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Since plant/model mismatch is almost inevitable in practical applications, to enhance the robustness of this control system, the controllers based on the H∞ mixed sensitivity problem are designed in the PLS latent subspace.
The second contribution is the use of control theoretic techniques (PI controllers based on the Root Locus method) to model and design the feedback loops with desired dynamic performance.
The designed controllers based on the hierarchical control scheme can be divided into rotational controller and translational controller and their stability are validated by the Lyapunov stability theorem.
Using the data from a test vehicle with a V8 engine, we have developed a neural network engine model and neural network controllers based on the idea of sliding-mode control to achieve optimal torque control.
Here, we propose a method for reconstructing robust transcriptional controllers based on the structure of the controlled system instead.
Two fuzzy logic controllers have been developed using speed and mechanical power deviations, and a neural network has been designed to tune the gains of the fuzzy logic controllers based on the operating conditions of the biomass-based electric power plant.
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The controller based on the SPSA-based MF control algorithm is implemented on the Texas Instruments digital signal processor (DSP) TMS320VC33.
To control these unwanted chaotic oscillations, a nonlinear controller based on the backstepping nonlinear control theory is designed.
An optimal controller based on the disturbance accommodation control (DAC) theory is proposed for this load frequency control problem.
A controller based on the sliding mode control technique is proposed for the hybrid machine in this paper.
The controller based on the time-state control form, which switches the direction of the time axis, seems to be adequate to control vehicles that have to switch their traveling direction.
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