulting closed‐loop system are bounded and ultimately converge to zero. [ABSTRACT FROM AUTHOR]

Economic dispatch can promote the economic operation of power systems, and reactive power dispatch can ensure the safe and reliable operation of power systems. Essentially different from the related literature with separately optimizing the two dispatches, this paper proposes the collaborative optimization strategy of dynamic grid dispatch to optimize economic dispatch and reactive power dispatch simultaneously. The strategy is devoted to analyzing the internal relationship of the two dispatches, and establishing a collaborative optimization model to complete the collaborative optimization of the two dispatches. Moreover, multi-objective hybrid bat algorithm is improved by an unbalanced power distribution method, which is suitable to solve dynamic grid dispatch problem. Finally, wind power predicted by neural network is integrated in the collaborative optimization of dynamic grid dispatch, and the influence of wind power integration on grid dispatch is studied. The numerical examples on the IEEE 30-bus are provided to demonstrate the superiority of the proposed strategy. [ABSTRACT FROM AUTHOR]