Modular‐based adaptive control of uncertain nonlinear time‐varying systems with piecewise jumping parameters

Nonlinear time‐varying systems exist widely in practice. Therefore, it is of great theoretical importance and practical value to investigate the problem of controlling such systems. However, the results available in developing adaptive control to address such a problem are still limited. Especially a majority of them are restricted to be slowly time‐varying linear systems. This paper presents a modular‐based adaptive control scheme for parametric strict feedback nonlinear time‐varying systems. The parameters considered include both continuous and piecewise time‐varying parameters, and they are not necessarily restricted to be slowly time‐varying or infrequent jumping. The technique of adaptive backstepping with nonlinear damping is employed in the control design module, while the parameter projection algorithm is performed on the parameter estimation module. It is proved that the uniform boundedness of all closed‐loop system signals can be guaranteed with the proposed control scheme. The performance of the tracking error in the mean square sense with respect to the parameter variation rate is also established. Furthermore, perfect asymptotically tracking can be achieved when the varying rates of unknown parameters are in the space. Copyright © 2013 John Wiley & Sons, Ltd.     

Model‐free fault‐tolerant control approach for uncertain state‐constrained linear systems with actuator faults

This paper investigates the robust adaptive fault‐tolerant control problem for state‐constrained continuous‐time linear systems with parameter uncertainties, external disturbances, and actuator faults including stuck, outage, and loss of effectiveness. It is assumed that the knowledge of the system matrices, as well as the upper bounds of the disturbances and faults, is unknown. By incorporating a barrier‐function like term into the Lyapunov function design, a novel model‐free fault‐tolerant control scheme is proposed in a parameter‐dependent form, and the state constraint requirements are guaranteed. The time‐varying parameters are adjusted online based on an adaptive method to prevent the states from violating the constraints and compensate automatically the uncertainties, disturbances, and actuator faults. The time‐invariant parameters solved by using data‐based policy iteration algorithm are introduced for helping to stabilize the system. Furthermore, it is shown that the states converge asymptotically to zero without transgression of the constraints and all signals in the resulting closed‐loop system are uniformly bounded. Finally, two simulation examples are provided to show the effectiveness of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.     

An adaptive observer for joint estimation of states and parameters in both state and output equations

An adaptive observer is a recursive algorithm for joint state–parameter estimation of parameterized state‐space systems. Previous works on globally convergent adaptive observers consider unknown parameters either in state equations or in output equations, but not in both of them. In this paper, a new adaptive observer is designed for linear time‐varying systems with unknown parameters in both state and output equations. Its global convergence for simultaneous estimation of states and parameters is formally established under appropriate assumptions. A numerical example is presented to illustrate the performance of this adaptive observer. Copyright © 2011 John Wiley & Sons, Ltd.     

A model reference tracking based on an active fault tolerant control for LPV systems

This work deals with the problem of a model reference tracking based on the design of an active fault tolerant control for linear parameter‐varying systems affected by actuator faults and unknown inputs. Linear parameter‐varying systems are described by a polytopic representation with measurable gain scheduling functions. The main contribution is to design an active fault tolerant controller whose control law is described by an adaptive proportional integral structure. This one requires 3 types of online information, which are reference outputs, measured real outputs, and the fault estimation provided by a model reference, sensors, and an adaptive polytopic observer, respectively. These types of information are used to reconfigure the designed controller, which is able to compensate the fault effects and to make the closed‐loop system able to track reference outputs in spite of the presence of actuator faults and disturbances. The controller and the observer gains are obtained by solving a set of linear matrices inequalities. Performances of the proposed method are compared to another previous method to underline the relevant results.     

Observer design for nonlinear parameter‐varying systems: Application to diesel engines

This paper deals with the problem of designing a nonlinear observer for diesel engines. The goal is to estimate the masses entering the cylinders and the inlet and exhaust pressures. To achieve this, the considered model subject to disturbances contains linear parameter‐varying part and nonlinear part having a large Lipschitz constant. The H _∞ criterion is used to reject the disturbances. Besides, the modified mean value theorem is applied to express the nonlinear error dynamics as a convex combination of known matrices with time‐varying coefficients. The sufficient conditions are derived and given in terms of linear matrix inequality. The advantage of the proposed method is that it can be applied to a wider class of nonlinear systems, particulary our case: diesel engines. The proposed approach is tested and evaluated using an advanced diesel engine professional simulator AMEsim (LMS International, Leuven, Belgium). Copyright © 2014 John Wiley & Sons, Ltd.     

