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Finally, validity of the fractional order controller and the advantages over an integer order controller are examined by numerical simulation.
Modifications are made for our previous design to incorporate the performance-limiting element, i.e. actuator saturation, to acquire lower order controller, and to achieve faster computation.
From Fig. 8, the maximum amount of the changes around of nominal value is increased by using of reduced order controller and it is equal to 3% which is equivalent to 3.074 rad/s that is increased to 0.006 rad/s.
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The result is non-conservative in the case of full order controllers and the well known LMI technique for the design of full order dynamic controllers is recovered as a special case.
The plant is synthesized based on a pre-designed low-order controller, and robust stability criterion of the overall control system in the presence of Gaussian plant parametric perturbations is also included.
Further, we present performance comparisons between full-order controller and reduced-order controller.
Compared with most of the adaptive neural controllers, the smart neural controller (in the operational phase) is a static and low-order controller, and thus needs much less computational resources, and is more feasible in practical implementation.
Both the cases of fixed order controller tuning and controller structure learning are discussed and two different design approaches are provided.
Using this model, three controllers' viz., PID controller, Internal model controller and Fractional order controller are developed and their responses are compared.
This paper deals with fractional-order controllers and a method of their synthesis.
This paper proposes a method for the design of reduced-order controllers and the proposed method is applied to the active suspension system in the Laboratoire d'Automatique de Grenoble, France.
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