The paper proposes a framework, which involves using an interval model for describing the uncertain or variable dynamics of the process. The framework employs a particle swarm optimization algorithm for obtaining the best performing PID controller with regard to several possible criteria, but at the same time taking into account the complementary sensitivity function constraints, which ensure robustness within the bounds of the uncertain parameters’ intervals.
COBISS.SI-ID: 11237972
The RECCo control algorithm, presented in this article, is based on the fuzzy rule-based (FRB) system named ANYA which has non-parametric antecedent part. The RECCo does not require any off-line training or any type of model of the controlled process (e.g. differential equations). The RECCo control algorithm autonomously learns and evolves the structure and adapts its parameters in an online unsupervised manner.
COBISS.SI-ID: 11394900
An effective but yet simple approach is introduced to automatically attain a dynamic feedforward control law for non-linear dynamic systems represented by discrete-time local model networks (LMN). In this context, feedback linearization is applied to the generic model structure of LMN and the resulting input transformation is used as model inverse. This general and automated approach for model inversion is applicable even when the overall model complexity may be high.
COBISS.SI-ID: 11294804
This paper presents the design and implementation of a unique control system for a smart hoist, a therapeutic device that is used in rehabilitation of walking. The control system features a unique human-machine interface that allows the human to intuitively control the system just by moving or rotating its body. The prototype of the complete system was tested by conducting a 6-runs experiment on 11 subjects and results are showing that the proposed control system interface is indeed intuitive and simple to adopt by the user.
COBISS.SI-ID: 11361364