Adaptive output feedback control for a class of nonlinear systems with unknown virtual control coefficients signs

This paper presents a global output-feedback control scheme for a class of nonlinear systems that are transformed via a parameter-independent change of co-ordinates into a form in which there exist three kinds of unknown parameters: one is the unknown virtual control coefficients, one is the unknown parameters that multiply output nonlinearities and the other kind is the unknown parameters that multiply affine functions of the derivative of the measured output with coefficients that are smooth nonlinear functions of the measured output. We use two parameter-dependent changes of co-ordinates to transform the system considered into parametric output-feedback form. One transformation is used to eliminate the difficulty in dealing with unknown virtual control coefficients and the other transformation is used to remove the nonlinearities which are affine functions of the derivative of the measured output with coefficients that are smooth nonlinear functions of the measured output. Then the scheme presented by Ye (IEEE Trans. Automat. Control 2001; 46 :112–115) can be applied to the new system. Global results can be obtained for the overall closed-loop systems without any constraints on the nonlinear terms. Copyright © 2006 John Wiley & Sons, Ltd.     

Adaptive finite-time control for output regulation of nonlinear systems with completely unknown control directions

This article studies the finite-time output regulation problem for nonlinear strict-feedback systems with completely unknown control directions and unknown functions. First, according to the necessary conditions for the solvability of the output regulation problem, the output regulation problem of nonlinear strict-feedback systems and the external system is transformed into a stabilization problem of nonlinear systems. Second, an internal model with external signals is designed. Third, based on finite time, fuzzy control, output feedback control, and Nussbaum gain functions, the control law is designed so that all signals of the closed-loop system are the semi-global practically finite-time stable (SGPFS), and the tracking error converges to a small neighborhood of the origin in a finite-time. Finally, the proposed algorithm is applied to the finite-time tracking problem of Chua's oscillator system.     

Adaptive output feedback regulation for a class of uncertain feedforward nonlinear systems

In this paper, we solve the problem of global output feedback regulation for uncertain feedforward nonlinear systems. The nonlinear functions, in the class of systems under consideration, are assumed to be dominated by an input‐output function multiplied by an unknown parameter and a linear unmeasured states. Contrarily to the previous works, the interval of the output's power has been expanded from to . A numerical example is provided to illustrate the effectiveness of the proposed design scheme. Copyright © 2016 John Wiley & Sons, Ltd.     

An adaptive dynamic surface control of output constrained stochastic nonlinear systems with unknown control directions

This article is concerned about an adaptive dynamic surface control (DSC) of output constrained stochastic nonlinear systems with unknown control directions and unmodeled dynamics. Nonlinear mapping-based backstepping control design is presented for stochastic nonlinear systems with output constraint. The explosion of complexity exists in tradition backstepping method is avoided by using the DSC technique. The radial basis function neural networks are employed to deal with unknown nonlinear functions. Nussbaum gain technique is employed to handle the unknown control directions. And a dynamic signal is employed to dominate the unmodeled dynamics. The adaptive controller is designed can ensure that the tracking error converges on a small region of the origin. And all signals of the closed-loop systems are semiglobal uniformly ultimately bounded. Finally, the results of the simulation cases are provided to show the effectivity of the designed controller scheme.     

Adaptive variable universe of discourse fuzzy control for a class of nonlinear systems with unknown dead zones

In this paper, based on an adaptive nonbackstepping design algorithm, we proposed a novel variable universe of discourse fuzzy control (VUDFC) approach for a class of single‐input–single‐output strict‐feedback nonlinear systems with unknown dead‐zone inputs. Firstly, we convert the form of system into a normal form on the basis of some new state variables and coordinate transformation; at the same time, state‐feedback control is changed to output‐feedback control. Secondly, we design observers to estimate the new unmeasurable states. Then, different from considering the traditional backstepping‐based fuzzy control scheme, we introduce a direct VUDFC scheme, which is mainly based on changing of contraction‐expansion factors to modify the universe of discourse online, and fuzzy rules can automatically reproduce to develop the control performance; thus, the size of initial rule base is greatly reduced. This new algorithm can alleviate tracking error, improve the accuracy of the system, and strengthen robustness. Lastly, according to Lyapunov theorem analysis, we prove that all the signals in the closed‐loop system can be guaranteed to be stable, and the output can track the reference signal very well. Simulation results illustrated the effectiveness of the proposed VUDFC approach.     