Model reference adaptive control system design for linear time‐varying systems with unstructured and bounded varying functions

In recent years, many methods of model reference adaptive control system (MRACS) for a linear time‐varying (LTV) plant have been proposed. These methods assumed that the structure of plant parameters is known in advance. However, it is difficult to get a priori information of plant parameters. In this paper, an MRACS design for an LTV system based on high‐order estimator (HOE) is proposed. By applying dynamic certainty equivalence (DyCE) to LTV plants, a new MRAC law of LTV system is derived without knowing the structure of the plant parameters. The MRACS law is generated by using high‐order derivatives of an estimated parameter, so that robust HOE with a normalization signal and σ modification for the system introduced. Our proposed method can attain better performance than conventional methods, such as estimation with variable forgetting factor (VF) and the gradient projection method (GPM). The robust HOE establishes the boundedness of all of the estimated parameters under the condition that the estimated parameter and the first derivative of the parameter are bounded. It is shown that all signals in the adaptive loop are bounded and the output error converges to a closed set. The proposed method is compared to the familiar schemes, the gradient projection method and the estimation based on forgetting factor through numerical simulations, and the effectiveness of our proposed method is shown. © 2000 Scripta Technica, Electr Eng Jpn, 130(4): 87–98, 2000     

Equivalence between adaptive feedforward cancellation and disturbance rejection using the internal model principle

The paper shows that a large class of adaptive algorithms for disturbance cancellation yields control systems that are equivalent to compensators implementing the internal model principle (IMP). The fact has been known to be true for periodic disturbances with fixed frequency. However, the paper shows that the result can be extended to disturbances of time‐varying frequency (i.e. frequency‐modulated signals), regardless of the rate of variation of the frequency. In particular, several adaptive controllers are shown to be equivalent to linear time‐varying compensators implementing the IMP. Further, a pseudo‐gradient algorithm produces the same responses as a polytopic linear parameter‐varying compensator. Copyright © 2009 John Wiley & Sons, Ltd.     

The internal model principle for periodic disturbances with rapidly time‐varying frequencies

In this paper, we study the limitations of scheduling an internal model to reject disturbances with a time‐varying frequency. Hence, any adaptive method that uses scheduling and frequency estimation is also limited in the same manner. The limitations of scheduling are to investigate by posing and solving the problem of rejecting periodic disturbances from a multichannel system when the frequencies of the periodic disturbances are changing rapidly in time by designing an scheduled controller that satisfies the internal model principle. The periodic disturbances are modeled by a sum of sinusoids and the frequencies of the disturbances are used for scheduling the controller. It is shown that a controller that regulates input additive disturbances may not regulate the same disturbances added to the output of the system. This is in contrast to the classical case where the frequency of the disturbances is constant. Copyright © 2011 John Wiley & Sons, Ltd.     

Construction of a robust adaptive regulator for time-varying discrete time systems

A robust adaptive regulator is constructed for single-input/single-output discrete time systems modelled by a linear time-varying difference equation that includes an error term to incorporate model errors and/or disturbances. It is assumed that the parameters of the nominal model belong to a known bounded convex set and that the ‘frozen time’ nominal model is stabilizable for all possible parameter values. The estimation of the parameters of the nominal model is carried out using a standard gradient-type algorithm with a projection operation. An adaptive regulator is then constructed from the solution to a finite time Riccati equation. It is shown that the resulting closed-loop system is globally stable if the mean of the parameter time variations is sufficiently small and if the model error is sufficiently small, but where the disturbances applied to the plant may be arbitrarily large.     

A new model reference adaptive control for linear time‐varying systems

Recent results on the adaptive control of linear time‐varying systems have considered mostly the case in which the range or rate of parameter variations is small. In this paper, a new state feed‐back model reference adaptive control is developed for systems with bounded arbitrary parameter variations. The important feature of the proposed adaptive control is an uncertainty estimation algorithm, which guarantees almost zero tracking error. Note that the conventional parameter estimation algorithm in the adaptive control guarantees only bounded tracking error. Copyright © 2000 John Wiley & Sons, Ltd.     

Adaptive sliding mode–based diagnosis of actuator faults for LPV systems

In this paper, an adaptive sliding mode observer is developed for actuator fault diagnosis of linear parameter–varying systems. The main advantage of the proposed approach is its ability to cope with time‐varying distribution matrix in linear parameter–varying systems. Furthermore, the proposed adaptive observer is characterized by its output robustness against parameter uncertainties and disturbances without any a priori knowledge about their bounds. The efficiency of the proposed fault diagnosis approach is validated using simulation studies.     

