This paper investigates the sampled-data stabilization problem of spacecraft relative positional holding with improved Lyapunov function approach. The classical Clohessy-Wiltshire equation is adopted to describe the relative dynamic model. The relative position holding problem is converted into an output tracking control problem using sampling signals. A time-dependent discontinuous Lyapunov functionals approach is developed, which will lead to essentially less conservative results for the stability analysis and controller design of the corresponding closed-loop system. Sufficient conditions for the exponential stability analysis and the existence of the proposed controller are provided, respectively. Finally, a simulation result is established to illustrate the effectiveness of the proposed control scheme.
No abstract is provided for this article.
Data-driven intelligent fault diagnosis methods have emerged as powerful tools for monitoring and maintaining the operating conditions of mechanical equipment. However, in real-world engineering scenarios, mechanical equipment typically operates under normal conditions, resulting in limited and imbalanced (L&I) data. This situation gives rise to label bias and biased training. Meanwhile, the current multi-source information fault diagnosis research to date has tended to focus on fault identification rather than effective feature fusion strategies. To solve these issues, a novel end-to-end mechanical fault diagnosis framework under limited & imbalanced data using multi-source information fusion is proposed to model data-level and algorithm-level ideas in a unified deep network for achieving effective multi-source information fusion under the L&I working conditions. From a data-level perspective, a data preprocessing operation is first employed to capture time–frequency information simultaneously. Subsequently, multi-source time–frequency information is fed into feature extractors with information discriminators to construct local and information-invariant feature maps with different scales to eliminate multi-source information domain shift. Then, the multi-source feature vectors are modeled by a multi-source information transformer-based neural network to achieve effective multi-source information fusion through cross-attention mechanism. Next, the global max pooling and global average pooling layers are leveraged to obtain the more representative features. Finally, from an algorithm-level perspective, a dual-stream diagnosis predictor with a binary diagnosis predictor and a multi-class diagnosis predictor is designed to synthesize the diagnostic results through a reweighing activation mechanism for addressing the L&I problems. Extensive experiments on four different multi-source information datasets show the superiority and promising performance of our method compared to the state-of-the-art methods, as evidenced by indicators from various aspects.
Blood pressure (BP) is known as an indicator of human health status, and regular measurement is helpful for early detection of cardiovascular diseases. Traditional techniques for measuring BP are either invasive or cuff-based and thus are not suitable for continuous measurement. Aiming at the deficiencies in existing studies, a novel cuffless BP estimation framework of Receptive Field Parallel Attention Shrinkage Network (RFPASN) and BP range constraint is proposed. Firstly, RFPASN uses the multi-scale large receptive field convolution module to capture the long-term dynamics in the photoplethysmography (PPG) signal without using long short-term memory (LSTM). On this basis, the features acquired by the parallel mixed domain attention module are used as thresholds, and the soft threshold function is used to screen the input features to enhance the discriminability and robustness of features, which can significantly improve the prediction accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP). Finally, in order to prevent large fluctuations in the prediction results of RFPASN, RFPASN based on BP range constraint is proposed to make the prediction results of RFPASN more accurate and reasonable. The performance of the proposed method is demonstrated on a publically available MIMIC-II database. The database contains normal, hypertensive and hypotensive people. We have achieved MAE of 1.63/1.59 (DBP) and 2.26/2.15 (SBP) mmHg for BP on total population of 1562 subjects. A comparative study shows that the proposed algorithm is more promising than the state-of-the-art.
This study is concerned with the stability, l 2 ‐gain analysis and ℋ ∞ control for a class of discrete‐time switched linear parameter‐varying systems with both mode‐dependent average dwell time (MDADT) and asynchronous switching, where ‘asynchronous’ means the switching of controllers has a lag to the switching of system modes. The l 2 ‐gain for general switched systems with MDADT in non‐linear setting is firstly derived. Based on the obtained results, the problem of asynchronous ℋ ∞ control for the studied systems is formulated under the framework of MDADT switching logic, and the conditions for the existence of admissible asynchronous ℋ ∞ controllers are deduced in the form of parameterised linear matrix inequalities. A numerical example is provided to verify the effectiveness of the acquired results.
In this paper, the sliding mode control problem is addressed for the automotive electronic valve system, which is described by the Markovian model according to the voltage failure. It is supposed that both the system states and the system modes are unavailable to the controller. In order to avoid data collision on the sensor-to-controller transmission, the scheduling among the sensor nodes is ruled by the weighted try-once-discard protocol. A mode detector via a hidden Markovian model is introduced, and an asynchronous token-dependent state observer is proposed. Dependent on the hidden mode information and current token directive, a sliding mode controller is constructed to assure the reachability of a sliding region. Besides, the hidden Markovian model approach is developed to derive mean-square stability conditions for the augmented system. Eventually, simulation studies are provided to demonstrate the validity of the proposed control scheme for the system under consideration.