Dual high‐gain‐based adaptive output‐feedback control for a class of nonlinear systems

We propose an adaptive output‐feedback controller for a general class of nonlinear triangular (strict‐feedback‐like) systems. The design is based on our recent results on a new high‐gain control design approach utilizing a dual high‐gain observer and controller architecture with a dynamic scaling. The technique provides strong robustness properties and allows the system class to contain unknown functions dependent on all states and involving unknown parameters (with no magnitude bounds required). Unlike our earlier result on this problem where a time‐varying design of the high‐gain scaling parameter was utilized, the technique proposed here achieves an autonomous dynamic controller by introducing a novel design of the observer, the scaling parameter, and the adaptation parameter. This provides a time‐invariant dynamic output‐feedback globally asymptotically stabilizing solution for the benchmark open problem proposed in our earlier work with no magnitude bounds or sign information on the unknown parameter being necessary. Copyright © 2007 John Wiley & Sons, Ltd.     

Direct adaptive neural tracking control for a class of stochastic pure‐feedback nonlinear systems with unknown dead‐zone

This paper considers the problem of adaptive neural tracking control for a class of nonlinear stochastic pure‐feedback systems with unknown dead zone. Based on the radial basis function neural networks' online approximation capability, a novel adaptive neural controller is presented via backstepping technique. It is shown that the proposed controller guarantees that all the signals of the closed‐loop system are semi‐globally, uniformly bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the suggested control scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

Nonlinear control for uncertain nonlinear systems with unknown control directions using less or no parameter estimates

For the parametric strict‐feedback nonlinear systems with unknown virtual control coefficients and unknown control directions, the control schemes presented in the existing literature have the disadvantage of overparametrization. In this paper, a novel systematic design procedure is developed to solve the overparametrization problem. Two nonlinear controllers are designed by combining the backstepping technique and the Nussbaum gain approach. A main advantage of the proposed controllers is that they contain less or no parameter estimates that need to be updated online. In the first scheme, the number of the estimated parameters is equal to the dimension of the controlled system. In the second scheme, no parameter estimates are required. In both of the control schemes, the boundedness of all the closed‐loop signal is guaranteed, and the asymptotic convergence of the system states is achieved. An example is provided to demonstrate the effectiveness of the proposed design approaches. Copyright © 2014 John Wiley & Sons, Ltd.     

一类不确定非线性系统的动态面输出调节方法

针对一类由线性中性稳定的外系统驱动的具有未建模动态和外界扰动的非线性不确定系统的输出调节问题,结合动态面控制法和内模原理提出一种具有动态面控制的设计方法.根据非线性输出调节问题可解的必要条件,运用状态变换和标准内模将输出调节问题转化为镇定问题.运用动态面控制法将一阶滤波器引入反步设计中,避免了反步设计中所存在的"膨胀项...     

