Robust multiple model adaptive control (RMMAC): a case study

We evaluate the performance of the robust multiple model adaptive control (RMMAC) methodology by considering a mass–spring–dashpot (MSD) system subject to high-frequency disturbances that strongly excite all its lightly damped oscillatory modes. The results demonstrate the superior performance of the RMMAC and its variant RMMAC/XI architecture for a much more difficult adaptive control problem than that designed and analysed in Reference (Int. J. Adaptive Control Signal Processing, in press). Copyright © 2006 John Wiley & Sons, Ltd.     

Stability analysis of a combined direct variable structure adaptive control – the discrete‐time case

This paper presents the proofs of robust stability of a discrete‐time robust model reference controller combined with variable structure in an adaptive framework. All the proofs of robust stability are derived for the discrete‐time case and are similar to those already existing for the conventional non‐combined case. The controller is applied to a SISO LTI plant with unmodeled dynamics of multiplicative and additive types. It is shown that the combined controller can arbitrarily improve the convergence of the error while maintaining the robustness if compared with the non–combined case. Simulation results illustrate the performance of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.     

Multiple‐model adaptive robust dynamic surface control with estimator resetting

A multiple‐model adaptive robust dynamic surface control with estimator resetting is investigated for a class of semi‐strict feedback nonlinear systems in this paper. The transient performance is mainly considered. The multiple models are composed of fixed models, one adaptive model, and one identification model that can be obtained when the persistent exciting condition is satisfied. The transient performance of the final tracking system can be improved significantly by designing proper switching mechanism during the parameter tuning procedure. The semi‐globally uniformly ultimately bounded stability of the closed‐loop system can be easily achieved because of the framework of adaptive robust dynamic surface control. Numerical examples are provided to demonstrate the effectiveness of the proposed multiple‐model controller. Copyright © 2014 John Wiley & Sons, Ltd.     

Iterative adaptive robust control of multivariable CD processes

In this paper we present a novel adaptive robust control approach to the multivariable cross‐directional (CD) process of continuous web manufacturing. The common assumption of spatial frequency decomposition (SFD) is used to allow the process analysis in terms of a family of single‐input single‐output (SISO) transfer functions across the spatial frequencies. We then apply discretized Windsurfing adaptive robust control to each individual separated spatial frequency, starting with a stable initial model and a robust stabilizing controller at each spatial frequency. This approach allows the 2D bandwidth of the closed‐loop system to be increased progressively at each spatial frequency through an iterative relevant system identification and control design procedure. The method deals with both model uncertainty and measurement noise issues. Simulation results are given to illustrate the performance of the applied method. Copyright © 2009 John Wiley & Sons, Ltd.     

Adaptive robust boundary control of shaft vibrations under perturbations with unknown upper bounds

This paper presents robust and adaptive boundary control designs to stabilize the two‐dimensional vibration of hybrid shaft model. The hybrid shaft is mathematically represented by a set of partial differential equations, governing the shaft vibrations, coupled to ordinary differential equations, describing rigid body spinning and dynamic boundary conditions. The control objective is to stabilize the transverse vibrations of the perturbed shaft while regulating the spinning rate. To achieve this, the paper first establishes robust boundary control laws that fulfil the control objective in the presence of modeling uncertainties and external disturbances operating over the shaft domain and boundary. Lyapunov‐based analyses show that the proposed robust control exponentially stabilizes the shaft with vanishing distributive perturbations, while assuring ultimately bounded vibrations in the case of nonvanishing perturbations. Then, adaptive control philosophy is utilized to achieve redesigned robust controllers that only use online adaptation of control gains without acquiring the knowledge of bounds on perturbations, as well as dynamic parameters. An advantage of this design is avoiding an overconservative robust control law, which may induce poor stability and chattering in tackling system perturbations with unknown upper bounds. Simulations through finite element method illustrate the results. Copyright © 2014 John Wiley & Sons, Ltd.     

Robust adaptive MPC for constrained uncertain nonlinear systems

In this paper, we consider the problem of adaptive model predictive control subject to exogenous disturbances. Using a novel set‐based adaptive estimation, the problem of robust adaptive MPC is proposed and solved for a class of linearly parameterized uncertain nonlinear systems subject to state and input constraints. Two formulations of the adaptive MPC routine are proposed. A general minmax approach is considered. A Lipschitz‐based formulation is also proposed. The closed‐loop robust stability of both approaches is demonstrated. The Lipschitz‐based approach avoids the need for a minmax optimization problem and is amenable to real‐time computation. A simple chemical reactor simulation example is presented that demonstrates the effectiveness of the technique. Copyright © 2010 John Wiley & Sons, Ltd.     

