Finite-Time Robust Intelligent Control of Strict-Feedback Nonlinear Systems With Flight Dynamics Application.

Event-triggered tracking control of strict-feedback nonlinear systems with output constraints via an adaptive constraint-handling mechanism.

This work investigates the adaptive finite-time tracking problem for a class of strict-feedback nonlinear systems with parametric uncertainties and symmetric constant output constraint. A tan-type Barrier Lyapunov Function (BLF) is proposed to ensure the boundedness of the output tracking error, and a new tuning function is given to eliminate the effect of uncertainties. It is theoretically shown that the control scheme not only can assure the finite-time convergence of system output tracking error into a small set around zero but also can guarantee that the output constraint is not overstepped. The effectiveness of the proposed finite-time control scheme is further verified with a numerical example.

A new robust tracking control approach is proposed for strict-feedback nonlinear systems with state and input constraints. The constraints are tackled by extending the control input as an extended state and introducing an integral barrier Lyapunov function (IBLF) to each step in a backstepping procedure. This extends current research on barrier Lyapunov functions(BLFs)-based control for nonlinear systems with state constraints to IBLF-based control for strict-feedback nonlinear systems with state and input constraints. Since the IBLF allows the original constraints to be mixed with the error terms, the use of IBLF decreases conservatism in barrier Lyapunov functions-based control. In the backstepping procedure, neural networks (NNs) with projection modifications are applied to estimate system uncertainties, due to their ability in guaranteeing estimators in a given bounded area. To facilitate the use of the once-differentiable NNs estimators in the backstepping procedure, the virtual controllers are passed through command filters. Finally, simulation results are presented to illustrate the feasibility and effectiveness of the proposed control.

Adaptive optimal backstepping control for strict-feedback nonlinear systems with time-varying partial output constraints

In this paper, the problem of fixed-time adaptive tracking control for strict-feedback nonlinear systems is studied. By introducing a new tunnel constraint variable, a new adaptive and practical fixed-time controller is constructed based on fuzzy control, fixed-time Lyapunov stability theory and backstepping algorithm. Fuzzy logic system is introduced to approximate the unknown term of the system. Theoretical analysis shows that under the control strategy, and the tracking error converges to a small neighborhood of the origin within fixed-time intervals, where the convergence time is independent of the initial state of the system. At the same time, all the signals of the closed-loop system are bounded. Simulation results show that the proposed controller is effective.

This article studies neural-network (NN) adaptive control for strict-feedback nonlinear systems with matched uncertainties and event-triggered communication. Radial basis function NNs (RBFNNs) are used in the backstepping design approach to compensate for nonlinear uncertain functions. The concept of valid compact sets for RBFNN adaptive controllers is proposed, where a local RBFNN approximator is defined and the closed-loop state can remain. To guarantee the existence of such valid compact sets, a new property on RBFNNs is presented, which shows that, in some properly designed RBFNNs, the norm of their ideal weight vectors can always become arbitrarily small. By utilizing this property, the selections on valid compact sets are investigated, resulting in rigorous proof on RBFNN adaptive controllers to solve a local tracking problem with a given smooth enough reference signal. Subsequently, to save limited communication resources, a Zeno-free event-triggering mechanism in controller-to-actuator channels is proposed. Under this event-triggered adaptive controller, the corresponding tradeoff among the tracking performance, computational burden, and communication consumption is analyzed. Furthermore, two extensions are made to the general local function approximator, which is in the form of a weight vector multiplying a group of basis functions, and to the communication in sensor-to-controller channels. Finally, several simulation results are provided to illustrate the efficiency and feasibility of the obtained results.

ABSTRACTThis paper focuses on composite nonlinear feedback (CNF) controller design for tracking control problem of strict-feedback nonlinear systems with input saturation to address the improvement of transient performance. First, without considering the input saturation, a stabilisation control law is designed by using standard backstepping technique for the nonlinear system, then a feedforward control law is added to the backstepping-based stabilisation control law to construct a tracking control law. The tracking control law is tuned to drive the output of the closed-loop system to track a command input with quick response. Then, an additional nonlinear feedback law is constructed and combined with the tracking control law to obtain a CNF control law. The role of this additional nonlinear feedback law is to smoothly change the damping ratio of the closed-loop system while the system output approaches the command input, and to reduce overshoot caused by the tracking control law. It is shown that the extra-adding nonlinear feedback part does not cause the loss of stability of the closed-loop system in its attractive basin.