一类不确定非线性系统的有限时间输出调节方法

针对一类外系统是中性稳定的不确定非线性系统的输出调节问题,提出一种有限时间收敛的输出调节器的设计方法。该方法结合内模原理和有限时间稳定性理论,先根据输出调节问题可解的必要条件,将原受控系统和外系统的输出调节问题转化为增广系统的镇定问题;再利用动态面技术设计一种鲁棒有限时间稳定的镇定器,从而保证闭环系统的信号是全局最终有界的,并且使调节误差在有限的时间内收敛至任意给定的范围内。仿真结果说明了所设计调节器的有效性和可行性。     

Decentralized prescribed performance adaptive tracking control for Markovian jump uncertain nonlinear systems with input saturation

A decentralized prescribed performance adaptive tracking control problem is investigated for Markovian jump uncertain nonlinear interconnected large‐scale systems. The considered interconnected large‐scale systems contain unknown nonlinear uncertainties, unknown control gains, actuator saturation, and Markovian jump signals, and the Markovian jump subsystems are in the form of triangular structure. First, by defining a novel state transformation with the performance function, the prescribed performance control problem is transformed to stabilization problem. Then, introducing an intermediate control signal into the control design, employing neural network to approximate the unknown composite nonlinear function, and based on the framework of the backstepping control design and adaptive estimation method, a corresponding decentralized prescribed performance adaptive tracking controller is designed. It is proved that all the signals in the closed‐loop system are bounded, and the prescribed tracking performances are guaranteed. A numerical example is provided to illustrate the effectiveness of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.     

Robust adaptive output‐feedback control for a class of nonlinear systems with time‐varying actuator faults

A robust adaptive output‐feedback control scheme is proposed for a class of nonlinear systems with unknown time‐varying actuator faults. Additional unmodelled terms in the actuator fault model are considered. A new linearly parameterized model is proposed. The boundedness of all the closed‐loop signals is established. The desired control performance of the closed‐loop system is guaranteed by appropriately choosing the design parameters. The properties of the proposed control algorithm are demonstrated by two simulation examples. Copyright © 2010 John Wiley & Sons, Ltd.     

Adaptive fault-tolerant tracking control for nonlinear systems with unknown control coefficient and input saturation

In this article, the tracking control problem is investigated for a class of nonlinear systems in the presence of unknown disturbance, input saturation, actuator fault, and unknown control coefficient. A novel disturbance observer-based adaptive fault-tolerant tracking control strategy is proposed with regard to nonlinear systems. Based on the Gaussian error function, the auxiliary dynamic system is designed to offset effects caused by the input saturation. Moreover, the Nussbaum-type function is employed to avert control singularity and deal with the unknown control coefficient. A theoretical analysis indicates that the boundedness of all signals in the closed-loop system can be guaranteed. Finally, two examples with one concerning the dynamic point-the-bit rotary steerable drilling tool system are given to confirm the validity of the method.     

Filtered adaptive constrained sampled-data control for uncertain multivariable nonlinear systems

A filtered adaptive constrained sampled-data controller for uncertain multivariable nonlinear systems in the presence of various constraints is synthesized in this paper. A piecewise constant adaptive law drives that estimation error dynamics to zero at each sampling time instant yields adaptive parameters. The filtered control scheme consists of two components. Based on an estimation/cancellation strategy, a disturbance rejection control law is designed to compensate the nonlinear uncertainties within the bandwidth of low-pass filters, whereas a constraint violation avoidance control law is designed to solve an online constrained optimization problem. Although a reduced sampling time helps to minimize the estimation error caused by the neglect of unknowns, the resulting aggressive signals put more restrictions on the control law. Greater sacrifice of tracking performance is required to satisfy the constraints. The constraints violation avoidance control law is in favor of a larger sampling time. Sufficient conditions are given to guarantee the stability of the closed-loop system with the sampled-data controller, where the input/output signals are held constant over the sampling period. Numerical examples are provided to validate the theoretical results, comparisons between the constrained sampled-data controller and unconstrained adaptive controller with the implementation of different sampling times are carried out.     

