Class Of Pure-feedback Nonlinear Systems Research Articles

Finite-time adaptive prescribed performance DSC for pure feedback nonlinear systems with input quantization and unmodeled dynamics

<abstract><p>This paper presents a new prescribed performance-based finite-time adaptive tracking control scheme for a class of pure-feedback nonlinear systems with input quantization and dynamical uncertainties. To process the input signal, a new quantizer combining the advantages of a hysteresis quantizer and uniform quantizer has been used. Radial basis function neural networks have been utilized to approximate unknown nonlinear smooth functions. An auxiliary system has been employed to estimate unmodeled dynamics by producing a dynamic signal. By introducing a hyperbolic tangent function and performance function, the tracking error was made to fall within the prescribed time-varying constraints. Using modified dynamic surface control (DSC) technology and a finite-time control method, a novel finite-time controller has been designed, and the singularity problem of differentiating each virtual control scheme in the existing finite-time control scheme has been removed. Theoretical analysis shows that all signals in the closed-loop system are semi-globally practically finite-time stable, and that the tracking error converges to a prescribed time-varying region. Simulation results for two numerical examples have been provided to illustrate the validity of the proposed control method.</p></abstract>

Adaptive fixed-time tracking control for a class of nonlinear pure-feedback systems: A relative threshold event-triggered strategy

In this paper, a fixed-time controller under the mechanism of event-trigger is designed for a class of nonlinear pure-feedback systems with non-differentiable non-affine functions. By properly modeling non-affine terms, the limitation of the partial derivatives of non-affine functions is eliminated. In our design process, we first develop a fixed-time adaptive controller using decoupling method. Then, a relative threshold event-trigger mechanism (ETM) is introduced in Section 3.1. The proposed controller can not only stabilize the system within a fixed-time, but also save communication resources more effectively. Lastly, the feasibility of the proposed control scheme is verified by two simulation examples.

Output regulation problem of a class of pure-feedback nonlinear systems via adaptive neural control

In the paper, a control algorithm for output regulation problem of nonlinear pure-feedback systems with unknown functions is proposed. The main contributions of the proposed method are not only to avoid Assumptions of unknown functions, but also adopt a non-backstepping control scheme. First, a high-gain state observer with disturbance signals is designed based on the new system that has been converted. Second, an internal model with the observer state is established. Finally, based on Lyapunov analysis and the neural network approximation theory, the control algorithm is proposed to ensure that all the signals of the closed-loop system are the semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. Three simulation studies are worked out to show the effectiveness of the proposed approach.

Observer-Based Adaptive Fuzzy Predefined Performance Control of a Class of Nonlinear Pure-Feedback Systems with Input Delay

This paper presents a problem of observer-based adaptive fuzzy predefined performance control of a class of nonlinear pure-feedback systems with input delay and unknown control direction. Compared with the existing research, a novel predefined performance controller is proposed, which relaxes the assumption that the initial error is known. In addition, it is difficult to design the controllers due to input delay and nonaffine properties of the pure-feedback systems, which can be simplified by Pade approximation. Moreover, dynamic surface control and Nussbaum functions are applied to overcome the calculation explosion caused by repeated differentiations and unknown control direction, respectively. Based on the above methods, an adaptive fuzzy predefined performance controller is proposed, and it is proved that all the signals of a closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). The tracking errors converge within a predefined range, while the observer estimation errors converge within a small zero region. Finally, the simulation results demonstrate the effectiveness of the proposed control system.

Finite-Time Adaptive Tracking Control for a Class of Pure-Feedback Nonlinear Systems with Disturbances via Decoupling Technique

This paper addresses the finite-time adaptive tracking control problem for a class of pure feedback nonlinear systems whose nonaffine functions may not be differentiable. By properly modeling the nonaffine function, the design difficulty of the pure feedback structure is overcome without using the median value theorem. In our design procedure, an finite-time adaptive controller is elaborately developed using the decoupling technology, which eliminates the limitation assumption on the partial derivatives of nonaffine functions. Furthermore, the constructed controller can stabilize the system within a finite-time so that all signals in the closed-loop system are semiglobally uniformly finite-time bounded (SGUFB), while ensuring the tracking performance. Finally, the simulation results prove the effectiveness of the proposed method.

