An experimental comparison of $$\mathcal{H}_2$$ and $$\mathcal{H}_\infty$$ designs for an interferometer testbed

Flatness-Based Adaptive Fuzzy Control Of Spark-Ignited Engines

“…In a similar manner one obtains (8) Moreover, it holds that (9) Additionally, it holds that (10) while one also obtains (11) which also shows that the relative degree of the SIengine model is n = 3. It can be also confirmed that it holdsż…”

“…To make embedded control systems capable of functioning efficiently under variable operating conditions and despite modeling uncertainties and external perturbations, robustness of the control al--242 10.1515/jaiscr-2015-0011 gorithm has become a prerequisite [8][9][10]. An approach for obtaining such robustness has been the development of adaptive neurofuzzy control methods [11][12].…”

Adaptive fuzzy control of DC motors using state and output feedback

“…In the presence of non-Gaussian disturbances w, successful tracking of the reference signal is denoted by the H ∞ criterion [33][34][35] T 0 e T Qe dt ≤ 2…”

“…The paper extends the results of [24,32].Two cases can be distinguished: (i) control with feedback of the full state vector and (ii) control using only output feedback. In the first case the closed-loop system consists of the DC motor and an adaptive fuzzy controller based on H ∞ theory [33][34][35]. Neuro-fuzzy networks are used to approximate the unknown motor dynamics and subsequently this information is used for the generation of the control signal.…”

Adaptive fuzzy control for field-oriented induction motor drives

“…where e is the output error and q is the attenuation level that corresponds to the maximum singular value of the transfer function G(s) of the linearized equivalent of the system's model [18][19][20][21]. For measurable state vector x and uncertain functions f(x, t) and g(x, t), an appropriate control law for (69) is…”