For systems with time delay, several control strategies have been developed, for example, Smith predictor control, IMC (Internal Model control), LQG and so on. IMC is one of the simple design methods for systems with ...
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For systems with time delay, several control strategies have been developed, for example, Smith predictor control, IMC (Internal Model control), LQG and so on. IMC is one of the simple design methods for systems with time delay, however, the above model based controller requires exactly parameter, including the value of time delay. Time delay systems exist in the process control and it is difficult for the process system to make a mathematical model. In order to derive a good closed-loop property, closed-loop identification is one of the strongly strategy. This paper deals with experimental results for a cooling temperature control systems with an input time delay by using a joint design method of closed-loop identification and IMC. The results show that the closed-loop has good performance with easily parameter setting compared with ordinary PID control.
Adaptive control schemes are developed, which provide global asymptotic stability property with respect to state space vector of the plant with functional uncertainty. In the presence of disturbance these schemes ensu...
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For predictive control in industry often very long horizons for control error and manipulated signal are used because of the slow processes which take place in the petrochemical industry. In order to reduce the comput...
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Adaptive control schemes are developed, which provide global asymptotic stability property with respect to state space vector of the plant with functional uncertainty. In the presence of disturbance these schemes ensu...
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Adaptive control schemes are developed, which provide global asymptotic stability property with respect to state space vector of the plant with functional uncertainty. In the presence of disturbance these schemes ensure boundedness of all trajectories of the system. A switching modification of proposed schemes is considered.
In this paper, statistical pattern recognition method based on AR model was introduced to discriminate the electroencephalograph (EEG) signals recorded during right and left motor imagery. And learning methods were in...
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In this paper, statistical pattern recognition method based on AR model was introduced to discriminate the electroencephalograph (EEG) signals recorded during right and left motor imagery. And learning methods were investigated. Also, correlation between C3 and C4 signals were investigated, and thereby which AR (combine AR or multivariable AR) model must be used in each EEC recording method.
Electroencephalograph (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communicatio...
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Electroencephalograph (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by e.g., handicap users with amyotrophic lateral sclerosis (ALS). In this study, statistical pattern recognition method based on AR model was introduced to discriminate the EEG signals recorded during right and left motor imagery. And learning methods (processing period for parameter estimation, AR order, etc.) were investigated. Finally, the effectiveness of our method was confirmed through the experimental studies.
In a distributed control systems the internal communication bus represents a critical technology component that interconnects particular control system elements. Efficiency of a communication inside the distributed co...
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In a distributed control systems the internal communication bus represents a critical technology component that interconnects particular control system elements. Efficiency of a communication inside the distributed control system is relied using an appropriate industrial bus. To estimate crucial parameters of the bus (such as a bus throughput, etc.), the bus communication model is designed to verify the usability of the concrete industrial bus under real application conditions. The CAN bus communication modelling is presented in this paper.
The main goal is to introduce quadratic and cubic neural units (QNU, CNU) as appropriate neural units for fast state feedback control of unknown, unstable, nonlinear systems and depict their ability to provide a more ...
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The main goal is to introduce quadratic and cubic neural units (QNU, CNU) as appropriate neural units for fast state feedback control of unknown, unstable, nonlinear systems and depict their ability to provide a more robust and faster response than as it is in the case of a common linear state feedback controllers. The concepts of CNU and QNU are briefly introduced. Stability criteria of a nonlinear control loop including CNU and QNU are discussed. The universal structure of a stable and fast controller of an unknown linear or nonlinear second order system with variable damping realized by a subset of CNU is proposed. Results of unknown nonlinear system identification and control are shown. A possible structure of a neural nonlinear state controller which uses a subset of CNU for parallel identification of an unknown, controlled nonlinear plant with varying parameter values and structure is considered
In this paper, a direct learning control method for a class of switched systems is proposed. The objective of direct learning is to generate the desired control profile for a newly switched system without any feedback...
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In this paper, a direct learning control method for a class of switched systems is proposed. The objective of direct learning is to generate the desired control profile for a newly switched system without any feedback, even if the system may have uncertainties. This is achieved by exploring the inherent relationship between any two systems before and after a switch. The new method is applicable to a class of linear time varying, uncertain and switched systems, when the trajectory tracking control problem is concerned. A numerical simulation demonstrates the effectiveness of the proposed method.
Long-range optimal prediction algorithms use the predicted output for several steps ahead. The prediction based on traditionally estimated model parameters does not result in an optimal prediction if the measurements ...
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Long-range optimal prediction algorithms use the predicted output for several steps ahead. The prediction based on traditionally estimated model parameters does not result in an optimal prediction if the measurements are noisy or/and model structure differs from real process structure. In this paper two different identification schemes are presented and compared: long-range predictive single-model identification and simultaneous multi-step-ahead prediction identification. It is shown that the first method is easier to realize but the second one leads to more accurate results. Both methods are derived for a first-order model in details. Simulation runs and a level control example illustrate the algorithms presented.
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