Adaptive neural funnel control for a class of pure-feedback nonlinear systems with event-trigger strategy

In this paper, an event-trigger-based adaptive funnel control problem is considered for a class of pure-feedback nonlinear systems. Due to the nonaffine variables existed in the virtual controls of pure-feedback systems, the implicit function theorem and mean-value theorem are adopted to guarantee the existence of the ideal virtual controls, which can be effectively approximated by neural networks. Then, a novel event-triggered control strategy is designed to consume less communication resources. The event-triggered condition depends on the amplitudes of the control input, the tracking error and a fixed threshold, which makes the control more flexible in real applications. Furthermore, the proposed control scheme ensures the transient and steady state performance for the tracking errors by constructing a funnel constraint function. Also, the stability analysis proves that all the signals of the closed-loop system are uniformly ultimately bounded. Finally, the feasibility and effectiveness of the proposed control scheme are verified through the simulation.

Prescribed Performance Tracking Control of a Class of Uncertain Pure-Feedback Nonlinear Systems With Input Saturation

In this paper, we address the issue of the prescribed performance control of a class of pure-feedback nonlinear systems with uncertainties and input saturation. The active disturbance rejection control is adopted at each step of the backstepping technology. In detail, the extended state observer is employed to estimate unknown functions to compensate for uncertain items. The tracking differentiator is used to take place of the derivative of the virtual control signals, which overcomes the repeated differentiations of nonlinear functions. The control input limitation is handled with the auxiliary system, and predefined tracking performance functions are utilized to improve the tracking accuracy and speed. By means of the input-to-state stability and Lyapunov stability theory, the tracking error is proven to converge to arbitrarily small neighborhood of the origin. Two simulation examples are provided to demonstrate the effectiveness of the presented results.

Adaptive Dynamic Surface Control for a Class of Nonlinear Pure-Feedback Systems with Parameter Drift

In order to solve the problem of unknown parameter drift in the nonlinear pure-feedback system, a novel nonlinear pure-feedback system is proposed in which an unconventional coordinate transformation is introduced and a novel unconventional dynamic surface algorithm is designed to eliminate the problem of “calculation expansion” caused by the use of backstepping in the pure-feedback system. Meanwhile, a sufficiently smooth projection algorithm is introduced to suppress the parameter drift in the nonlinear pure-feedback system. Simulation experiments demonstrate that the designed controller ensures the global and ultimate boundedness of all signals in the closed-loop system and the appropriate designed parameters can make the tracking error arbitrarily small.

Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered Strategy

In this paper, an adaptive event-triggered tracking control problem is considered for a class of pure-feedback nonlinear systems with output constraints. The mean value theorem is used to transform the pure-feedback system in non-affine form into a system in affine form. In addition, the radial basis function neural network (RBF NN) control is used to approximate the unknown nonlinear function in the system and the tracking error of the controller is limited to a small constant boundary by using the positive obstacle Lyapunov function. An adaptive controller for a class of pure-feedback systems is established, which based on the backstepping control theory and event-triggered control theory, it can ensure all the closed-loop signals are bounded and avoid the Zeno-behavior. The simulation results prove the effectiveness of the controller design.

Robust adaptive prescribed performance dynamic surface control for uncertain nonlinear pure-feedback systems

A robust adaptive prescribed performance dynamic surface control design procedure for a class of nonlinear pure-feedback systems with both unknown vector parameters and unmodeled dynamics is presented. The unmodeled dynamics lie within some bounded functions, which are assumed to be partially known. A state transformation and an auxiliary system are proposed to avoid using the cumbersome formula to handle the non-affine structure. A radical-type Lyapunov function and L function are designed to ensure the prescribed performance of the pure-feedback system. Bounded value of finding the partial derivative of the non-affine function is avoided by designing special virtual controllers in using dynamic surface control. As illustrated by examples, the proposed adaptive prescribed performance dynamic surface control scheme is shown to guarantee the semi-global uniformly ultimately bounded, and the dynamic performance and steady-state performance of the tracking error depend on the prescribed performance functions.