No abstract is provided for this article.
No abstract is provided for this article.
In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST control. Then the optimization problem is solved based on an ACO algorithm, used at the optimization process in AFMPC to determine optimal controller parameters of RST control. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, where the proposed approach provides better performances compared with proportional integral-ant colony optimization controller and adaptive fuzzy model predictive controller.
An important challenge in the static output-feedback control context is to provide an isolated gain matrix possessing a zero–nonzero structure, mainly in problems presenting information structure constraints. Although some previous works have contributed some relevant results to this issue, a fully satisfactory solution has not yet been achieved up to now. In this note, by using a Linear Matrix Inequality approach and based on previous results given in the literature, we present an efficient methodology which permits us to obtain an isolated static output-feedback gain matrix having, simultaneously, a zero–nonzero structure imposed a priori.
This article investigates the problem of sliding mode control (SMC) for semi-Markov switching systems (S-MSSs) with quantized measurement in finite-time level. The transition between different subsystems obeys a stochastic semi-Markov process related to nonexponential distribution. Additionally, due to the sensor information constraints, the state vectors are not always measurable in practice. Moreover, compared with existing results in literature, the output quantization is first considered for finite-time SMC problem via a logarithmic quantizer. Our attention is to design an appropriate finite-time SMC law to attenuate the influences of parametrical uncertainty and external disturbance onto the overall performance of the system under consideration. First, by the key points of stochastic semi-Markov Lyapunov function and observer design theory, a desired SMC law is constructed to guarantee that the system trajectories can arrive at the specified sliding surface (SSS) within an assigned finite-time level. Then, ST-dependent sufficient conditions are established to ensure the required finite-time boundedness performance including both reaching phase and sliding motion phase. Finally, the applicability of the proposed results is demonstrated by a single-link robot arm model.
This paper studies the problem of finite-impulse response (FIR) filtering design of time-delay system. The time-delay considered here is time-varying meanwhile with a certain stochastic characteristic, and the probability of delay distribution is assumed to be known. Furthermore, the requirement of pulse-shape is also considered in filter design. Employing the information about the size and probability distribution of delay, a delay-probability-distribution-dependent criterion is proposed for the filtering error system. Based on a Lyapunov-Krasovskii functional, a set of linear matrix inequalities (LMIs) are formulated to solve the problem. At last, a numerical example is used to demonstrate the effectiveness of the filter design approach proposed in the paper.
This paper employs fuzzy set theory to solve the unintuitive problem of the Markowitz mean‐variance (MV) portfolio model and extend it to a fuzzy investment portfolio selection model. Our model establishes intervals for expected returns and risk preference, which can take into account investors′ different investment appetite and thus can find the optimal resolution for each interval. In the empirical part, we test this model in Chinese stocks investment and find that this model can fulfill different kinds of investors’ objectives. Finally, investment risk can be decreased when we add investment limit to each stock in the portfolio, which indicates our model is useful in practice.
An eigensystem realization algorithm (ERA) approach for estimating the structural system matrices is proposed in this paper using the measurements of acceleration data available from the real crash test. A mathematical model that represents the real vehicle frontal crash scenario is presented. The model’s structure is a double-spring-mass-damper system, whereby the front mass represents the vehicle-chassis and the rear mass represents the passenger compartment. The physical parameters of the model are estimated using curve-fitting approach, and the estimated state system matrices are estimated by using the ERA approach. The model is validated by comparing the results from the model with those from the real crash test.
In this chapter, we investigate the observer-based finite-time robust H ∞ vibration control problem for half-car active suspension systems with actuator parametric uncertainties and nonlinearities. The objective is to derive a suitable robust vibration controller which makes the vibration of the closed-loop suspension system be finite-time stabilizable and satisfies the given H ∞ performance index. Applying the Lyapunov function approach, we establish sufficient conditions to control the bounce and pitch vibrations in half-car active suspension system, stabilize the attitude of the vehicle, and improve the riding comfort. The observer-based finite-time robust H ∞ vibration controller gain parameters can be solved directly by using the existing linear matrix inequalities optimization techniques. Finally, a half-car active suspension model is used to illustrate the feasibility and validity of the proposed methods.
In this paper, the problems of stability analysis and H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> controller design of a class of switched nonlinear systems are investigated. In a classical way, the modeling of the systems is approached by switched fuzzy systems, and both fast switching and slow switching are considered there. In particular, for slow switching scheme, a new mode-dependent average dwell time switching is proposed for the underlying switched fuzzy systems. Based on a fuzzy-basis-dependent and mode-dependent Lyapunov function, the H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> state-feedback controller is derived. A numerical example is given to show the validity and potential of the theoretical results.
