143 research outputs found
Adaptive Fuzzy Tracking Control for Nonlinear State Constrained Pure-Feedback Systems With Input Delay via Dynamic Surface Technique
This brief constructs the adaptive backstepping control scheme for a class of
pure-feedback systems with input delay and full state constraints. With the
help of Mean Value Theorem, the pure-feedback system is transformed into
strict-feedback one. Barrier Lyapunov functions are employed to guarantee all
of the states remain constrained within predefined sets. By introducing the
Pade approximation method and corresponding intermediate, the impact generated
by input delay on the output tracking performance of the system can be
eliminated. Furthermore, a low-pass filter driven by a newly-defined control
input, is employed to generate the actual control input, which facilitates the
design of backstepping control. To approximate the unknown functions with a
desired level of accuracy, the fuzzy logic systems (FLSs) are utilized by
choosing appropriate fuzzy rules, logics and so on. The minimal learning
parameter (MLP) technique is employed to decrease the number of nodes and
parameters in FLSs, and dynamic surface control (DSC) technique is leveraged to
avoid so-called "explosion of complexity". Moreover, smooth robust compensators
are introduced to circumvent the influences of external disturbance and
approximation errors. By stability analysis, it is proved that all of signals
in the closed-loop system are semi-globally ultimately uniform bounded, and the
tracking error can be within a arbitrary small neighbor of origin via selecting
appropriate parameters of controllers. Finally, the results of numerical
illustration are provided to demonstrate the effectiveness of the designed
method.Comment: arXiv admin note: text overlap with arXiv:2310.1540
Finite-Time Adaptive Fuzzy Tracking Control for Nonlinear State Constrained Pure-Feedback Systems
This paper investigates the finite-time adaptive fuzzy tracking control
problem for a class of pure-feedback system with full-state constraints. With
the help of Mean-Value Theorem, the pure-feedback nonlinear system is
transformed into strict-feedback case. By employing finite-time-stable like
function and state transformation for output tracking error, the output
tracking error converges to a predefined set in a fixed finite interval. To
tackle the problem of state constraints, integral Barrier Lyapunov functions
are utilized to guarantee that the state variables remain within the prescribed
constraints with feasibility check. Fuzzy logic systems are utilized to
approximate the unknown nonlinear functions. In addition, all the signals in
the closed-loop system are guaranteed to be semi-global ultimately uniformly
bounded. Finally, two simulation examples are given to show the effectiveness
of the proposed control strategy
Prescribed Performance Fuzzy Adaptive Output-Feedback Control for Nonlinear Stochastic Systems
A prescribed performance fuzzy adaptive output-feedback control approach is proposed for a class of single-input and single-output nonlinear stochastic systems with unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output-feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. A simulation example is provided to show the effectiveness of the proposed approach
Observer and command-filter-based adaptive fuzzy output feedback control of uncertain nonlinear systems
In this paper, observer and command-filterbased adaptive fuzzy output feedback control is proposed for a class of strict-feedback systems with parametric uncertainties and unmeasured states. First, fuzzy logic systems are used to approximate the unknown and nonlinear functions. Next, a fuzzy state observer is developed to estimate the immeasurable states. Then, command-filtered backstepping control is designed to avoid the explosion of complexity in the backstepping design, and compensating signals are introduced to remove the effect of the errors caused by command filters. The proposed method guarantees that all signals in the closed-loop systems are bounded. The main contributions of this paper are the proposed control method can overcome two problems of linear in the unknown system parameter and explosion of complexity in backstepping-design methods and it does not require that all of the states of the system are measured directly. Finally, two examples are provided to illustrate its effectiveness.Jinpeng Yu, Peng Shi, Wenjie Dong and Haisheng Y
Adaptive Backstepping Fuzzy Control Based on Type-2 Fuzzy System
A novel indirect adaptive backstepping control approach based on type-2 fuzzy system is developed for a class of nonlinear systems. This approach adopts type-2 fuzzy system instead of type-1 fuzzy system to approximate the unknown functions. With type-reduction, the type-2 fuzzy system is replaced by the average of two type-1 fuzzy systems. Ultimately, the adaptive laws, by means of backstepping design technique, will be developed to adjust the parameters to attenuate the approximation error and external disturbance. According to stability theorem, it is proved that the proposed Type-2 Adaptive Backstepping Fuzzy Control (T2ABFC) approach can guarantee global stability of closed-loop system and ensure all the signals bounded. Compared with existing Type-1 Adaptive Backstepping Fuzzy Control (T1ABFC), as the advantages of handling numerical and linguistic uncertainties, T2ABFC has the potential to produce better performances in many respects, such as stability and resistance to disturbances. Finally, a biological simulation example is provided to illustrate the feasibility of control scheme proposed in this paper
Adaptive Backstepping Control for a Class of Uncertain Nonaffine Nonlinear Time-Varying Delay Systems with Unknown Dead-Zone Nonlinearity
An adaptive backstepping controller is constructed for a class of nonaffine nonlinear time-varying delay systems in strict feedback form with unknown dead zone and unknown control directions. To simplify controller design, nonaffine system is first transformed into an affine system by using mean value theorem and the unknown nonsymmetric dead-zone nonlinearity is treated as a combination of a linear term and a bounded disturbance-like term. Owing to the universal approximation property, fuzzy logic systems (FLSs) are employed to approximate the uncertain nonlinear part in controller design process. By introducing Nussbaum-type function, the a priori knowledge of the control gains signs is not required. By constructing appropriate Lyapunov-Krasovskii functionals, the effect of time-varying delay is compensated. Theoretically, it is proved that this scheme can guarantee that all signals in closed-loop system are semiglobally uniformly ultimately bounded (SUUB) and the tracking error converges to a small neighbourhood of the origin. Finally, the simulation results validate the effectiveness of the proposed scheme
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