This paper deals with the design of robust fault detection system for Takagi-Sugeno (T-S) modes with parametric uncertainties and time-varying delay. An Unknown Input Observer (UIO) is designed such that the unknown inputs are thoroughly decoupled from residual signals while they show the maximum possible sensitivity to the faults and the minimum possible sensitivity to the external disturbances. Since the system under consideration is subjected to parametric uncertainties, the H ∞ model matching approach is used to design an optimal observer. Design procedure is given in terms of Linear Matrix Inequalities (LMIs). Finally, a numerical example is presented to show the effectiveness of the proposed method.
This study analyses the stability of cubature Kalman filter (CKF) for non‐linear systems with linear measurement. The certain conditions to ensure that the estimation error of the CKF remains bounded are proved. Then, the effect of process noise covariance is investigated and an adaptive process noise covariance is proposed to deal with large estimation error. Since adaptation law has a very important role in convergence, fuzzy logic is proposed to improve the versatility of the proposed adaptive noise covariance. Accordingly, a modified CKF (MCKF) is developed to enhance the stability and accuracy of state estimation. The performance of the modified CKF is compared to the CKF in two case studies. Simulation results demonstrate that the large estimation error may lead to instability of CKF, while the MCKF is successfully able to estimate the states. In addition, the superiority of MCKF that uses fuzzy adaptation rules is shown.
In this paper, a novel procedure is proposed to design a nonlinear unknown input observer (NUIO) for robust fault detection purposes. NUIO decouples disturbances and uncertainties from the state estimation in nonlinear systems. The proposed method is based on cubature rules which are utilized in Cubature Kalman filter (CKF). The highlight of this work is to combine CKF with UIO to achieve a new NUIO structure for robust fault detection. For illustrating the performance of this approach, it is applied to a nonlinear continuous stirred tank reactor (CSTR) under faulty sensors. Compared to the previous NUIOs, which use extended Kalman filter (EKF) and unscented Kalman filter (UKF), simulation results demonstrate the superiority of the proposed approach.
This paper presents a new approach for broken rotor bars detection in induction motors. When broken rotor bar occurs, the rotor resistance will increase. Furthermore, thermal effects of rotor resistance are considered to address practical aspects. Therefore, a suitable method to detect broken rotor bar is rotor resistance estimation. An applicable method in state estimation is Cubature Kalman Filter (CKF). To display the advantages of the CKF, simulation results are compared with Unscented Kalman Filter (UKF). In addition to not needing setting parameters, this filter is more accurate in rotor resistance estimation compared to the UKF.
This paper investigates the problem of network-based fault-tolerant controller design for networked control systems (NCSs) in the presence of random delays and data packet dropouts. A novel actuator fault model which is more general and practical than the conventional actuator fault models is developed. Considering this new fault model, the NCSs are firstly modeled as a Markovian jump system (MJS) with partly unknown transition probabilities (TPs), upon which sufficient conditions based on linear matrix inequalities (LMIs) are then developed to design the output feedback fault-tolerant controller to ensure the stochastic stability of the NCS. Finally, simulation results are provided to illustrate the effectiveness and superiority of the proposed method compared to the existing approaches in the literatures.
This article studies the resilient finite-time consensus tracking problem for high-order nonholonomic chained-form systems against denial-of-service (DoS) attacks. The first step is to develop a novel secure distributed observer for each follower in which the tangent hyperbolic function is used to accelerate the convergence speed of the observer by inducing a high-gain effect. The paralyzed-connectivity graphs resulting from DoS attacks are repaired to the initially connected graphs by integrating both acknowledgment-based attack detection techniques and the communication recovery process. In addition, it is demonstrated that the duration of DoS attacks directly affects the convergence time of the proposed scheme. Then, a fast finite-time backstepping control (FFTBC) algorithm is established for each follower to track the estimated leader's information, ensuring fast convergence performance regardless of whether the follower states are near or far from the equilibrium point. An approximation-based approach is also presented for reducing the conservatism of the upper estimate of the settling time. An evaluation of the proposed control algorithm under DoS attacks is conducted using a group of wheeled mobile robots.
In this article, we develop a novel multiobjective controller to regulate the power converters of a class of dc microgrids connected to nonlinear constant power loads and linear resistive loads. The suggested control approach uses the nondominating sorting binary genetic algorithm (NSBGA-II) to directly design the on/off switching signal of the converters without using the pulsewidth modulation technique. The multiobjective controller minimizes the tracking error of the dc bus voltage and at the same time tries to reduce the total number of switching actions. Thereby, the developed controller tracks the desired reference with a reduced converter switching action and power loss by using a proper Pareto solution. Moreover, by employing the NSBGA-II algorithm, it is feasible to involve the switching frequency in the design procedure to enhance the performance. Exploiting the binary genetic algorithm instead of the conventional genetic algorithm (GA) turns a continuous surface searching into a binary one, which not only makes it more compatible with the nature of the power converter control but also decreases the online computational burden. To illustrate the superiority of the proposed approach, real-time OPAL results are provided.
This paper investigates the problem of robust unknown input observers design for fault detection of Takagi–Sugeno fuzzy systems. In order to handle uncertainties related to membership functions and rule-base, in this study interval type-2 fuzzy sets are employed as activation functions. The system is supposed to be affected by parameter uncertainties and time-varying delays, which makes the design procedure more challenging. Furthermore, to achieve better results in the detection of faults, a multi-objective optimization index is considered so as to get a residual signal with the most possible sensitivity to the fault and least one to other signals. This issue will lead to some design constraints in the terms of linear matrix inequalities. Two case studies are provided to show the validity of the proposed method. In addition, the superiority of interval type-2 fuzzy sets compared to type-1 sets is investigated in the simulation part.
In this paper, a novel estimation algorithm based on fractional order interpolatory cubature Kalman filter is introduced to achieve the state estimation in nonlinear discrete-time fractional order systems with colored noise. In the proposed approach, the system with colored noise is transformed to a system with correlated process and measurement noises by presenting a new auxiliary output based on the extension of the measurements differencing method. Then, the novel fractional order interpolatory cubature Kalman filter algorithm is introduced based on these new outputs. The proposed algorithm is applied for the synchronization of fractional order hyperchaotic Lorenz system targeting the cryptography in a communication system where the transmitter and the transmission channel possess colored noise. Simulation results demonstrate the effectiveness of the proposed scheme.
This study proposes an effective positive control design strategy for cancer treatment by resorting to the combination of immunotherapy and chemotherapy. The treatment objective is to transfer the initial number of tumor cells and immune-competent cells from the malignant region into the region of benign growth where the immune system can inhibit tumor growth. In order to achieve this goal, a new modeling strategy is used that is based on Takagi-Sugen. A Takagi-Sugeno fuzzy model is derived based on the Stepanova nonlinear model that enables a systematic design of the controller. Then, a positive Parallel Distributed Compensation controller is proposed based on a linear copositive Lyapunov Function so that the tumor volume and administration of the chemotherapeutic and immunotherapeutic drugs is reduced, while the density of the immune-competent cells is reached to an acceptable level. Thanks to the proposed strategy, the entire control design is formulated as a Linear Programming problem, which can be solved very efficiently. Finally, the simulation results show the effectiveness of the proposed control approach for the cancer treatment. Keywords: Co-positive linear Lyapunov function, Cancer, Chemotherapy, Immunotherapy, Positive system, Takagi-Sugeno fuzzy system.
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