This paper presents novel Takagi-Sugeno (T-S) fuzzy model-based robust finite time control methods for a class of uncertain nonlinear systems. First, the nonlinear system is approximated by a suitable T-S fuzzy model in which most of the parameters can be computed offline. Then, a T-S fuzzy model-based nonsingular terminal sliding mode control (TS-NTSMC) method is designed based on a proposed nonsingular terminal sliding mode (NTSM) surface. This method not only alleviates the online computational burden, but also preserves the advantages of fast and finite time convergence, high tracking precision, and robustness to uncertainties and disturbances. However, the discontinuous function [Formula: see text] causes the chattering phenomenon, which is unavoidable. In order to attenuate the chattering, maintain the key properties of the TS-NTSMC method and achieve higher control performance, a novel T-S fuzzy model-based super-twisting higher order nonsingular terminal sliding mode control (TS-STHONTSMC) method is proposed. The global finite time stability of the resulting closed-loop systems is analytically proven herein. Two numerical simulations are presented to illustrate the effectiveness and applicability of the proposed control methods and to validate the theoretical results.

This paper design an output tracking controller for a class of uncertain nonlinear systems to address the input quantization and output tracking funnel control problem. By the traditional adaptive control and backstepping technique, an adaptive quantized funnel control scheme is designed. It is shown that the presented controller can ensure that arbitrary closed-loop signals are globally uniformly bounded, and the output tracking error is bounded, and held in the prescribed funnel performance functions. The presented control scheme is checked through the simulation result.

Adaptive fuzzy asymptotic tracking control of uncertain nonaffine nonlinear systems with non-symmetric dead-zone nonlinearities

Increasing competition in the global marketplace demands products be adaptable to the changes of functional requirements, operation environments, and technology advancement. Adaptable design can be employed for developing adaptable products and systems that can be changed, such as reconfigured and upgraded, during product operation stage. In addition, since product and operating parameters are normally subject to influence of uncertainties that lead to variations of functional performance, robust design needs to be carried out to identify the design whose functional performance is the least sensitive to the variations of product and operating parameters. This research aims at developing a robust adaptable design approach such that the product is adaptable to various changes in requirements and parameters during the product operation stage, meanwhile the product functional performance measures are the least sensitive to parameter variations. First, the requirements, parameters, and variations of these parameters are defined as numerical functions of a time parameter. Then, mathematical models are established to describe the relationships among product design/operating parameters, functional performances, and variations of these parameters and performances. Furthermore, an optimization-based model is developed to achieve the robust adaptable design. A case study has also been developed to demonstrate the effectiveness of the newly developed robust adaptable design method.

Increasing competition in the global marketplace demands products be adaptable to the changes of functional requirements, operation environments, and technology advancement. Adaptable design can be used for developing adaptable products that can be easily changed, such as reconfigured or upgraded, in the product operation stage. In addition, since the uncertainties in product parameters and environment parameters usually lead to the variations of functional performance, robust design has to be carried out to identify the design whose functional performance is the least sensitive to the variations of product and environment parameters. In this research, a robust adaptable design method is developed to identify the product whose parameter values can be changed in the product operation stage. First, a mathematical model is established to describe the relationships among product functional performances, parameters, and variations of these parameters. Then, an optimization-based design model is developed to achieve the robust adaptable design. A case study has also been developed to demonstrate the effectiveness of the newly developed robust adaptable design method.

Two types of cellular system designs are possible for a production environment consisting of dynamic part population: adaptive and robust layouts. Cellular manufacturing system designs in literature show that some of the cells formed have large number of machines compared to other cells. This paper is mainly concerned with developing suitable mathematical model so that more or less same number of machines in every cell is possible for the adaptive and robust designs. A genetic algorithm-based solution procedure is used to solve the problem. The suggested designs are giving less variation in cell size than adaptive and robust designs in literature.

Simplified adaptive neural control of strict-feedback nonlinear systems

Fixed-Time Control for Strict-Feedback Nonlinear Systems