An indirect adaptive pole‐placement control for MIMO discrete‐time stochastic systems

In this paper, an indirect adaptive pole‐placement control scheme for multi‐input multi‐output (MIMO) discrete‐time stochastic systems is developed. This control scheme combines a recursive least squares (RLS) estimation algorithm with pole‐placement control design to produce a control law with self‐tuning capability. A parametric model with a priori prediction outputs is adopted for modelling the controlled system. Then, a RLS estimation algorithm which applies the a posteriori prediction errors is employed to identify the parameters of the model. It is shown that the implementation of the estimation algorithm including a time‐varying inverse logarithm step size mechanism has an almost sure convergence. Further, an equivalent stochastic closed‐loop system is used here for constructing near supermartingales, allowing that the proposed control scheme facilitates the establishment of the adaptive pole‐placement control and prevents the closed‐loop control system from occurring unstable pole‐zero cancellation. An analysis is provided that this control scheme guarantees parameter estimation convergence and system stability in the mean squares sense almost surely. Simulation studies are also presented to validate the theoretical findings. Copyright © 2005 John Wiley & Sons, Ltd.     

Composite adaptive disturbance observer‐based control for a class of nonlinear systems with multisource disturbance

Antidisturbance control and estimation problem are introduced for a class of nonlinear system subject to multisource disturbances. The uncertain multisource disturbances consist of not only a single harmonic or constant disturbance but also another unexpected nonlinear signal described as a nonlinear function. The composite adaptive disturbance observers are constructed separately from the controller design to estimate the disturbance with partial known information. By integrating disturbance observer‐based controller with robust adaptive control, a novel type of composite adaptive disturbance observer‐based control scheme is presented for a class of nonlinear system with multisource disturbances. Simulations for a flight control system are given to demonstrate the effectiveness of the results compared with the previous schemes. Copyright © 2012 John Wiley & Sons, Ltd.     

Adaptive attenuation of unknown and time‐varying narrow band and broadband disturbances

In many classes of applications like active vibration control and active noise control, the disturbances can be characterized by their frequency content and their location in a specific region in the frequency domain. The disturbances can be of narrow band type (simple or multiple) or of broad band type. A model can be associated to these disturbances. The knowledge of this model allows to design an appropriate control system in order to attenuate (or to reject) their effect upon the system to be controlled. The attenuation of disturbances by feedback is limited by the Bode Integral and the ‘water bed’ effect upon the output sensitivity function. In such situations, the feedback approach has to be complemented by a ‘feedforward disturbance compensation’ requiring an additional transducer for obtaining information upon the disturbance. Unfortunately, in most of the situations, the disturbances are unknown and time‐varying and therefore an adaptive approach should be considered. The generic term for adaptive attenuation of unknown and time‐varying disturbances is ‘adaptive regulation’ (known plant model, unknown, and time‐varying disturbance model). The paper will review a number of recent developments for adaptive feedback compensation of multiple unknown and time‐varying narrow band disturbances and for adaptive feedforward compensation of broad band disturbances in the presence of the inherent internal positive feedback caused by the coupling between the compensator system and the measurement of the image of the disturbance. Some experimental results obtained on a relevant active vibration control system will illustrate the performance of the various algorithms presented. Some open research problems will be mentioned in the conclusion. Copyright © 2015 John Wiley & Sons, Ltd.     

Composite learning fuzzy synchronization for incommensurate fractional‐order chaotic systems with time‐varying delays

This paper presents a composite learning fuzzy control to synchronize two different uncertain incommensurate fractional‐order time‐varying delayed chaotic systems with unknown external disturbances and mismatched parametric uncertainties via the Takagi‐Sugeno fuzzy method. An adaptive controller together with fractional‐order composite learning laws is designed based on both a parallel distributed compensation technology and a fractional Lyapunov criterion. The boundedness of all variables in the closed‐loop system and the Mittag‐Leffler stability of tracking error can be guaranteed. T‐S fuzzy systems are provided to tackle unknown nonlinear functions. The distinctive features of the proposed approach consist in the following: (1) a supervisory control law is designed to compensate the lumped disturbances; (2) both the prediction error and the tracking error are used to estimate the unknown fuzzy system parameters; (3) parameter convergence can be ensured by an interval excitation condition. Finally, the feasibility of the proposed control strategy is demonstrated throughout an illustrative example.     