Event-triggering based adaptive neural tracking control for a class of pure-feedback systems with finite-time prescribed performance

In this paper, a finite-time tracking control problem is considered for a class of pure-feedback nonlinear systems with event-triggered strategy. The implicit function theorem and the mean value theorem are used to transform the pure-feedback nonlinear systems into strict feedback nonlinear systems. The neural network is adopted to approximate the unknown function and the tracking error is limited to a pre-given boundary by prescribed performance at a finite time. In addition, an improved event-triggered control strategy is proposed to obtain a larger threshold, and also the proposed controller can avoid the Zeno-behavior. Based on Lyapunov stability theory, the adaptive neural network controller can ensure that all the signals in the closed-loop system are uniformly ultimately bounded. Finally, the feasibility of this control scheme is proved by simulation.

Observer-Based Fuzzy Adaptive Event-Triggered Control for Pure-Feedback Nonlinear Systems With Prescribed Performance

This paper studies the problem of fuzzy adaptive event-triggered control for a class of pure-feedback nonlinear systems, which contain unknown smooth functions and unmeasured states. Fuzzy logic systems are adopted to approximate unknown smooth functions and a fuzzy state observer is designed to estimate unmeasured states. Via the event-triggered control technique, the control signal of the fixed threshold strategy is obtained. By converting the tracking error into a new virtual error variable, an observer-based fuzzy adaptive event-triggered prescribed performance control strategy is designed. The key advantage is that the proposed method does not require a priori knowledge of partial derivatives of system functions, i.e., it relaxes the restrictive condition that the partial derivatives of system functions need to be known for pure-feedback nonlinear systems. Simulation results confirm the efficiency of the proposed method.

Practical Adaptive Fuzzy Control of Nonlinear Pure-Feedback Systems With Quantized Nonlinearity Input

This paper investigates the fuzzy adaptive practical tracking problem for a class of nonlinear pure-feedback systems with quantized input signal. In the control scheme design process, the considered system is transformed into a plant with a strict-feedback form by borrowing the mean value theorem of differential, then fuzzy logic systems are used to compensate for some uncertain nonlinearities in the considered plant and the classical adaptive technique is employed to handle some unknown parameters. In the backstepping design, some non-negative switching functions are introduced to develop the desired fuzzy controller, and Barbalat’s lemma is used to analyze the stability and the control performance of the closed-loop system. It can be shown that under the novel adaptive fuzzy controller, all the closed-loop signals are semiglobally uniformly ultimately bounded, and especially the tracking error satisfies the accuracy assigned a priori . A simulation example is presented to verify the effectiveness of the proposed control method.

Understanding gaps in perception surrounding long-acting injectable antipsychotic use within the forensic system

In this paper, a finite-time tracking control problem is considered for a class of pure-feedback nonlinear systems with event-triggered strategy. The implicit function theorem and the mean value theorem are used to transform the pure-feedback nonlinear systems into strict feedback nonlinear systems. The neural network is adopted to approximate the unknown function and the tracking error is limited to a pre-given boundary by prescribed performance at a finite time. In addition, an improved event-triggered control strategy is proposed to obtain a larger threshold, and also the proposed controller can avoid the Zeno-behavior. Based on Lyapunov stability theory, the adaptive neural network controller can ensure that all the signals in the closed-loop system are uniformly ultimately bounded. Finally, the feasibility of this control scheme is proved by simulation.

