A Robust Fault Estimation Scheme for a Class of Nonlinear Systems

This paper presents an observer‐based robust fault estimation scheme for a class of nonlinear systems. We consider the system where the fault enters both the state and output equations via unmeasurable nonlinear functions, for which currently no fault estimation scheme exists. The proposed scheme exploits the special structures and information embedded in the fault‐dependent nonlinear functions. We propose a design method to minimize the ?₂ gain from the disturbances to the fault estimation, and provide conditions for the existence of such observers. The effectiveness of this scheme is demonstrated on a nonlinear single‐link flexible joint robot system with disturbances.     

A COMPARATIVE STUDY ON COMPUTATIONAL SCHEMES FOR NONLINEAR MODEL PREDICTIVE CONTROL

This paper briefly reviews development of nonlinear model predictive control (NMPC) schemes for finite horizon prediction and basic computational algorithms that can solve the stable real‐time implementation of NMPC in space state form with state and input constraints. In order to ensure stability within a finite prediction horizon, most NMPC schemes use a terminal region constraint at the end of the prediction horizon — a particular NMPC scheme using a terminal region constraint, namely quasi‐infinite horizon, that guarantees asymptotic closed‐loop stability with input constraints is presented. However, when nonlinear processes have both input and state constraints, difficulty arises from failure to satisfy the state constraints due to constraints on input. Therefore, a new NMPC scheme without a terminal region constraint is developed using soften state constraints. A brief comparative simulation study of two NMPC schemes: quasi‐infinite horizon and soften state constraints is done via simple nonlinear examples to demonstrate the ability of the soften state constraints scheme. Finally, some features of future research from this study are discussed.     

Anti‐windup synthesis for nonlinear dynamic inversion control schemes

A general anti‐windup (AW) compensation scheme is provided for a class of input constrained feedback‐linearizable nonlinear systems. The controller considered is an inner‐loop nonlinear dynamic inversion controller, augmented with an outer‐loop linear controller, of arbitrary structure. For open‐loop globally exponentially stable plants, it is shown that (i) there always exists a globally stabilizing AW compensator corresponding to a nonlinear generalization of the Internal‐Model‐Control (IMC) AW solution; (ii) important operator theoretic parallels exist between the AW design scheme for linear control and the suggested AW design scheme for nonlinear affine plants and (iii) a more attractive AW compensator may be obtained by using a nonlinear state‐feedback term, which plays a role similar to the linear state‐feedback term in linear coprime factor‐based AW compensation. The results are demonstrated on a dual‐tank simulation example. Copyright © 2009 John Wiley & Sons, Ltd.     

Optimal grade transition control for liquid‐propylene polymerization reactor

A nonlinear control system integrating an off‐line optimizer and a nonlinear model‐based controller is developed to perform optimal grade transition operations in a continuous pilot plant reactor. A simple black‐box model is developed and used to determine optimal trajectories of inputs and outputs for a series of three polypropylene grades. The simplified model is also used to develop a nonlinear controller. This controller is similar to generic model control; however, the integral action is omitted and an on‐line updating scheme is incorporated to update pre‐specified model parameters using delayed process measurements. The time optimal inputs, which are calculated by the off‐line optimizer, are introduced to the plant in a feedforward manner. At the same time, the deviations from the optimal output are corrected using the feedback nonlinear controller. The simulations on a complex mechanistic model of the process reveal that the nonlinear control scheme performs well for both set point tracking and disturbance rejection. This paper integrates well‐known methodologies, such as the generic‐model control algorithm, parameter update schemes, and off‐line optimization, together to develop an applicable and robust control technique for continuous polymerization reactors. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Adaptive Observer‐Based Global Sliding Mode Control for Uncertain Discrete‐Time Nonlinear Systems with Time‐Delays and Input Nonlinearity

A new discrete‐time adaptive global sliding mode control (SMC) scheme combined with a state observer is proposed for the robust stabilization of uncertain nonlinear systems with mismatched time delays and input nonlinearity. A state observer is developed to estimate the unmeasured system states. By using Lyapunov stability theorem and linear matrix inequality (LMI), the condition for the existence of quasi‐sliding mode is derived and the stability of the overall closed‐loop system is guaranteed. Finally, simulation results are presented to demonstrate the validity of the proposed scheme.     

