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Coupling characteristics of integrated mechanical-hydraulic-control systems for active hydro-suspension with uncertain and time-varying parameters make it difficult to achieve system-level optimal performances if only through physical or control system design. A novel collaborative design framework is proposed to optimize selected variables with objectives of structural lightweight, controllable suspension performances, and energy consumption. To improve ride/handling performances of active hydro-suspension under limited chatter space and allowable tire dynamic load, nonlinear filter barrier-Lyapunov-function-based backstepping upper controller is designed to generate target force under uncertain body weight, and projection-based adaptive backstepping sliding mode bottom controller is presented for valve current adjustment to drive asymmetric actuator precisely track required target force under time-varying fluid parameters. Based on designed hierarchical controller, physical/control collaborative design problem for system-level optimization is formulated by tailored optimal objective functions/constraints, independent and coupling design variables. The solution efficiency is improved through reduced calls of physical/control systems using response extreme difference sensitivity analysis, updated initial sets, and dynamic search interval for subsequent optimization. Finally, numerical simulation is presented to verify the effectiveness and benefits of the proposed collaborative optimization hierarchical control design method with eliminated conflicts between ride comfort and suspension deformation, improved control performances, better robustness, and lighter structure.

Adaptive exponential functional link neural network (AeFLNN) based on functional link architecture is a recently added member in the family of linear-in-parameter nonlinear filters. However, AeFLNN does not fulfill the criteria of universal approximate due to the absence of cross-terms in its functional expansion. Therefore, a new nonlinear filter based on even mirror Fourier nonlinear filters (EMFN) and exponentially varying sinusoidal basis functions named varying exponential EMFN (VeEMFN) is presented in this paper. To further improve the modeling accuracy, an independently varying exponential EMFN (IVeEMFN) filter is designed to allow each sinusoid in the basis function to grow or decay independently. A suitable update rule for updating the filter coefficients and exponential parameters are derived with the bounds on the learning rates is also presented. The simulation study demonstrates the enhanced modeling accuracy of the proposed filters.

Lately, an adaptive exponential functional link network (AEFLN) involving exponential terms integrated with trigonometric functional expansion is being introduced as a linear-in-the-parameters nonlinear filter. However, they exhibit degraded efficacy in lieu of non-Gaussian or impulsive noise interference. Therefore, to enhance the nonlinear modelling capability, here is a modified logarithmic hyperbolic sine cost function in amalgamation with the adaptive recursive exponential functional link network. In conjugation with this, a sparsity constraint motivated by a curvelet-dependent notion is employed in the suggested approach. Therefore, this paper presents an individually weighted modified logarithmic hyperbolic sine curvelet-based recursive exponential FLN (IMLSC-REF) for robust sparse nonlinear system identification. An individually weighted adaptation gain is imparted to several coefficients corresponding to the nonlinear adaptive model for accelerating the convergence rate. The weight update rule and the maximum criteria for the convergence factor are being further derived. Exhaustive simulation studies profess the effectiveness of the introduced algorithm in case of varied nonlinearity and for identifying as well as modelling the physical path of the acoustic feedback phenomenon of a behind-the-ear (BTE) hearing aid.

This study is devoted to a nonlinear filter-based adaptive fuzzy output-feedback control scheme for uncertain fractional-order (FO) nonlinear systems with unknown external disturbance. Fuzzy logic systems (FLSs) are applied to estimate unknown nonlinear dynamics, and a new FO fuzzy state observer based on a nonlinear disturbance observer is established for simultaneously estimating the unmeasurable states and mixed disturbance. Then, with the aid of auxiliary functions, a novel FO nonlinear filter is given to approximately replace the virtual control functions, together with the corresponding fractional derivative, which not only erases the inherent complexity explosion problem under the framework of backstepping, but also completely compensates for the effects of the boundary errors induced by the constructed filters compared to the previous FO linear filter method. Under certain assumptions, and in line with the FO stability criterion, the stability of the controlled system is ensured. An FO Chua–Hartley simulation study is presented to verify the validity of the proposed method.

In this work, an adaptive fuzzy backstepping fault-tolerant control (FTC) issue is tackled for uncertain fractional-order (FO) nonlinear systems with sensor and actuator faults. A fuzzy logic system is exploited to manage unknown nonlinearity. In addition, a novel FO nonlinear filter-based dynamic surface control (DSC) method is constructed, effectively avoiding the inherent complexity explosion problem in the backstepping recursive process, and in the light of the construction of auxiliary functions, compensating the coupling term introduced by faults. On account of certain assumptions, the stability criterion of the FO Lyapunov function is applied to guarantee the stability of the closed-loop system. Finally, the simulation example verifies the validity of the presented control strategy.

