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Extremum seeking is a form of adaptive control where the steady-state input-output characteristic is optimized, without requiring any explicit knowledge about this input-output characteristic other than that it exists and that it has an extremum. Because extremum seeking is model free, it has proven to be both robust and effective in many different application domains. Equally being model free, there...
Stochastic distribution control (SDC) for non-Gaussian system is a mathematically complicated yet practical problem to solve. The most recent solution involves a radial basis function neural network (RBFNN) framework to approximate non-Gaussian output probability density function (PDF). The dynamic weights of such neural network are controlled within each batch of ILC, using a dedicated adaptive controller...
In practice, the convergence rate and stability of perturbation based extremum-seeking (ES) schemes can be very sensitive to the curvature of the plant map. This sensitivity arises from the use of a gradient descent adaptation algorithm. Such ES schemes may need to be conservatively tuned in order to maintain stability over a wide range of operating conditions, resulting in slower optimisation than...
Some currently used algorithms for decoupling and control based on neural network are very complicated and massive online computations and operations are necessary, which causes problems in real-time control and practical implementation. A kind of fault-tolerant decouple and control algorithms for non-linear and time-varying MIMO systems based on neuron adaptive PID and neural network is proposed...
Difficulties in water level control of deaerator lie in the strong couples between water level of deaerator and pressure in outlet of condenser pump, which causes unavailability of automatic control with traditional PID controllers. General problems in traditional decoupling control and PID control schemes are analyzed. Control scheme of combining neuron intelligent regulator with static decoupling,...
Effective control for non-linear MIMO systems with strong couples and time-varying property can not be implemented with traditional decouple and control algorithms. Some currently used algorithms cause problems in real-time control and practical implementation. A kind of fault-tolerant decouple and control algorithm for non-linear and time-varying MIMO systems based on neuron adaptive PID and neural...
The design of a nonlinear predictive controller, based on a fuzzy model is presented. The Takagi-Sugeno fuzzy model with an adaptive neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model...
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