1,256 publications from this institution
This paper proposes a Robust Fuzzy Multivariable Model Predictive Controller (RFMMPC) using Linear Matrix Inequalities (LMIs) formulation. The main idea is to solve at each time instant, an LMI optimization problem that incorporates input, output and Constrained Receding Horizon Predictive Control (CRHPC) constraints, and plant uncertainties, and guarantees certain robustness properties. The RFMMPC is easily designed by solving a convex optimization problem subject to LMI conditions. Then, the derived RFMMPC applied to a variable wind turbine with blade pitch and generator torque as two control inputs. The effectiveness of the proposed design is shown by simulation results.
This paper deals with adaptive output tracking of a transverse flux permanent magnet machine as a nonlinear system with unknown nonlinearities by utilizing Takagi-Sugeno type neuro-fuzzy networks. The technique of feedback linearization and H control are used to design an adaptive control law for compensating the unknown nonlinear parts, such the effect of cogging torque, as a disturbance on the rotor angle and angular velocity tracking performances. Finally, the capability of the proposed method is shown by the simulation results.
The tensile strength of asphaltic mixtures is an important parameter in designing the pavement materials and life assessment of the overlays. This parameter is often determined experimentally by using the indirect diametral tension (IDT) test or sometimes by employing the semi-circular bend (SCB) test. However, neither can provide accurate and true tensile strength values for practical and field situations. While the applied load to the overlay induced by traffic loading is along the compaction direction, there is a 90-degree angle difference between the direction of applied loading to the IDT and SCB samples and the compaction direction of the asphalt mixture manufactured inside the Marshall or Gyratory cylinders. Hence, in this research, a novel test specimen is proposed for the first time to measure the tensile strength of asphalt concrete materials in more realistic conditions. The proposed method utilizes a disc shape sample named Symmetric Disc Bend (SDB), which is a specimen loaded by a conventional three-point bending fixture. The tensile strength value obtained from the SDB specimen was observed to be significantly greater than the results obtained from the SCB and IDT methods for any given test conditions. The main reason for such discrepancy can be attributed to the compaction direction of the asphalt mixture and the applied loading direction to the samples. Accordingly, the tensile cracking resistance of the HMA mixture along the compaction direction is significantly higher than in the other directions. Therefore, the test data often determined from the IDT testing method can provide conservative predictions for the service life of real asphaltic pavements. In addition, due to the bending type load in the SDB testing procedure, the proposed method provides better consistency with the tensile cracking of real flexible pavements subjected to bending type loads applied from the vehicles. Some suitable SDB dimensions and loading span ranges were proposed for conducting the indirect tensile strength tests using the SDB specimen. In addition, the fracture patterns of specimens tested were simulated and predicted using the XFEM method, and stress distributions were reviewed.
In this paper, the robust fault-tolerant (FT) H ∞ control problem of active suspension systems with finite-frequency constraint is investigated. A full-car model is employed in the controller design such that the heave, pitch and roll motions can be simultaneously controlled. Both the actuator faults and external disturbances are considered in the controller synthesis. As the human body is more sensitive to the vertical vibration in 4–8Hz, robust H ∞ control with this finite-frequency constraint is designed. Other performances such as suspension deflection and actuator saturation are also considered. As some of the states such as the sprung mass pitch and roll angles are hard to measure, a robust H ∞ dynamic output-feedback controller with fault tolerant ability is proposed. Simulation results show the performance of the proposed controller.
This paper is concerned with the problems on simultaneous actuator and sensor fault estimation as well as the fault-tolerant control for a class of Markovian jump systems subjected to faults and disturbances. Firstly, the original system is converted into a descriptor system by extending the sensor faults as auxiliary states. Secondly, an adaptive observer is designed for the descriptor system, in which the actuator faults are adjusted by the designed adaptive law. Based on the estimations of the actuator faults, a fault-tolerant control strategy is therefore proposed to stabilize the closed-loop system against actuator faults, sensor faults and disturbances. Sufficient conditions for the existence of the observer and controller are derived in accordance with linear matrix inequalities. Finally, some practical examples are delivered to illustrate the validation and effectiveness of the proposed method.
Notice of Violation of IEEE Publication Principles<br><br>After careful consideration by a duly constituted committee, an author of this article, Hamid Reza Karimi, was found to have acted in violation of the IEEE Principles of Ethical Publishing by artificially inflating the number of citations to this article. <br/> This paper is concerned with dissipativity-based fuzzy integral sliding mode control (FISMC) of continuous-time Takagi-Sugeno (T-S) fuzzy systems with matched/unmatched uncertainties and external disturbance. To better accommodate the characteristics of T-S fuzzy models, an appropriate integral-type fuzzy switching surface is put forward by taking the state-dependent input matrix into account, which is the key contribution of the paper. Based on the utilization of Lyapunov function and property of the transition matrix for unmatched uncertainties, sufficient conditions are presented to guarantee the asymptotic stability of corresponding sliding mode dynamics with a strictly dissipative performance. A FISMC law is synthesized to drive system trajectories onto the fuzzy switching surface despite matched/unmatched uncertainties and external disturbance. A modified adaptive FISMC law is further designed for adapting the unknown upper bound of matched uncertainty. Two practical examples are provided to illustrate the effectiveness and advantages of developed FISMC scheme.
