Abstract: This paper provides a comparative study on the
performances of standard PID and adaptive PID controllers tested on
travel angle of a 3-Degree-of-Freedom (3-DOF) Quanser bench-top
helicopter. Quanser, a well-known manufacturer of educational
bench-top helicopter has developed Proportional Integration
Derivative (PID) controller with Linear Quadratic Regulator (LQR)
for all travel, pitch and yaw angle of the bench-top helicopter. The
performance of the PID controller is relatively good; however, its
performance could also be improved if the controller is combined
with adaptive element. The objective of this research is to design
adaptive PID controller and then compare the performances of the
adaptive PID with the standard PID. The controller design and test is
focused on travel angle control only. Adaptive method used in this
project is self-tuning controller, which controller’s parameters are
updated online. Two adaptive algorithms those are pole-placement
and deadbeat have been chosen as the method to achieve optimal
controller’s parameters. Performance comparisons have shown that
the adaptive (deadbeat) PID controller has produced more desirable
performance compared to standard PID and adaptive (poleplacement).
The adaptive (deadbeat) PID controller attained very fast
settling time (5 seconds) and very small percentage of overshoot (5%
to 7.5%) for 10° to 30° step change of travel angle.
Abstract: Control of a semi-batch polymerization reactor using
an adaptive radial basis function (RBF) neural network method is
investigated in this paper. A neural network inverse model is used to
estimate the valve position of the reactor; this method can identify the
controlled system with the RBF neural network identifier. The
weights of the adaptive PID controller are timely adjusted based on
the identification of the plant and self-learning capability of RBFNN.
A PID controller is used in the feedback control to regulate the actual
temperature by compensating the neural network inverse model
output. Simulation results show that the proposed control has strong
adaptability, robustness and satisfactory control performance and the
nonlinear system is achieved.
Abstract: In this paper a PID control strategy using neural
network adaptive RASP1 wavelet for WECS-s control is proposed.
It is based on single layer feedforward neural networks with hidden
nodes of adaptive RASP1 wavelet functions controller and an infinite
impulse response (IIR) recurrent structure. The IIR is combined by
cascading to the network to provide double local structure resulting
in improving speed of learning. This particular neuro PID controller
assumes a certain model structure to approximately identify the
system dynamics of the unknown plant (WECS-s) and generate the
control signal. The results are applied to a typical turbine/generator
pair, showing the feasibility of the proposed solution.
Abstract: Multi-loop (De-centralized) Proportional-Integral-
Derivative (PID) controllers have been used extensively in process
industries due to their simple structure for control of multivariable
processes. The objective of this work is to design multiple-model
adaptive multi-loop PID strategy (Multiple Model Adaptive-PID)
and neural network based multi-loop PID strategy (Neural Net
Adaptive-PID) for the control of multivariable system. The first
method combines the output of multiple linear PID controllers,
each describing process dynamics at a specific level of operation.
The global output is an interpolation of the individual multi-loop
PID controller outputs weighted based on the current value of the
measured process variable. In the second method, neural network
is used to calculate the PID controller parameters based on the
scheduling variable that corresponds to major shift in the process
dynamics. The proposed control schemes are simple in structure with
less computational complexity. The effectiveness of the proposed
control schemes have been demonstrated on the CSTR process,
which exhibits dynamic non-linearity.
Abstract: A self tuning PID control strategy using reinforcement
learning is proposed in this paper to deal with the control of wind
energy conversion systems (WECS). Actor-Critic learning is used to
tune PID parameters in an adaptive way by taking advantage of the
model-free and on-line learning properties of reinforcement learning
effectively. In order to reduce the demand of storage space and to
improve the learning efficiency, a single RBF neural network is used
to approximate the policy function of Actor and the value function of
Critic simultaneously. The inputs of RBF network are the system
error, as well as the first and the second-order differences of error.
The Actor can realize the mapping from the system state to PID
parameters, while the Critic evaluates the outputs of the Actor and
produces TD error. Based on TD error performance index and
gradient descent method, the updating rules of RBF kernel function
and network weights were given. Simulation results show that the
proposed controller is efficient for WECS and it is perfectly
adaptable and strongly robust, which is better than that of a
conventional PID controller.