Neural network based adaptive dynamic surface control of nonaffine nonlinear systems with time delay and input hysteresis nonlinearities

In this paper, neural networks (NNs)-based adaptive dynamic surface control is investigated for a class of pure-feedback nonlinear systems with unknown time delay and input hysteresis nonlinearities. The design difficulty appeared in this paper due to time delay is handled by combining neural networks with finite covering lemma instead of using Krasovskii functionals, and the assumptions on the time-delay functions are removed based the finite covering lemma and NNs. Meanwhile, completely unknown backlash-like hysteresis control input that frequently exists in practice is also considered. By combining the adaptive backstepping recursive design technique with the universal approximation ability of neural networks, an adaptive neural control algorithm is systemically designed for the system under consideration, and the explosion of complexity exists in traditional backstepping design is avoided by using dynamic surface technique. It is shown that the designed adaptive controller can guarantee that all the signals are ultimately uniformly bounded and the desired signal can be tracked with a small domain of the origin. A numerical example and an example of a real plant for adjustable metal cutting system are provided to show the feasibility of the proposed method.

Adaptive neural network-based control for a class of nonlinear pure-feedback systems with time-varying full state constraints

In this paper, an adaptive neural network &#x0028 NN &#x0029 control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions &#x0028 BLFs &#x0029 with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.

Adaptive dynamic surface-based differential games for a class of pure-feedback nonlinear systems with output constraints

ABSTRACTThis paper investigates the zero-sum differential game problem for a class of uncertain nonlinear pure-feedback systems with output constraints and unknown external disturbances. A barrier Lyapunov function is introduced to tackle the output constraints. By constructing an affine variable at each dynamic surface control design step rather than utilising the mean-value theorem, the tracking control problem for pure-feedback systems can be transformed into an equivalent zero-sum differential game problem for affine systems. Then, the solution of associated Hamilton–Jacobi–Isaacs equation can be obtained online by using the adaptive dynamic programming technique. Finally, the whole control scheme that is composed of a feedforward dynamic surface controller and a feedback differential game control strategy guarantees the stability of the closed-loop system, and the tracking error is remained in a bounded compact set. The simulation results demonstrate the effectiveness of the proposed control scheme.

Fuzzy observer-based sampled-data control for a class of pure-feedback nonlinear systems

This paper aims at the sampled-data control problem for a class of pure-feedback nonlinear systems. A fuzzy state observer is constructed to evaluate the unavailable states. In this process, fuzzy logic systems are applied to approximate the uncertain nonlinear functions. Based on the new designed state observer, a sampled-data control scheme for the pure-feedback nonlinear systems is proposed. The designed sampled-data controller ensures the boundedness of the nonlinear systems. Finally, two numerical examples are used to demonstrate that the proposed method is efficient.

Adaptive Neural Control of Pure-Feedback Nonlinear Systems With Event-Triggered Communications.

This paper is concerned with the adaptive event-triggered control problem for a class of pure-feedback nonlinear systems. Unlike the existing results where the control execution is periodic, the new proposed scheme updates the controller and the neural network weights only when desired control specifications cannot be guaranteed. Clearly, this can largely reduce the amount of transmission data. Besides, since the event-trigger error is discontinuous because of the event-triggering mechanism, the stability analysis in the classical sense may not be guaranteed. To solve this problem, we formulate the event-triggered network control systems as a nonlinear impulsive dynamical system, and a novel Lyapunov theorem is used to show the stability properties of the closed-loop systems. Finally, two simulation examples are given to illustrate the effectiveness of the theoretical results.

Fuzzy-approximation adaptive fault-tolerant control for nonlinear pure-feedback systems with unknown control directions and sensor failures

In this paper, the problem of adaptive fuzzy fault-tolerant control (FTC) is investigated for a class of nonlinear pure-feedback systems with unknown control directions and sensor failures. By utilizing the mean value theorem, the considered nonlinear pure-feedback system is transformed into a strict-feedback structure. The unavailability of state variables caused by sensor faults is dealt with by using parameter separation and regrouping technique. Based on cancelation and decoupled backstepping technique, an adaptive fuzzy fault-tolerant control method is developed. It is proved that the proposed adaptive FTC scheme can guarantee the exponential stabilization of the resulting closed-loop system. Furthermore, all the signals of the system remain semi-global uniformly ultimately bound (SGUUB). The simulation results are provided to illustrate the effectiveness of the proposed control scheme.