This paper studies a synthesis approach to predictive control for networked control systems with data loss and quantization. An augmented Markov jump linear model with polytopic uncertainties is modeled to describe th...
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ISBN:
(纸本)9781479947249
This paper studies a synthesis approach to predictive control for networked control systems with data loss and quantization. An augmented Markov jump linear model with polytopic uncertainties is modeled to describe the quantization errors and possible data loss. Based on this model, a predictive control synthesis approach is developed, which involves online optimization of a infinite horizon objective and conditions to deal with system constraints. The proposed MPC algorithm guarantees closed-loop mean-square stability and constraints satisfaction.
The problem of controller synthesis in a fixed time interval for discrete-time switching Markov jump systems is dealt with in this paper. Compared with the existing results, the new proposed stabilization conditions a...
The problem of controller synthesis in a fixed time interval for discrete-time switching Markov jump systems is dealt with in this paper. Compared with the existing results, the new proposed stabilization conditions are obtained by permitting the stochastic Lyapunov energy function to rise at each switching instant, but the switching signal is constraint by an average dwell time. Finally, the validity of the obtained results is demonstrated with an example.
In this paper, the disturbance decoupling observer design problem for a class of distributed parameter systems with unknown disturbance in the state and in the measurement equations is considered. The disturbance dist...
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In this paper, the disturbance decoupling observer design problem for a class of distributed parameter systems with unknown disturbance in the state and in the measurement equations is considered. The disturbance distribution is an unknown signal, but the operator describing the disturbance is known. All operators for systems and observers are bounded operators. A novel disturbance decoupling observers design scheme is proposed via the algebraic transformation. The solvability of the problem hinges on the solution to a corresponding operator equation. The existence conditions for the disturbance decoupling observers are presented. Finally, a numerical example of a 1-D parabolic system is used to illustrate the results of the disturbance decoupling observers..
This paper presents two recursive identification algorithms for bilinear-parameter models: a decomposition based stochastic gradient algorithm and a decomposition based recursive least squares algorithm. The key is to...
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This paper presents two recursive identification algorithms for bilinear-parameter models: a decomposition based stochastic gradient algorithm and a decomposition based recursive least squares algorithm. The key is to decompose a bilinear-parameter model into two fictitious subsystems, and to identify the parameters of each subsystem by replacing the unknown variables in the information vectors with their estimates. The simulation results show the performances the proposed algorithms.
This paper aims to generalize the loop interaction measurement, relative normalized gain array (RNGA), for multivariable systems regarding a class of reference inputs. In the existing studies, RNGA loop pairing criter...
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This paper aims to generalize the loop interaction measurement, relative normalized gain array (RNGA), for multivariable systems regarding a class of reference inputs. In the existing studies, RNGA loop pairing criterion is analyzed and widely utilized on the basis of detailed assumption of step reference input. For multivariable systems under step, ramp and other general types of set-point changes, the general loop pairing technique is put forward, and the average residence time is calculated in terms of first order plus delay time and second order plus delay time processes. The analysis results show RNGA based control-loop configuration is independent of input signals, and available to multivariable systems for various reference inputs. Several examples are employed to demonstrate the effectiveness and universality of the pairing approach of this paper.
This paper focuses on parameters estimation problems of multivariable nonlinear systems. A hierarchical least squares algorithm is proposed by using key-term separation principle and hierarchical identification princi...
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This paper focuses on parameters estimation problems of multivariable nonlinear systems. A hierarchical least squares algorithm is proposed by using key-term separation principle and hierarchical identification principle. The algorithm has lower computational load than the existing over-parametrization methods. Finally, a numerical example is given to show the effectiveness of the proposed algorithm.
This paper considers parameter estimation problems of a controlled autoregressive ARMA system. We decompose this system into two subsystems, use the data filtering technique to derive a maximum likelihood multi-innova...
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This paper considers parameter estimation problems of a controlled autoregressive ARMA system. We decompose this system into two subsystems, use the data filtering technique to derive a maximum likelihood multi-innovation stochastic gradient algorithm. The simulation results show that the proposed algorithm has a higher computational efficiency than the maximum likelihood gradient algorithm and the filtering-based maximum likelihood stochastic gradient algorithm.
Simultaneous saccharification and fermentation (SSF) of glutinous rice was performed by using o-amylase, glucoamylase, and rice wine yeast strain Saccharomyces cerevisiae Su-25. Experiments were carried out at two dif...
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ISBN:
(纸本)9781632668455
Simultaneous saccharification and fermentation (SSF) of glutinous rice was performed by using o-amylase, glucoamylase, and rice wine yeast strain Saccharomyces cerevisiae Su-25. Experiments were carried out at two different locations, and the main products were identified and measured by HPLC. A low-order kinetic model structure (forms or constructs of model with adjustable parameters) was proposed based on the major chemical reactions in the SSF process. The model structure was then tested for its abilities to capture the main kinetic variations after parameter optimization by a least-squares algorithm. The proposed model structure was found useful in representing measured kinetic variations. The estimated reactions rates correctly reflected the variations observed from the experiments and provided insights into the reaction processes. While additional research is warranted for further validation and refinement, the proposed model structure shows promise for describing the simultaneous saccharification and fermentation process of glutinous rice.
Infrared remote sensing image has poor contrast and lower SNR so that real-time and robustness are not superior in image registration. In order to solve it, a novel registration based on Multi-scale feature extraction...
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In this paper, a linear unbiased minimum-variance filtering problem is considered for a class of systems with randomly multi-step sensor delays. A new mathematical model is established for the multi-step sensor delays...
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ISBN:
(纸本)9781479940318
In this paper, a linear unbiased minimum-variance filtering problem is considered for a class of systems with randomly multi-step sensor delays. A new mathematical model is established for the multi-step sensor delays. Different from the augmented method for dealing with delayed systems, a linear unbiased minimum-variance filter design method is proposed without augmenting the state vector, which effectively reduces the filter dimensions. A recursive algorithm for calculating the filter gain matrix is developed. The simulation results illustrate the effectiveness of the proposed method.
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