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In this paper, fault detection of networked control systems with random delays, packet dropout and noises is studied. The filter is designed using a minimum error entropy criterion. The residual generated by the filter is then evaluated to detect faults in networked control systems. An illustrative networked control system is used to verify the eff...
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... the initial filter gain is set to L [ 0.1 0.15] . The simulation results are shown in Figures 4–7. Figure 4 shows the increasing information potential of the MEE filter, in which the entropy of the filtering error deceases with time. Figures 5 and 6 indicate the evolution of two state errors respectively. The convergent state errors demonstrate better performance of the filter. Figure 7 shows the evolution of the evaluation function k 1 T 2 for both the fault case and fault-free case, and it can be seen that fault ...
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Citations
... Shannon entropy was used to estimate the weight of each particle from the weights of different measurement models for the fusion algorithm in [10]. Quadratic Rényi entropy [11] of innovation has been used as a minimum entropy criterion under a nonlinear and non-Gaussian circumstance [12] in unscented Kalman filter (UKF) [13] and finite ...
Rényi entropy as a generalization of the Shannon entropy allows for different averaging of probabilities of a control parameter α. This paper gives a new perspective of the Kalman filter from the Rényi entropy. Firstly, the Rényi entropy is employed to measure the uncertainty of the multivariate Gaussian probability density function. Then, we calculate the temporal derivative of the Rényi entropy of the Kalman filter's mean square error matrix, which will be minimized to obtain the Kalman filter's gain. Moreover, the continuous Kalman filter approaches a steady state when the temporal derivative of the Rényi entropy is equal to zero, which means that the Rényi entropy will keep stable. As the temporal derivative of the Rényi entropy is independent of parameter α and is the same as the temporal derivative of the Shannon entropy, the result is the same as for Shannon entropy. Finally, an example of an experiment of falling body tracking by radar using an unscented Kalman filter (UKF) in noisy conditions and a loosely coupled navigation experiment are performed to demonstrate the effectiveness of the conclusion.
... Therefore, the transient performance of the system outputs can be enhanced via minimising the entropy. In practice, entropy optimisation is widely used for network systems [20,21] , filter design [22,23] and fault diagnosis [24,25] . ...
This paper investigates the stabilisation problem and consider transient optimisation for a class of the multi-input-multi-output (MIMO) semi-linear stochastic systems. A control algorithm is presented via an m-block backstepping controller design where the closed-loop system has been stabilized in a probabilistic sense and the transient performance is optimisable by optimised by searching the design parameters under the given criterion. In particular, the transient randomness and the probabilistic decoupling will be investigated as case studies. Note that the presented control algorithm can be potentially extended as a framework based on the various performance criteria. To evaluate the effectiveness of this proposed control framework, a numerical example is given with simulation results. In summary, the key contributions of this paper are stated as follows: 1) one block backstepping-based output feedback control design is developed to stabilize the dynamic MIMO semi-linear stochastic systems using a linear estimator; 2) the randomness and probabilistic couplings of the system outputs have been minimized based on the optimisation of the design parameters of the controller; 3) a control framework with transient performance enhancement of multi-variable semi-linear stochastic systems has been discussed.
... As a summary, the MEE principle has been widely used for control and optimization of the stochastic non-Gaussian systems (see e.g. Zhang & Wang, 2008;Zhang, Chu, et al. 2009;Zhang, Du, et al. 2012;. ...
... Filtering design and fault diagnosis are two important problems in non-linear and non-Gaussian systems. Recently, Guo and Wang (2005a), Zhang et al. (2007), Yi et al. (2012), Wang (2011), Zhang, Yu, et al. (2006), Guo, Wang, and Chai (2006), Guo, Zhang, Wang, and Fang (2006), Zhang, Du, et al. 2012, Zhang, Liu, et al. (2012, Guo, Yin, Wang, and Chai (2009b), Li and Guo (2009), Yao, Qin, Wang, and Jiang (2012, Yao, Cocquempot, and Wang (2010), Guo (2009), Zhang, Guo, and, , Ren and Wang (2010) investigate these problems using SDC concept. ...
Probability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense.
