As wind energy is sustainable, pollution-free, easily available, and free of cost, it has become an efficient source of renewable energy for electricity generation. But, the problem with wind energy is that it varies with time, seasons, and location. This makes the Wind Energy Conversion System (WECS) unstable as it frequently needs to match the load demands. The balance in power generation by wind energy is essential since it has to be connected to various grids. So, this unbalanced energy production can affect the stability of the associated power grids as well. It also results in expensive regulatory measures, storage options, and load shedding. So, the stable operation of the WECS is highly essential to adapt it as a trustable source of electricity production. The stable operation of the WECS requires a robust and advanced system for control. Better control of the wind power extracting model is achieved by controlling the Maximum Power Point Tracking (MPPT) and blade pitch. So, an Adaptive MPPT and Blade Pitch Controller (BPC) for the WECS have been developed in this article, with the support of a hybrid optimization algorithm. In order to enhance the working principles of this controller, two effective algorithms such as Sand Cat Swarm Optimization (SCSO) and Galactic Swarm Optimization (GSO) are integrated and named Hybrid Sand Cat Galactic Swarm Optimization (HSC-GSO). With the help of the recommended HSC-GSO, the functionality of the controller is enhanced and also at the same time this algorithm helps to optimize the three gains in the Proportional Integral Differential (PID) controller of both MPPT and BPC, respectively. Moreover, with the support of the proposed HSC-GSO the damping oscillations in the WECS output power and voltage are minimized. In the end, the numerical analysis is conducted for the presented system by comparing it with the traditional techniques. From the overall result analysis, the stability of the recommended adaptive WECS is 97, which is higher than the conventional algorithms such as DHOA, SCSO, GSO, and DA. Thus, it has been proved that the proposed HSC-GSO algorithm for the parameters optimization in the PID controller of MPPT and the PID controller of BPC attains high robustness, increased steady-state stability, and efficient transient response than the traditional techniques.
The thesis studies a stochastic finite-time bounded (SFTB) filter design method for time-varying delay Markov jump systems (MJSs) with partially unknown transition rates. First, a Lyapunov–Krasovskii functional with triple integral is built, the free weight matrix is added for lowering the conservatism. And the condition of SFTB of the error system is analyzed. Then, according to linear matrix inequalities (LMIs), a new filter design method is presented. Moreover, the dimension of the filter obtained is free, and the filter ensures that the error system is SFTB. Finally, the validity and effectiveness of the conclusion of this paper are demonstrated by simulation examples.
In this article, for non-parameterized nonlinear continuous (NPNC) multiple-input multiple-output (MIMO) systems, two combined iteration-domain and time-domain adaptive iterative learning control (ILC) algorithms are proposed to track iteration-varying reference trajectories repetitively over a finite time interval. Different from the general requirement in adaptive control community that the control gain matrices of the controlled systems are real symmetric and positive-definite, only the nonsingular property of the control gain matrices is assumed. Moreover, there are just two adaption parameters and one adaption parameter involved in the proposed two adaptive ILC algorithms respectively such that the computation load and memory-space are greatly saved. A simulation example is utilized to illustrate the effectiveness of the two proposed adaptive ILC algorithms with less adaption parameters.
This article considers the problem of edge-based event-triggered output feedback control for linear multi-agent systems (MASs) with state-independent process and measurement noise under undirected communication topologies. The main breakthroughs are the design of distributed event-triggered output feedback control strategy and the stochastic stability analysis. Toward this, first, according to Kalman filtering theory, An observer is contrasted to optimally estimate the state of each agent. Next, a novel edge-based event-triggered mechanism (EBETM) is designed to reduce the communication frequency among agents effectively, and a positive interval between events is enforced in EBETM, which can eliminate the Zeno behavior. Then, stability of the estimation error and the consensus error systems is analyzed, the execution error is estimated and the almost sure consensus is achieved. Finally, a numerical example is given to show that MASs achieves almost sure consensus and there is a positive interval between the events of all edges.
In this article, for switched nonlinear time-delay multi-agent systems (MASs) under directed graphs, an adaptive event-triggered (ET) consensus problem is investigated. Each agent's switching signal is allowed to be arbitrary and asynchronous. To save communication resources, an adaptive ET consensus strategy is designed. In addition, the Pade approximation approach is introduced to transform the system into an input delay-free system. A Lyapunov–Krasovskii functional is employed to analyze the effect of time-varying delays on system stability. It is proved that the consensus errors of switched NMASs are bounded, and the Zeno behavior does not exist. Finally, the presented strategy's effectiveness is verified by simulation results.
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