Global adaptive finite‐time stabilization for a class of p‐normal nonlinear systems via an event‐triggered strategy

This article addresses the problem of global adaptive finite‐time control for a class of p‐normal nonlinear systems via an event‐triggered strategy. A state feedback controller is first designed for the nominal system by adding a power integrator method. Then, by the skillful design of adaptive dynamic gain mechanism, a novel event‐triggered controller is constructed for uncertain nonlinear system without homogeneous growth condition. It is proved that the global finite‐time stabilization of p‐normal nonlinear systems is guaranteed and the Zeno phenomenon is excluded. Finally, two examples are presented to indicate the effectiveness of the proposed control scheme.     

Adaptive neural control for a class of uncertain nonlinear systems with unknown time delay

A neural network (NN)‐based robust adaptive control design scheme is developed for a class of nonlinear systems represented by input–output models with an unknown nonlinear function and unknown time delay. By approximating on‐line the unknown nonlinear functions with a three‐layer feedforward NN, the proposed approach does not require the unknown parameters to satisfy the linear dependence condition. The control law is delay independent and possible controller singularity problem is avoided. It is proved that with the proposed neural control law, all the signals in the closed‐loop system are semiglobally bounded in the presence of unknown time delay and unknown nonlinearity. A simulation example is presented to demonstrate the method. Copyright © 2008 John Wiley & Sons, Ltd.     

A DESIGN OF MULTILOOP PID CONTROLLERS WITH A NEURAL‐NET BASED DECOUPLER

In process industries, PID control schemes have been widely used due to their simple structures and easiness of comprehending the physical meanings of control parameters. However, the good control performance cannot be obtained by simply using PID controlschemes, since most processes are considered as nonlinear multivariable systems with mutual interactions. In this paper, a design method of multiloop PID controllers neural‐net based decoupler is proposed for nonlinear multivariable systems with mutual interactions. The proposed method consists of a decoupler given by the sum of a static decoupler and a neural‐net based decoupler, and multi‐loop PID controllers. Finally, the effectiveness of the proposed control scheme is evaluated on the simulation examples.     

Nonlinear model predictive control for path following problems

This paper presents a general nonlinear model predictive control scheme for path following problems. Path following problem of nonlinear systems is transformed into a parameter‐dependent regulation problem. Sufficient conditions for recursive feasibility and asymptotic convergence of the given scheme are presented. Furthermore, a polytopic linear differential inclusion‐based method of choosing a suitable terminal penalty and the corresponding terminal constraint are proposed. To illustrate the implementation of the nonlinear model predictive control scheme, the path following problem of a car‐like mobile robot is discussed, and the control performance is confirmed by simulation results. Copyright © 2014 John Wiley & Sons, Ltd.     

DECENTRALIZED STABILIZATION OF H FUZZY CONTROL FOR NONLINEAR MULTIPLE TIME‐DELAY INTERCONNECTED SYSTEMS

A robustness design of fuzzy control is proposed in this paper to overcome the effect of modeling errors between nonlinear multiple time‐delay systems and fuzzy models. In terms of Lyapunov's direct method, a stability criterion is derived to guarantee the UUB (uniformly ultimately bounded) stability of nonlinear multiple time‐delay interconnected systems with disturbances. Based on this criterion and the decentralized control scheme, a set of fuzzy controllers is then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear multiple time‐delay interconnected systems and the Hcontrol performance is achieved in the mean time.     

Active disturbance rejection control for nonlinear fractional‐order systems

This paper investigates active disturbance rejection control involving the fractional‐order tracking differentiator, the fractional‐order PID controller with compensation and the fractional‐order extended state observer for nonlinear fractional‐order systems. Firstly, the fractional‐order optimal‐time control scheme is studied to propose the fractional‐order tracking differentiator by the Hamilton function and fractional‐order optimal conditions. Secondly, the linear fractional‐order extend state observer is offered to acquire the estimated value of the sum of nonlinear functions and disturbances existing in the investigated nonlinear fractional‐order plant. For the disturbance existing in the feedback output, the effect of the disturbance is discussed to choose a reasonable parameter in fractional‐order extended state observer. Thirdly, by this observed value, the nonlinear fractional‐order plant is converted into a linear fractional‐order plant by adding the compensation in the controller. With the aid of real root boundary, complex root boundary, and imaginary boot boundary, the approximate stabilizing boundary with respect to the integral and differential coefficients is determined for the given proportional coefficient, integral order and differential order. By choosing the suitable parameters, the fractional‐order active disturbance rejection control scheme can deal with the unknown nonlinear functions and disturbances. Finally, the illustrative examples are given to verify the effectiveness of fractional‐order active disturbance rejection control scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