一类不确定非线性系统的鲁棒镇定

针对一类含有不确定性的非线性系统,首先通过精确线性化方法,将其变为一种规范型形式,在一定的不确定性的假设条件下,构造了鲁棒控制器,仿真算例说明了方法的有效性。     

Adaptive neural dynamic surface control of MIMO stochastic nonlinear systems with unknown control directions

In this paper, an adaptive neural output‐feedback control approach is considered for a class of uncertain multi‐input and multi‐output (MIMO) stochastic nonlinear systems with unknown control directions. Neural networks (NNs) are applied to approximate unknown nonlinearities, and K‐filter observer is designed to estimate unavailable system's states. Due to utilization of Nussbaum gain function technique in the proposed approach, the singularity problem and requirement to prior knowledge about signs of high‐frequency gains are removed, simultaneously. Razumikhin functional method is employed to deal with unknown state time‐varying delays, so that the offered control approach is free of common assumptions on derivative of time‐varying delays. Also, an adaptive neural dynamic surface control is developed; hence, explosion of complexity in conventional backstepping method is eliminated, effectively. The boundedness of all the resulting closed‐loop signals is guaranteed in probability; meanwhile, convergence of the tracking errors to adjustable compact set in the sense of mean quartic value is also proved. Finally, simulation results are shown to verify and clarify efficiency of the offered approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Finite-time adaptive fuzzy control for a class of output constrained nonlinear systems with dead-zone

The article investigates the finite-time adaptive fuzzy control for a class of nonlinear systems with output constraint and input dead-zone. First, by skillfully combining the barrier Lyapunov function, backstepping design method, and finite-time control theory, a novel adaptive state-feedback tracking controller is constructed, and the output constraint of the nonlinear system is not violated. Second, the fuzzy logic system is used to approximate unknown function in the nonlinear system. Third, the finite-time command filter is introduced to avoid the problem of “complexity explosion” caused by repeated differentiations of the virtual control signal in conventional backstepping control schemes. Meanwhile, a new saturation function is added in the compensating signal for filter error to improve control accuracy. Finally, based on Lyapunov stability analysis, all the signals of the closed-loop are proved to be semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood region of the origin in a finite time. A simulation example is presented to demonstrate the effectiveness for the proposed control scheme.     

Adaptive control of feedforward nonlinear systems with uncertain time-varying parameters and an unknown time-varying delay in the input

In this paper, we consider a global regulation problem for a class of feedforward nonlinear systems. The key features of our considered system are identified by the presence of uncertain time-varying parameters associated with main diagonal states and input and an unknown time-varying delay in the input. Moreover, the growth rates of nonlinearity and the input-delay are only known to be finite. To solve our considered problem, we give a process to obtain a compact set that contains the allowed time-varying parameters. Then, we propose an adaptive controller which handles both unknown growth rate of nonlinearity and input-delay for system regulation. We carry out the rigorous system analysis and show the effectiveness of our proposed control scheme via an application example.     

The robust adaptive control of chaotic systems with unknown parameters and external disturbance via a scalar input

This paper investigates the control of chaotic systems in the presence of unknown parameters, model uncertainties, and external disturbance. We first discuss the control of a class of chaotic systems and then investigate the control of general chaotic systems. Based on the adaptive control scheme, some novel criteria are proposed via a backstepping‐like procedure. As an example, the control of the Zhang hyperchaotic system is investigated via a single input. Some numerical simulations are given to demonstrate the robustness and efficiency of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Sampled‐data adaptive control of a class of nonlinear systems

The paper considers the discrete‐time implementation of a class of backstepping adaptive controllers for uncertain nonlinear systems in the parametric strict‐feedback form. The stability of the resultant sampled‐data system cannot be guaranteed when the direct discretization of the continuous‐time backstepping controller is applied to the same system via a sampling and hold device. Therefore, the paper has presented a sampled‐data control scheme which involves first modifying the existing continuous‐time controller and then discretizing it using the forward Euler method. It has been shown that the proposed control guarantees in a semi‐global sense the boundedness of all the variables of the overall sampled‐data system under some conditions. Particularly, the state of the nonlinear system to be controlled can converge to any arbitrarily small neighbourhood of the origin. Copyright © 2011 John Wiley & Sons, Ltd.