اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدAdaptive High Order Sliding Mode Observer Based Fault Reconstruction for a Class of Nonlinear Uncertain Systems: Application to PEM Fuel Cell System
75
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
This paper focuses on observer based fault reconstruction for a class of nonlinear uncertain systems with Lipschitz nonlinearities. An adaptive-gain Super-Twisting (STW) observer is developed for observing the system states, where the adaptive law compensates the uncertainty in parameters. The inherent equivalent output error injection feature of STW algorithm is then used to reconstruct the fault signal. The performance of the proposed observer is validated through a Hardware-In-Loop (HIL) simulator which consists of a commercial twin screw compressor and a real time Polymer Electrolyte Membrane fuel cell emulation system. The simulation results illustrate the feasibility and effectiveness of the proposed approach for application to fuel cell systems.
قيم البحث
اقرأ أيضاً
In this paper, a full-bridge boost power converter topology is studied for power factor control, using output high order sliding mode control. The AC/DC converters are used for charging the battery and super-capacitor in hybrid electric vehicles from
the utility. The proposed control forces the input currents to track the desired values, which can controls the output voltage while keeping the power factor close to one. Super-twisting sliding mode observer is employed to estimate the input currents and load resistance only from the measurement of output voltage. Lyapunov analysis shows the asymptotic convergence of the closed loop system to zero. Simulation results show the effectiveness and robustness of the proposed controller.
We consider the problem of asymptotic reconstruction of the state and parameter values in systems of ordinary differential equations. A solution to this problem is proposed for a class of systems of which the unknowns are allowed to be nonlinearly pa
rameterized functions of state and time. Reconstruction of state and parameter values is based on the concepts of weakly attracting sets and non-uniform convergence and is subjected to persistency of excitation conditions. In absence of nonlinear parametrization the resulting observers reduce to standard estimation schemes. In this respect, the proposed method constitutes a generalization of the conventional canonical adaptive observer design.
In this paper, a novel adaptive-gain Second Order Sliding Mode (SOSM) observer is proposed for multicell converters by considering it as a class of hybrid systems. The aim is to reduce the number of voltage sensors by estimating the capacitor voltage
s only from the measurement of load current. The proposed observer is proven to be robust in the presence of perturbations with emph{unknown} boundary. However, the states of the system are only partially observable in the sense of observability rank condition. Due to its switching behavior, a recent concept of $Z(T_N)$ observability is used to analysis its hybrid observability, since its observability depends upon the switching control signals. Under certain condition of the switching sequences, the voltage across each capacitor becomes observable. Simulation results and comparisons with Luenberger switched observer highlight the effectiveness and robustness of the proposed observer with respect to output measurement noise and system uncertainties (load variations).
In this paper, we investigate a constrained optimal coordination problem for a class of heterogeneous nonlinear multi-agent systems described by high-order dynamics subject to both unknown nonlinearities and external disturbances. Each agent has a pr
ivate objective function and a constraint about its output. A neural network-based distributed controller is developed for each agent such that all agent outputs can reach the constrained minimal point of the aggregate objective function with bounded residual errors. Two examples are finally given to demonstrate the effectiveness of the algorithm.
This paper studies the extremum seeking control (ESC) problem for a class of constrained nonlinear systems. Specifically, we focus on a family of constraints allowing to reformulate the original nonlinear system in the so-called input-output normal f
orm. To steer the system to optimize a performance function without knowing its explicit form, we propose a novel numerical optimization-based extremum seeking control (NOESC) design consisting of a constrained numerical optimization method and an inversion based feedforward controller. In particular, a projected gradient descent algorithm is exploited to produce the state sequence to optimize the performance function, whereas a suitable boundary value problem accommodates the finite-time state transition between each two consecutive points of the state sequence. Compared to available NOESC methods, the proposed approach i) can explicitly deal with output constraints; ii) the performance function can consider a direct dependence on the states of the internal dynamics; iii) the internal dynamics do not have to be necessarily stable. The effectiveness of the proposed ESC scheme is shown through extensive numerical simulations.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
