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An improved algorithm called active disturbance rejection generalized predictive control which combines advantages of active disturbance rejection control and generalized predictive control is proposed for time-delay systems in this paper to reduce the limitations of active disturbance rejection control (ADRC) in plants with large time-delay and improve the imperfections of generalized predictive...
Active Disturbance Rejection Control (ADRC), an innovative control method, has been applied successfully in dealing with internal uncertainties and external disturbances. However, ADRC for time-delay plants is still a challenge due to the restriction on the bandwidth of the extended state observer (ESO). When designing the ESO for time-delay plants, both full-order and reduced-order extended state...
An improved batch process fault identification approach with kernel exponential discriminant analysis (KEDA) is proposed, in which performance index based on difference degree is given to identify fault classification. This method takes the advantages of both the kernel technology and the exponential discriminant analysis technique. The proposed KEDA method shows powerful ability in dealing with nonlinear,...
One of the fundamental problems of an interconnected interactive system is the huge amounts of data that are being generated by every entity. Unfortunately, what we seek is not data but information, and therefore, a growing bottleneck is exactly how to extract and learn useful information from data. In this paper, the information-theoretic learning in data-driven games is studied. This learning shows...
Due to the nonaffine nature, tracking control of unknown nonlinear pure feedback system is difficult. Traditional control method based on backstepping has the problem of differential explosion. Reinforcement learning control strategy can avoid this problem. However, the tracking error is relatively large because of lack of system structure information. To overcome this problem, an improved reinforcement...
This note addresses the problem of sampled-data iterative learning control (SDILC) for continuous-time nonlinear systems with randomly iteration varying lengths. To deal with the iteration varying trial lengths, a P-type ILC scheme with a modified tracking error is proposed. Sufficient conditions are derived to ensure the convergence of the nonlinear system at each sampling instant. An illustrative...
This paper addresses iterative learning control problem for singular distributed parameter systems with parabolic type. Owing to singular value decomposition theory, the singular distributed parameter systems are transformed into its dynamic decomposition standard form. Then, in virtue of the Bellman-Gronwall inequality and contraction mapping approach, the learning convergence of L2 norm of output...
The conventional multivariate statistical process control (MSPC) methods may not be sensitive to the detection of incipient changes since they in general quantify the distance between the new sample and the modeling samples without checking the changes of data distribution. In the present works, a method with dissimilarity analysis and quality-relevant subspace decomposition based on process monitoring...
This paper investigates the adaptive consensus problem of first-order linearly parameterized multi-agent systems (MASs) with imprecise communication topology structure. T-S fuzzy models are presented to describe leader-followers MASs with imprecise communication topology structure, and a fuzzy distributed adaptive iterative learning control protocol is proposed. With the dynamic of leader unknown...
The purpose of this work is to improve the tracking performance of the iterative learning control (ILC) by designing a new learning law that has the ability to update the input along both the time and iterative axes. First, the reference is generated by a high-order internal model (HOIM) along the iterative axis and can be approximated by an HOIM along the time axis. Then, the HOIM-based repetitive...
Several studies have adopted hidden Markov model (HMM) to monitor multimode processes. The drawback of HMM is that its inherent duration probability density is exponential and hence it is inappropriate for the modeling of multimode processes. To address this problem, hidden semi-Markov model (HSMM), which introduces the mode duration probability into HMM, is combined with principal component analysis...
By using delayed matrix sine and cosine of polynomial degrees methods, learning updating laws are designed for an oscillating system with pure delay to track the reference accurately. Several convergence results of open-loop, closed-loop and open-closed-loop P-type and D-type convergence results are obtained. Two numerical examples are finally given.
This work addresses the boundary tracking control of a class of MIMO PDE-ODE cascade systems via learning control approach. Due to the temporal-, spatial- and iteration-varying properties, one of the key steps before the controller design is to reduce the variation of the systems. Therefore, frequency domain analysis techniques are adopted in this work, which can be used to remove the time domain...
Though in the era of big data, it remains a challenge to be tackled that the forecasting model with high accuracy and robustness needs to be built using small size samples. One effective tool of addressing this problem is the virtual sample generation (VSG), which can generate a mass of new virtual samples on the basis of small sample sets. The bootstrap method is adopted to feasibly resample the...
This paper deals with the issue on air conditioning energy consumption and system monitoring of different data in building. Various environmental parameters inside the building are changed in real time, while the conventional air conditioning energy consumption forecasting with the load simulation software cannot adapt to these variations. Therefore, the air conditioning energy consumption forecasting...
In this paper, a Model Free Control based Nonlinear Integral Backstepping Control (MFC-NIB) strategy is developed and applied to blood glucose regulation systems, which is a typical biological system with parameter variations, uncertainties and external disturbances. Firstly, an Intelligent Proportional controller (iP), which is based on model-free theory and whose algebraic estimation technique is...
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