Voice activity detection (VAD) is a vital process in voice communication systems to avoid unnecessary coding and transmission of noise. Most of the existing VAD algorithms continue to suffer high false alarm rates and low sensitivity when the signal-to-noise ratio (SNR) is low, at 0 dB and below. Others are developed to operate in offline mode or are impractical for implementation in actual devices due to high computational complexity. This paper proposes the upper envelope weighted entropy (UEWE) measure as a means to enable high separation of speech and non-speech segments in voice communication. The asymmetric nonlinear filter (ANF) is employed in UEWE to extract the adaptive weight factor that is subsequently used to compensate the noise effect. In addition, this paper also introduces a dual-rate adaptive nonlinear filter (DANF) with high adaptivity to rapid time-varying noise for computation of the decision threshold. Performance comparison with standard and recent VADs shows that the proposed algorithm is superior especially in real-time practical applications.

A class of high-order nonlinear filter schemes by Yee et al. (J Comput Phys 150:199–238, 1999), Sjögreen and Yee (J Comput Phys 225:910–934, 2007), and Kotov et al. (Commun Comput Phys 19:273–300, 2016; J Comput Phys 307:189–202, 2016) is examined for long-time integrations of computational aeroacoustics (CAA) turbulence applications. This class of schemes was designed for an improved nonlinear stability and accuracy for long-time integration of compressible direct numerical simulation and large eddy simulation computations for both shock-free turbulence and turbulence with shocks. They are based on the skew-symmetric splitting version of the high-order central base scheme in conjunction with adaptive low-dissipation control via a nonlinear filter step to help with stability and accuracy capturing at shock-free regions as well as in the vicinity of discontinuities. The central dispersion-relation-preserving schemes as well as classical central schemes of arbitrary orders fit into the framework of skew-symmetric splitting of the inviscid flux derivatives. Numerical experiments on CAA turbulence test cases are validated.

We present a model of a stochastic observation system that allows for time delays between the received observation and the actual state of the observed object that formed these observations. Such delays can occur when observing the movement of an object in a water medium using acoustic sonars and have a significant impact on the accuracy of position tracking. We present equations to solve the optimal mean square filtering problem. Since the practical use of the optimal solution is barely feasible due to its computational complexity, we pay the main attention to an alternative, suboptimal but computationally efficient approach. Specifically, we adapted a conditional minimax nonlinear filter (CMNF) to the proposed model and formulated sufficient existence conditions for its estimate. We conducted a computational experiment on a model that is close to practical needs. The results of the experiment show the effectiveness of CMNF in the model considered. However, they also show a significant decrease in the quality of estimation compared to the model without random observation delays, which can be considered as a motivation for further research into the model and related problems.

This paper studies the real-time optimal state estimation-based feedback control for two-level stochastic quantum systems in the non-Markovian case. The system model is established by combining the time-convolutionless non-Markovian master equation and the stochastic master equation. A nonlinear filter based on the state-dependent Riccati equation is designed in order to achieve the real-time optimal estimation of quantum states. A quadratic function multiplied with an exponential term is selected as the Lyapunov function, and a continuous-time control law is deduced via the stochastic Lyapunov stability theorem to realize eigenstate feedback control based on real-time optimal state estimation. Numerical simulation results illustrate that the proposed control scheme is capable of steering the two-level quantum system from an arbitrary initial state to the desired eigenstate with a fidelity higher than 99% within a time of 3 a.u.

In this paper, a new variational Gaussian regression filter (VGRF) is proposed by constructing the linear parametric Gaussian regression (LPGR) process including variational parameters. Through modeling the measurement likelihood by LPGR to implement the Bayesian update, the nonlinear measurement function will not be directly involved in the state estimation. The complex Monte Carlo computation used in traditional methods is also avoided well. Hence, in PVFF, the inference of state posteriori and variational parameters can be achieved tractably and simply by using variational Bayesian inference approach. Secondly, a filtering evidence lower bound (F-ELBO) is proposed as a quantitative evaluation rule of different filters. Compared with traditional methods, the higher estimation accuracy of VGRF can be explained by the F-ELBO. Thirdly, a relationship between F-ELBO and the monitored ELBO (M-ELBO) is found, i.e., F-ELBO is always larger than M-ELBO. Based on this finding, the accuracy performance improvement of VGRF can be theoretically explained. Finally, three numerical examples are employed to illustrate the effectiveness of VGRF.