Finite‐Time Fault‐Tolerant Control for a Class of Non‐Affine Nonlinear System Using Sliding Mode Disturbance Observer

This study investigates a finite‐time fault‐tolerant control scheme for a class of non‐affine nonlinear system with actuator faults and unknown disturbances. A global approximation method is applied to non‐affine nonlinear system to convert it into an affine‐like expression with accuracy. An adaptive terminal sliding mode disturbance observer is proposed to estimate unknown compound disturbances in finite time, including external disturbances and system uncertainties, which enhances system robustness. Controllers based on finite‐time Lyapunov theory are designed to force tracking errors to zero in finite time. Simulation results demonstrate the effectiveness of proposed method.     

Adaptive finite‐time neural backstepping control for multi‐input and multi‐output state‐constrained nonlinear systems using tangent‐type nonlinear mapping

This article focuses on the problem of adaptive finite‐time neural backstepping control for multi‐input and multi‐output nonlinear systems with time‐varying full‐state constraints and uncertainties. A tan‐type nonlinear mapping function is first proposed to convert the strict‐feedback system into a new pure‐feedback one without constraints. Neural networks are utilized to cope with unknown functions. To improve learning performance, a composite adaptive law is designed using tracking error and approximate error. A finite‐time convergent differentiator is adopted to avoid the problem of “explosion of complexity.” By theoretical analysis, all the signals of system are proved to be bounded, the outputs can track the desired signals in a finite time, and full‐state constraints are not transgressed. Finally, comparative simulations are offered to confirm the validity of the proposed control scheme.     

Actuator fault‐tolerant control (FTC) design with post‐fault transient improvement for application to aircraft control

A robust fault‐tolerant control scheme is proposed for uncertain nonlinear systems with zero dynamics, affected by actuator faults and lock‐in‐place and float failures. The proposed controller utilizes an adaptive second‐order sliding mode strategy integrated with the backstepping procedure, retaining the benefits of both the methodologies. A Lyapunov stability analysis has been conducted, which unfolds the advantages offered by the proposed methodology in the presence of inherent modeling errors and strong eventualities of faults and failures. Two modified adaptive laws have been formulated, to approximate the bounds of uncertainties due to modeling and to estimate the fault‐induced parametric uncertainties. The proposed scheme ensures robustness towards linearly parameterized mismatched uncertainties, in addition to parametric and nonparametric matched perturbations. The proposed controller has been shown to yield an improved post‐fault transient performance without any additional expense in the control energy spent. The proposed scheme is applied to the pitch control problem of a nonlinear longitudinal model of Boeing 747‐100/200 aircraft. Simulation results support theoretical propositions and confirm that the proposed controller produces superior post‐fault transient performance compared with already existing approaches designed for similar applications. Besides, the asymptotic stability of the overall controlled system is also established in the event of such faults and failures. Copyright © 2015 John Wiley & Sons, Ltd.     

A Rigging‐Skinning Scheme to Control Fluid Simulation

Inspired by skeletal animation, a novel rigging‐skinning flow control scheme is proposed to animate fluids intuitively and efficiently. The new animation pipeline creates fluid animation via two steps: fluid rigging and fluid skinning. The fluid rig is defined by a point cloud with rigid‐body movement and incompressible deformation, whose time series can be intuitively specified by a rigid body motion and a constrained free‐form deformation, respectively. The fluid skin generates plausible fluid flows by virtually fluidizing the point‐cloud fluid rig with adjustable zero‐ and first‐order flow features and at fixed computational cost. Fluid rigging allows the animator to conveniently specify the desired low‐frequency flow motion through intuitive manipulations of a point cloud, while fluid skinning truthfully and efficiently converts the motion specified on the fluid rig into plausible flows of the animation fluid, with adjustable fine‐scale effects. Besides being intuitive, the rigging‐skinning scheme for fluid animation is robust and highly efficient, avoiding completely iterative trials or time‐consuming nonlinear optimization. It is also versatile, supporting both particle‐ and grid‐ based fluid solvers. A series of examples including liquid, gas and mixed scenes are presented to demonstrate the performance of the new animation pipeline.     