Stability analysis and adaptive control for nonlinear discrete‐time systems with time delays and dead zone via state‐feedback technique

This research addresses the stability analysis and adaptive state‐feedback control for a class of nonlinear discrete‐time systems with multiple interval time‐varying delays and symmetry dead zone. The multiple interval time‐varying delays and symmetry dead zone are considered in the nonlinear discrete‐time system. The multiple interval time‐varying delays are bounded by the nonlinear function with unknown coefficients, and the symmetry dead zone is considered without the knowledge of the dead zone parameters. The adaptive state‐feedback controller is designed for the nonlinear discrete‐time systems with multiple interval time‐varying delays and dead zone. The discrete Lyapunov‐Krasovskii functional is introduced, such that the solutions of the closed‐loop error system converge to an adjustable bounded region and the state errors can be rendered arbitrarily small by adjusting the adaptive parameters. The designed adaptive state‐feedback controller does not require the knowledge of maximum and minimum values for the characteristic slopes of the dead zone. Finally, three simulation examples are given to show the effectiveness of the proposed methods.     

Bounded‐error state and parameter estimation of tip‐tilt disturbances in adaptive optics systems

The main objective of this paper was to estimate tip‐tilt disturbances in adaptive optics systems. In a bounded‐error context, set inversion methods based on interval analysis are used to guarantee both state and parameter estimation of tip‐tilt disturbances. Consequently, two methods are performed. The first method is based on contraction–bisection, and the second one is based only on contraction. Both methods are thus compared, and results are discussed according to computational time and pessimism introduced on each estimated parameter. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust adaptive control of nonlinearly parameterized systems with unmodeled dynamics

Many physical systems such as biochemical processes and machines with friction are of nonlinearly parameterized systems with uncertainties. How to control such systems effectively is one of the most challenging problems. This paper presents a robust adaptive controller for a significant class of nonlinearly parameterized systems. The controller can be used in cases where there exist parameter and nonlinear uncertainties, unmodeled dynamics and unknown bounded disturbances. The design of the controller is based on the control Lyapunov function method. A dynamic signal is introduced and adaptive nonlinear damping terms are used to restrain the effects of unmodeled dynamics, nonlinear uncertainties and unknown bounded disturbances. The backstepping procedure is employed to overcome the complexity in the design. With the proposed method, the estimation of the unknown parameters of the system is not required and there is only one adaptive parameter no matter how high the order of the system is and how many unknown parameters there are. It is proved theoretically that the proposed robust adaptive control scheme guarantees the stability of nonlinearly parameterized system. Furthermore, all the states approach the equilibrium in arbitrary precision by choosing some design constants appropriately. Simulation results illustrate the effectiveness of the proposed robust adaptive controller. __________ Translated from Journal of Sichuan University (Engineering Science Edition), 2005, 37(5): 148–153 (in Chinese)     

On‐line parameter estimation for a class of time‐varying continuous systems with bounded disturbances

In this paper, we proposed an on‐line parameter estimation algorithm for a class of time‐varying continuous systems with bounded disturbance. In this method, a novel polynomial approximator with a bounded regressor vector is constructed and utilized to approximate the time‐varying parameters. The direct least‐squares algorithm is employed to acquire the on‐line estimates, so that several useful properties of the direct estimation, such as fast convergence and robustness to the bounded disturbance, are reflected in our method. We have proved that the estimation error of this method is bounded. Furthermore, the bound on the Euclidean norm of the estimation error is derived. The simulation results demonstrate that this method can provide accurate estimates of time‐varying parameters even under the influence of bounded disturbance. Copyright © 2010 John Wiley & Sons, Ltd.     

Multiple characteristic model‐based golden‐section adaptive control: Stability and optimization

The characteristic model‐based golden‐section adaptive control (CM‐GSAC) law has been developed for over 20 years in China with a broad range of applications in various fields. However, quite a few theoretical problems remain open despite its satisfying performance in practice. This paper revisits the stability of the CM‐GSAC from its very beginning and explores the underlying implications of the so‐called golden‐section parameter l₂≈0.618. The closed‐loop system, which consists of the CM and the GSAC, is a discrete time‐varying system, and its stability is discussed from three perspectives. First, attentions have been paid to select the optimal controller coefficients such that the closed‐loop system exhibits the best transient performance in the worst case. Second, efforts are made to improve the robustness in the presence of parameter estimation errors, which provide another choice when designing the adaptive controller. Finally, by measuring the slowly time‐varying nature in an explicit inequality form, a bridge is built between the instantaneous stability and the time‐varying stability. In order to relax the constraints on the parameter bounds of the CM, the GSAC is further extended to multiple CMs, which shows more satisfying tracking performance than that of the traditional multiple model adaptive control method. Copyright © 2014 John Wiley & Sons, Ltd.