In this paper we aim to survey the performance of a non-linear H∞ method and a new proposed controller on a magnetic levitation model (maglev). The proposed controller is a new model-free adaptive design approach using an adaptive-fuzzy procedure based on feedback linearization. The main idea of the new controller comprises two steps: first, by means of the feedback linearization method, a measured signal is taken to a specific level with an error less than a defined value and second, proposed rules are applied to the system to keep the error near zero. The major advantage of new controller is that there is no need for identification of the system dynamics and only the output error is required.
In this paper, constrained memory state-feedback H∝ control for a half-car model of an active vehicle suspension system with input time-delay in the presence of external disturbance has been investigated. Its prime goal is to improve the inherent trade-offs among power consumption, handling performance, ride quality, and suspension travel. The tire deflections and the suspension deflections are constrained by their peak response values in time domain using the generalized H2 (GH∝) norm (energy-to-peak) performance, while the ride comfort performance of the suspension system is optimized by notion of the H∝ control (energy-to-energy) to measure the body accelerations including both the heaving and the pitching motions. Similar to the well-known prediction-based methods, the prediction vector of the system is achieved to construct the memory state-feedback controller. Using the prediction vector, sufficient conditions guaranteeing closed-loop system stability as well as disturbance attenuation are obtained as some delay-dependent linear matrix inequalities (LMIs). In addition, some LMIs are added to limit the gain of the controller. In the case of feasibility, obtained LMIs provide the stabilizing gain of the memory controller. The proposed approach is applied to a half-car model of an active suspension system considering the actuator time-delay to illustrate the effectiveness of the proposed method.
This study investigates the prediction-based (dynamic) stabilization of linear systems with input delay in the presence of external disturbances and multiplicative noise modelled as Itô type stochastic differential equations. Conventional memory-less (static) controllers are widely used for the stabilization of both deterministic and stochastic delayed systems. However, using these methods the upper bound for delay is strongly restricted. Motivated by acceptable performances of dynamic controllers for deterministic delayed systems, the extension of these methods for stochastic delayed systems is considered in this paper. The structure of the dynamic controller for stabilization of stochastic delayed systems is firstly derived utilizing the prediction vector. Then two sufficient conditions are given in the form of linear matrix inequalities that in the case of feasibility provide the stabilizing gain of the controller. Finally, simulation results are given to illustrate the effectiveness of the proposed method.
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