Robust discrete‐time nonlinear sliding mode state estimation of uncertain nonlinear systems

In this paper, we propose a discrete‐time nonlinear sliding mode observer for state and unknown input estimations of a class of single‐input/single‐output nonlinear uncertain systems. The uncertainties are characterized by a state‐dependent vector and a scalar disturbance/unknown input. The discrete‐time model is derived through Taylor series expansion together with nonlinear state transformation. A design methodology that combines the discrete‐time sliding mode (DSM) and a nonlinear observer design is adopted, and a strategy is developed to guarantee the convergence of the estimation error to a bound within the specified boundary layer. A relation between sliding mode gain and boundary layer is established for the existence of DSM, and the estimation is made robust to external disturbances and uncertainties. The unknown input or disturbance can also be estimated through the sliding mode. The conditions for the asymptotical stability of the estimation error are analysed. Application to a bioreactor is given and the simulation results demonstrate the effectiveness of the proposed scheme. Copyright © 2006 John Wiley & Sons, Ltd.     

Robust adaptive control of nonlinearly parametrized multivariable systems with unmatched disturbances

A new robust adaptive control scheme is developed for nonlinearly parametrized multivariable systems in the presence of parameter uncertainties and unmatched disturbances. The developed control scheme employs a new integrated framework of a functional bounding technique for handling nonlinearly parametrized system dynamics, an adaptive parameter estimation algorithm for dealing with parameter uncertainties, a nonlinear feedback controller structure for stabilization of interconnected system states, and a robust adaptive control design for accommodating unmatched disturbances. It is proved that such a new robust adaptive control scheme is capable of ensuring the global boundedness and mean convergence of all closed‐loop system signals. A complete simulation study on an air vehicle system with nonlinear parametrization in the presence of an unmatched wind disturbance is conducted, and its results verify the effectiveness of the proposed robust adaptive control scheme.     

Robust Adaptive Identification of Nonlinear System Using Neural Network

It is well known that disturbance can cause divergence of neural networks in the identification of nonlinear systems. Sufficient conditions using so‐called modified algorithms are available to provide guaranteed convergence for adaptive system. They are dead zone scheme, adaptive law modification, and σ‐modification. These schemes normally require knowledge of the upper bound of the disturbance. In this paper, a robust weighttuning algorithm is used to train the multi‐layered neural network with an adaptive dead zone scheme. The proposed robust adaptive algorithm does not require knowledge of either the upper bound of the disturbance or the bound on the norm of the estimate parameter. A complete convergence proof is provided based on Lyapunov theorem to deal with the nonlinear system. Simulation results are presented to show good perfor‐mance of the algorithm.     

A multivariable MRAC scheme with application to a nonlinear aircraft model

This paper revisits the multivariable model reference adaptive control (MRAC) problem, by studying adaptive state feedback control for output tracking of multi-input multi-output (MIMO) systems. With such a control scheme, the plant-model matching conditions are much less restrictive than those for state tracking, while the controller has a simpler structure than that of an output feedback design. Such a control scheme is useful when the plant-model matching conditions for state tracking cannot be satisfied. A stable adaptive control scheme is developed based on LDS decomposition of the high-frequency gain matrix, which ensures closed-loop stability and asymptotic output tracking. A simulation study of a linearized lateral-directional dynamics model of a realistic nonlinear aircraft system model is conducted to demonstrate the scheme. This linear design based MRAC scheme is subsequently applied to a nonlinear aircraft system, and the results indicate that this linearization-based adaptive scheme can provide acceptable system performance for the nonlinear systems in a neighborhood of an operating point.     

Adaptive Mittag‐Leffler Stabilization of a Class of Fractional Order Uncertain Nonlinear Systems

This paper deals with the stabilization of a class of commensurate fractional order uncertain nonlinear systems. The fractional order system concerned is of the strict‐feedback form with uncertain nonlinearity. An adaptive control scheme combined with fractional order update laws is proposed by extending classical backstepping control to fractional order backstepping scheme. The asymptotic stability of the closed‐loop system is guaranteed under the construction of fractional Lyapunov functions in the sense of generalized Mittag‐Leffler stability. The fractional order nonlinear system investigated can be stabilized asymptotically globally in presence of arbitrary uncertainty. Finally illustrative examples and numerical simulations are performed to verify the effectiveness of the proposed control scheme.