... In [12], Liu et al. study fault detection of linear systems over networks with bounded packet loss, and a fault detection filter for switched system is designed to satisfy performance requires. Zhang et al. [13] proposes the approach of minimum error entropy filter to indicate the existing fault in the networked control systems with random delays, packet dropout, and noises by the residual generated by the filter. In [14], T-S fuzzy model is adopted to deal with the fault detection of the networked control system with Markov transfer delays. ...
A fault detection approach based on nonlinear robust observer is designed for the networked suspension control system of Maglev train with random induced time delay. First, considering random bounded time-delay and external disturbance, the nonlinear model of the networked suspension control system is established. Then, a nonlinear robust observer is designed using the input of the suspension gap. And the estimate error is proved to be bounded with arbitrary precision by adopting an appropriate parameter. When sensor faults happen, the residual between the real states and the observer outputs indicates which kind of sensor failures occurs. Finally, simulation results using the actual parameters of CMS-04 maglev train indicate that the proposed method is effective for maglev train.
... Entropy is a known measure that describes the average information contained in the given PDF (JPDF), which has been widely used in information, thermodynamics and control fields [6,8,[27][28][29][30]. By minimizing the entropy, higher order moments can be minimized with respect to the random variables [13,26,27,[31][32][33]. ...
In this paper, the fault detection in uncertain multivariate nonlinear non-Gaussian stochastic systems is further investigated. Entropy is introduced to characterize the stochastic behavior of the detection errors, and the entropy optimization principle is established for the fault detection problem. The principle is to maximize the entropies of the stochastic detection errors in the presence of faults and to minimize the entropies of the detection errors in the presence of disturbances. In order to calculate the entropies, the formulations of the joint probability density functions (JPDFs) of the stochastic errors are presented in terms of the known JPDFs of both the disturbances and the faults. By using the novel performance indexes and the formulations for the entropies of the detection errors, new fault detection design methods are provided for the considered multivariate nonlinear non-Gaussian plants. Finally, a simulation example is given to illustrate the efficiency of the proposed fault detection algorithm.
In this paper, the problem of control algorithm design for a class of nonlinear two-input and two-output (TITO) networked control systems (NCSs) with non-Gaussian random time delays is investigated, where a general non-linear auto-regressive moving average with exogenous model is used to describe the plant. Due to the non-Gaussian random time delays involved in the systems, it is insufficient to obtain a satisfactory optimal control algorithm by only controlling the expected value of the tracking errors. The Renyi entropies of the tracking errors and control inputs are adopted to characterize the randomness of the closed-loop system. The formulations of the probability density functions (PDFs) of the tracking errors and control inputs are deduced. By minimizing the new performance index, a recursive optimal control algorithm is obtained. Furthermore, the local stability condition of the closed-loop systems is established. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.
In this study, an event-triggered distributed filtering problem is investigated for non-Gaussian systems over wireless sensor networks (WSNs). Each sensor node transmits the data to its neighbours according to predetermined sendon-delta (SoD) communication mechanism. Since stochastic disturbances imposed on WSNs and plants and stochastic behaviour induced by WSNs are usually non-Gaussian, the survival information potential criterion is employed to construct a distributed filter for each sensor node subject to sporadic communications over WSNs and non-Gaussian disturbances. In addition, the energy constraints on filter gain are also taken into account. The incremental filtering algorithm is given and the boundedness of the filter parameter is also analysed. The effectiveness of the proposed algorithm is verified by an illustrative example.
In this paper, a novel filter based fault detection and isolation strategy is presented for variable-speed wind energy conversion systems (WECSs) in the presence of sensor faults. Firstly, a nonlinear physical model of a WECS is built by integrating aerodynamic rotor, a drive train, a generator and a converter. The typical sensor faults are also modelled. Since the wind speed is usually a non-Gaussian signal, a novel filter is then presented for WECSs via improved minimum error entropy criterion. Based on the established filter, residual signals are generated, and accordingly the detection and isolation rule for the sensor faults is obtained by evaluating residual signals. Finally, the proposed fault detection and isolation scheme is applied to a WECS. Simulations results indicate the sensor faults can be detected and isolated effectively.
