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Similar to the case for the LSC dc voltage controller, the error in the current is processed by a PI controller designed as frac{{{text{d}}I}}{{{text{d}}t}} = U_{text{d}} = k_{text{p}} left( {I_{text{d}}^ - I_{text{d}} } right) + k_{text{i}} int {left( {I_{text{d}}^ - I_{text{d}} } right)} {text{d}}t, (21 where kp and ki are the proportional and integral gains.
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In the proposed MRPID controller, the errors with position and orientation are decomposed into different frequency components using discrete wavelet transform.
The control both in the WT and in the WPP level is realised by using PI controllers, for example, the PI controller in the WPP active power controller reduces the error between measured power at PCC (P meas_PCC) and the sum between the reference power and the primary response signal (P ref_demand and ΔP c), in the decision of the P ref_WT.
A decoupling adaptive controller uses the error between reference input, output and filtered outputs to produce control signals to the generator, forcing its outputs to behave like the reference model.
The inputs to fuzzy PID controller are the error and the change in error.
The adaptive nonlinear controller stabilizing the error system is designed by Lyapunov direct method and Matrosov theorem.
The inputs to the fuzzy controller are the error and change of error in the crank speed and the output is the applied voltage.
This fuzzy-PID controller takes the error signal of Euler's angles and the error of angular velocities of the chaser as its input items, and the driving force as its output.
Compare with traditional PID controllers, the error-dependent integration is employed in the proposed PID controller, in which more parameters are available to be tuned.
To design the sampled-data controller, the synchronization error system is represented as a switched system.
By increasing a single parameter in the controller, the tracking error can be arbitrarily reduced.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com