This paper investigates the problem of finite-time stabilization for a class of high-order nonlinear systems with zero dynamic. The systems under investigation allow inherent nonlinearities to include high-order and low-order nonlinear growth rates, except for unmeasurable dynamic uncertainties. On basis of the constructions of integral Lyapunov functions with sign functions and input-to-state stability property, a partial state feedback stabilizer is proposed to guarantee that the convergence of system state is faster than traditional finite-time stabilizers. In particular, the convergent time can be adjusted through preassigning design parameters. The novelty lies in a distinct perspective to applying fast finite-time stability in partial state feedback control design which was previously regarded as a rather difficult problem.
In ship motion control process, it is difficult to design ship controller due to the effects of environmental disturbances such as wind, waves, current, and unmodelled dynamics. In order to solve these problems, a nonlinear robust controller based on L2-gain disturbance rejection is proposed in this paper. To steer ships to the desired position, an error feedback control law on account of Lyapunov functions is designed. Then, to satisfy the L2-gain disturbance rejection, proper parameters are chosen based on the system dissipative property. In order to verify the performance of the proposed controller, the MATLAB simulation results in two situations which are without and with the effects of environmental disturbances are demonstrated.
In this paper, we are concerned with a parallel iterative method for nonlinear least-squares problems, which can be viewed as an alterative ordering of the variables in the Jacobian method, the variables are divided into non-disjoint groups. Furthermore, a parallel algorithm is proposed and performed on HP-SPP1600. The numerical results show that the algorithm has nice speedup and parallel efficiency.
We obtain an L p -Hardy inequality on the n-sphere and give the corresponding sharp constant.Furthermore, the obtained inequalities are used to derive an uncertainty principle inequality and some corollaries.The results generalize and improve some related inequalities in recent literature.
The magnetotelluric (MT) method has become more widely used in hydrocarbon exploration.The inversion of MT data, which can determine the electrical structure of subsurface, is a nonlinear and multimodal optimization problem.Particle swarm optimization (PSO) algorithm is a good solver for this geophysical inversion problem, whereas it has a shortage of heavy computation time.An improved parallel adaptive PSO inversion algorithm for MT data is proposed in order to decrease the computation time.The performance of the proposed algorithm was evaluated on the Dawn 4000L supercomputer using the synthetic MT data of 1D layered geo-electrical models of three and four layers.The numeric results show that the proposed algorithm can obtain the as good solution as the serious PSO inversion algorithm, and can reduce the computation time obviously when more computing nodes been employed.This result indicates that proposed improved parallel inversion algorithm can deal with the computation time problem and provide theory and technology support the MT data non-linear inversion based on PSO.
Summary This article investigates global finite‐time stabilization with prescribed output convergence for uncertain nonlinear systems. It is possible to achieve finite‐time stabilization via continuous adaptive feedback for the systems, but the transient performance could not be prescribed for such feedback. Although funnel control is capable of tackling serious uncertainties and ensuring prescribed performance (such as convergence rate and overshoot), it alone cannot achieve (global) finite‐time stabilization. To this end, we present an integrated controller to achieve the desirable stabilization, retaining the respective advantages of the above two schemes and circumventing their own disadvantages. Specifically, based on the funnel control scheme with suitable design parameters, the system output converges from any initial value to an arbitrarily adjustable region with the prescribed convergence rate before a pregiven time instant, while the system states keep bounded. The adaptive controller, in conjunction with a time‐dependent function, ensures that the system output and states converge to zero in finite time, and particularly the system output with prescribed convergence rate evolves within the prespecified envelope. The effectiveness of the proposed scheme is illustrated by two simulation examples.
As a tool to monitor marine environments and to perform dangerous tasks instead of manned vessels, unmanned surface vehicles (USVs) have extensive applications. Because most path planning algorithms have difficulty meeting the mission requirements of USVs, the purpose of this study was to plan a global path with multiple objectives, such as path length, energy consumption, path smoothness, and path safety, for USV in marine environments. A global path planning algorithm based on an improved quantum ant colony algorithm (IQACA) is proposed. The improved quantum ant colony algorithm is an algorithm that benefits from the high efficiency of quantum computing and the optimization ability of the ant colony algorithm. The proposed algorithm can plan a path considering multiple objectives simultaneously. The simulation results show that the proposed algorithm’s obtained minimum was 2.1–6.5% lower than those of the quantum ant colony algorithm (QACA) and ant colony algorithm (ACA), and the number of iterations required to converge to the minimum was 11.2–24.5% lower than those of the QACA and ACA. In addition, the optimized path for the USV was obtained effectively and efficiently.
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