No abstract is provided for this article.
For the complex industrial process, it has become increasingly challenging to effectively diagnose complicated faults. In this paper, a combined measure of the original Support Vector Machine (SVM) and Principal Component Analysis (PCA) is provided to carry out the fault classification, and compare its result with what is based on SVM-RFE (Recursive Feature Elimination) method. RFE is used for feature extraction, and PCA is utilized to project the original data onto a lower dimensional space. PCA<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math>, SPE statistics, and original SVM are proposed to detect the faults. Some common faults of the Tennessee Eastman Process (TEP) are analyzed in terms of the practical system and reflections of the dataset. PCA-SVM and SVM-RFE can effectively detect and diagnose these common faults. In RFE algorithm, all variables are decreasingly ordered according to their contributions. The classification accuracy rate is improved by choosing a reasonable number of features.
Focusing on the analog circuit performance evaluation demand of fast time responding online, a novel evaluation strategy based on adaptive Least Squares Support Vector Regression (LSSVR) which employs multikernel RBF is proposed in this paper. The superiority of the multi-kernel RBF has more flexibility to the kernel function online such as the bandwidths tuning. And then the decision parameters of the kernel parameters determine the input signal to map to the feature space deduced that a well plant model by discarding redundant features. Experiment adopted the typical circuit Sallen-Key low pass filter to prove the proposed evaluation strategy via the eight performance indexes. Simulation results reveal that the testing speed together with the evaluation performance, especially the testing speed of the proposed, is superior to that of the traditional LSSVR and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>ε</mml:mi></mml:mrow></mml:math>-SVR, which is suitable for promotion online.
No abstract is provided for this article.
This chapter presented a robust data-driven fault detection scheme with the application to a wind turbine benchmark. The proposed scheme is based on robust residual generators constructed directly from available process measurements. For this purpose, a parity space is first identified from the measured data, and optimal parity vectors are selected from the parity space according to a given performance index and an optimization criterion to generate a robust residual vector. A proper evaluation approach as well as a suitable decision logic is further given to make a correct final decision. The effectiveness of the proposed scheme is finally demonstrated by the results obtained from the simulation of a wind turbine benchmark model.
In this paper, we extend the wavelet networks for identification and H ∞ control of a class of nonlinear dynamical systems. The technique of feedback linearization, supervisory control and H ∞ control are used to design an adaptive control law and also the parameter adaptation laws of the wavelet network are developed using a Lyapunov-based design. By some theorems, it will be proved that even in the presence of modeling errors, named network error, the stability of the overall closed-loop system and convergence of the network parameters and the boundedness of the state errors are guaranteed. The applicability of the proposed method is illustrated on a nonlinear plant by computer simulation.
This article investigates region stabilization issue of switched neural networks (SNNs) with multiple modes (MMs) and multiple equilibria (ME) via a pole assignment method. In such an SNN, every neuron is observed with more than one mode and unstable equilibrium point. First, SNNs with MMs and ME are modeled in terms of switched systems with unstable subsystems and ME. Second, a necessary and sufficient condition and a sufficient condition are, respectively, proposed for arbitrary switching paths pole assignment and arbitrary periodic/quasi-periodic switching paths (PSPs/QSPs) asymptotically region stabilizing pole assignment of switched linear time-invariant (LTI) systems with ME. It is shown that to stabilize a switched LTI system, some/all poles of all/some linear subsystems can be assigned to suitable locations of the right-half side of the complex plane. Third, based on the obtained pole assignment results, an asymptotical-region-stabilizing-control law observed as distributed state feedback controllers of MMs, asymptotical-region-stabilizing PSPs/QSPs, and a corresponding algorithm are all designed for asymptotical region stabilization of switched linear/nonlinear neural networks with MMs and ME. Finally, a numeral example is given to illustrate the effectiveness and practicality of the new results.
This article studies consensus of linear multi-agent systems (MASs) on undirected graphs. An adaptive event-triggering protocol is constructed for consensus control by using relative information between agents. Sufficient conditions are established for consensus of linear MASs without and with external disturbances, respectively. Zeno behavior is proved to be excluded. Moreover, a self-triggered realization based on sampled information is formulated for the protocol. Since the proposed protocol incorporates both adaptive control and event-triggered control, it can be implemented in a fully distributed way and only makes use of the sampled relative information between neighboring agents. Two numerical examples are finally provided for demonstrating the effectiveness and advantages of the theoretical results.
