198 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
Adaptive Control of Unknown Pure Feedback Systems with Pure State Constraints
This paper deals with the tracking control problem for a class of unknown
pure feedback system with pure state constraints on the state variables and
unknown time-varying bounded disturbances. An adaptive controller is presented
for such systems for the very first time. The controller is designed using the
backstepping method. While designing it, Barrier Lyapunov Functions is used so
that the state variables do not contravene its constraints. In order to cope
with the unknown dynamics of the system, an online approximator is designed
using a neural network with a novel adaptive law for its weight update. In the
stability analysis of the system, the time derivative of Lyapunov function
involves known virtual control coefficient with unknown direction and to deal
with such problem Nussbaum gain is used to design the control law. Furthermore,
to make the controller robust and computationally inexpensive, a novel
disturbance observer is designed to estimate the disturbance along with neural
network approximation error and the time derivative of virtual control input.
The effectiveness of the proposed approach is demonstrated through a simulation
study on the third-order nonlinear system
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
Nonlinear Flight Control Design Using Backstepping Methodology
The subject of nonlinear flight control design using backstepping control methodology is investigated in the dissertation research presented here. Control design methods based on nonlinear models of the dynamic system provide higher utility and versatility because the design model more closely matches the physical system behavior. Obtaining requisite model fidelity is only half of the overall design process, however. Design of the nonlinear control loops can lessen the effects of nonlinearity, or even exploit nonlinearity, to achieve higher levels of closed-loop stability, performance, and robustness. The goal of the research is to improve control quality for a general class of strict-feedback dynamic systems and provide flight control architectures to augment the aircraft motion. The research is divided into two parts: theoretical control development for the strict-feedback form of nonlinear dynamic systems and application of the proposed theory for nonlinear flight dynamics. In the first part, the research is built on two components: transforming the nonlinear dynamic model to a canonical strict-feedback form and then applying backstepping control theory to the canonical model. The research considers a process to determine when this transformation is possible, and when it is possible, a systematic process to transfer the model is also considered when practical. When this is not the case, certain modeling assumptions are explored to facilitate the transformation. After achieving the canonical form, a systematic design procedure for formulating a backstepping control law is explored in the research. Starting with the simplest subsystem and ending with the full system, pseudo control concepts based on Lyapunov control functions are used to control each successive subsystem. Typically each pseudo control must be solved from a nonlinear algebraic equation. At the end of this process, the physical control input must be re-expressed in terms of the physical states by eliminating the pseudo control transformations. In the second part, the research focuses on nonlinear control design for flight dynamics of aircraft motion. Some assumptions on aerodynamics of the aircraft are addressed to transform full nonlinear flight dynamics into the canonical strict-feedback form. The assumptions are also analyzed, validated, and compared to show the advantages and disadvantages of the design models. With the achieved models, investigation focuses on formulating the backstepping control laws and provides an advanced control algorithm for nonlinear flight dynamics of the aircraft. Experimental and simulation studies are successfully implemented to validate the proposed control method. Advancement of nonlinear backstepping control theory and its application to nonlinear flight control are achieved in the dissertation research
Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach
10.1109/TNN.2008.2003290IEEE Transactions on Neural Networks19111873-1886ITNN
An Adaptive Dynamic Surface Controller for Ultralow Altitude Airdrop Flight Path Angle with Actuator Input Nonlinearity
In the process of ultralow altitude airdrop, many factors such as actuator input dead-zone, backlash, uncertain external atmospheric disturbance, and model unknown nonlinearity affect the precision of trajectory tracking. In response, a robust adaptive neural network dynamic surface controller is developed. As a result, the aircraft longitudinal dynamics with actuator input nonlinearity is derived; the unknown nonlinear model functions are approximated by means of the RBF neural network. Also, an adaption strategy is used to achieve robustness against model uncertainties. Finally, it has been proved that all the signals in the closed-loop system are bounded and the tracking error converges to a small residual set asymptotically. Simulation results demonstrate the perfect tracking performance and strong robustness of the proposed method, which is not only applicable to the actuator with input dead-zone but also suitable for the backlash nonlinearity. At the same time, it can effectively overcome the effects of dead-zone and the atmospheric disturbance on the system and ensure the fast track of the desired flight path angle instruction, which overthrows the assumption that system functions must be known
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