O. Namaki-Shoushtari, A. Khaki-Sedigh,
Volume 8, Issue 1 (3-2012)
Abstract
When the process is highly uncertain, even linear minimum phase systems must sacrifice desirable feedback control benefits to avoid an excessive ‘cost of feedback’, while preserving the robust stability. In this paper, the problem of supervisory based switching Quantitative Feedback Theory (QFT) control is proposed for the control of highly uncertain plants. According to this strategy, the uncertainty region is suitably divided into smaller regions. It is assumed that a QFT controller-prefilter exits for robust stability and performance of the individual uncertain sets. The proposed control architecture is made up by these local controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the candidate local model behavior with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching for stability reasons. Besides, each controller is designed to be stable in the whole uncertainty domain, and as accurate in command tracking as desired in its uncertainty subset to preserve the robust stability from any failure in the switching.
M. Kamali, F. Sheikholeslam, J. Askari,
Volume 13, Issue 2 (6-2017)
Abstract
In this paper, a robust adaptive actuator failure compensation control scheme is proposed for a class of multi input multi output linear systems with unknown time-varying state delay and in the presence of unknown actuator failures and external disturbance. The adaptive controller structure is designed based on the SPR-Lyapunov approach to achieve the control objective under the specific assumptions and the SDU factorization method of the high frequency gain matrix is employed to drive the suitable form of the error equation. The two component controller structure with an integral term is used in order to compensate the effect of unknown state delay and external disturbance. Using a suitable Lyapunov-Krasovskii functional, it is shown that despite existing external disturbance and actuator failures, all closed loop signals are bounded and the plant Output asymptotically tracks the output of a stable reference model. Simulation results are provided to demonstrate the effectiveness of the proposed theoretical results.
A. Afrush, M. Shahriari-Kahkeshi,
Volume 15, Issue 2 (6-2019)
Abstract
This paper proposes an adaptive approximation-based controller for uncertain strict-feedback nonlinear systems with unknown dead-zone nonlinearity. Dead-zone constraint is represented as a combination of a linear system with a disturbance-like term. This work invokes neural networks (NNs) as a linear-in-parameter approximator to model uncertain nonlinear functions that appear in virtual and actual control laws. Minimal learning parameter (MLP) algorithm is proposed to decrease the computational load, the number of adjustable parameters, and to avoid the “explosion of learning parameters” problem. An adaptive TSK-type fuzzy system is proposed to estimate the disturbance-like term in the dead-zone description which further will be used to compensate the effect of the dead-zone, and it does not require the availability of the dead-zone input. Then, the proposed method based on the dynamic surface control (DSC) method is designed which avoids the “explosion of complexity” problem. Proposed scheme deals with dead-zone nonlinearity and uncertain dynamics without requiring the availability of any knowledge about them, and it develops a control input without singularity concern. Stability analysis shows that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to the vicinity of the origin. Simulation and comparison results verify the acceptable performance of the presented controller.
B. Yassine, Z. Fatiha, L. Chrifi-Alaoui,
Volume 16, Issue 1 (3-2020)
Abstract
This paper suggests novel sensorless speed estimation for an induction motor (IM) based on a stator current model reference adaptive system (IS-MRAS) scheme. The IS-MRAS scheme uses the error between the reference and estimated stator current vectors and the rotor speed. Observing rotor flux and the speed estimating using the conventional MRAS technique is confronted with certain problems related to the presence of the pure integrator and the rotor resistance causing offsets at low speeds, as proved by the most recent publications. These offsets are disastrous in sensorless control since these signals are no longer suitable to calculate of park angle (θs). This paper discusses the new MRAS approach (IS-MRAS) for on-line identification of the rotor resistance suitable for compensating offsets and solving problems of ordinary MRAS at low speed. This new MRAS approach used to estimate the components of the rotor flux and rotor speed without using the voltage model with on-line Setting parameters (Kp, K1) based on Type-2 fuzzy Logic. The results of the simulation and the experimental results are presented and show the effectiveness of the proposed technique.
Somayeh Rajabi, Hadi Chahkandi Nejad, Majid Reza Naseh,
Volume 21, Issue 0 (3-2025)
Abstract
In this paper, a Lyapunov-based adaptive 2nd order sliding mode controller is proposed to control the current in an active power filter (APF). The penetration of the APFs has been exponentially increased because of their high flexibility and less resonance problems. Moreover, they can compensate high range of current harmonics and reactive power. The voltage and current control loops have always been an interesting area for researchers since the satisfactory performance of the APF is highly dependent to these control loops. Sliding mode controller (SMC) is a mighty controller when uncertain conditions are considered. However, in order to reduce the chattering- high-frequency switching- and improve the steady state operation, stability, and robustness of the controller, it is usually decided to adaptively tune the gains of the controller. In this paper, a simple-structure adaptive SMC (ASMC) is proposed which can be implemented easily. This ASMC is showed to be stable using the Lyapunov theorem and proved with SIMULINK simulation that has less steady state error, less chattering, and faster dynamic response compared to the conventional SMC.