Adaptive robust dynamic surface control with composite adaptation laws

This paper focuses on an adaptive robust dynamic surface control (ARDSC) with composite adaptation laws (CAL) for a class of uncertain nonlinear systems in semi‐strict feedback form. A simple and effective controller has been obtained by introducing dynamic surface control (DSC) technique and designing novel adaptation laws. First, the ‘explosion of terms’ problem caused by backstepping method in the traditional adaptive robust control (ARC) is avoided. Meanwhile, through a new proof philosophy the asymptotical output tracking that the ARC possesses is theoretically preserved. Second, when persistent excitation (PE) condition satisfies, true parameter estimates could be acquired via designing CALs which integrate the information of estimation errors. Finally, simulation results are presented to illustrate the effectiveness of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.     

Adaptive robust control of a class of nonlinear systems in semi‐strict feedback form with non‐uniformly detectable unmeasured internal states

This paper proposes a novel control method for a special class of nonlinear systems in semi‐strict feedback form. The main characteristic of this class of systems is that the unmeasured internal states are non‐uniformly detectable, which means that no observer for these states can be designed to make the observation error exponentially converge to zero. In view of this, a projection‐based adaptive robust control law is developed in this paper for this kind of system. This method uses a projection‐type adaptation algorithm for the estimation of both the unknown parameters and the internal states. Robust feedback term is synthesized to make the system robust to uncertain nonlinearities and disturbances. Although the estimation error for both the unknown parameters and the internal states may not converge to zero, the tracking error of the closed‐loop system is proved to converge to zero asymptotically if the system has only parametric uncertainties. Furthermore, it is theoretically proved that all the signals are bounded, and the control algorithm is robust to bounded disturbances and uncertain nonlinearities with guaranteed output tracking transient performance and steady‐state accuracy in general. The class of system considered here has wide engineering applications, and a practical example—control of mechanical systems with dynamic friction—is used as a case study. Simulation results are obtained to demonstrate the applicability of the proposed control methodology. Copyright © 2010 John Wiley & Sons, Ltd.     

Neural network adaptive robust control with application to precision motion control of linear motors

In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance‐oriented control laws for a class of single‐input–single‐output (SISO) nth‐order non‐ linear systems. Both repeatable (or state dependent) unknown non‐linearities and non‐repeatable unknown non‐linearities such as external disturbances are considered. In addition, unknown non‐linearities can exist in the control input channel as well. All unknown but repeatable non‐linear functions are approximated by outputs of multi‐layer neural networks to achieve a better model compensation for an improved performance. All NN weights are tuned on‐line with no prior training needed. In order to avoid the possible divergence of the on‐line tuning of neural network, discontinuous projection method with fictitious bounds is used in the NN weight adjusting laws to make sure that all NN weights are tuned within a prescribed range. By doing so, even in the presence of approximation error and non‐repeatable non‐linearities such as disturbances, a controlled learning is achieved and the possible destabilizing effect of on‐line tuning of NN weights is avoided. Certain robust control terms are constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy in general. In addition, if the unknown repeatable model uncertainties are in the functional range of the neural networks and the ideal weights fall within the prescribed range, asymptotic output tracking is also achieved to retain the perfect learning capability of neural networks in the ideal situation. The proposed neural network adaptive control (NNARC) strategy is then applied to the precision motion control of a linear motor drive system to help to realize the high‐performance potential of such a drive technology. NN is employed to compensate for the effects of the lumped unknown non‐linearities due to the position dependent friction and electro‐magnetic ripple forces. Comparative experiments verify the high‐performance nature of the proposed NNARC. With an encoder resolution of 1 µm, for a low‐speed back‐and‐forth movement, the position tracking error is kept within ±2 µm during the most execution time while the maximum tracking error during the entire run is kept within ±5.6 µm. Copyright © 2001 John Wiley & Sons, Ltd.     

Adaptive robust control of nonlinear systems with dynamic uncertainties

In this paper, the discontinuous projection‐based adaptive robust control (ARC) approach is extended to a class of nonlinear systems subjected to parametric uncertainties as well as all three types of nonlinear uncertainties—uncertainties could be state‐dependent, time‐dependent, and/or dynamic. Departing from the existing robust adaptive control approach, the proposed approach differentiates between dynamic uncertainties with and without known structural information. Specifically, adaptive robust observers are constructed to eliminate the effect of dynamic uncertainties with known structural information for an improved steady‐state output tracking performance—asymptotic output tracking is achieved when the system is subjected to parametric uncertainties and dynamic uncertainties with known structural information only. In addition, dynamic normalization signals are introduced to construct ARC laws to deal with other uncertainties including dynamic uncertainties without known structural information not only for global stability but also for a guaranteed robust performance in general. Copyright © 2008 John Wiley & Sons, Ltd.     

Reinforcing robustness of adaptive dynamic surface control

Adaptive dynamic surface control (ADSC) design was proposed as an alternative to adaptive backstepping, capable of curing the ‘explosion of complexity’ problem, caused by the repeated differentiations of the so called intermediate control signals. However, as it is clearly demonstrated in this work, ADSC schemes are sensitive to modeling uncertainties and/or additive external disturbances. In fact, it is shown that a uniformly bounded exogenous perturbation of unknown upper bound may easily destabilize the closed‐loop system. Subsequently, a constructive methodology based on the recently developed by the authors prescribed performance control technique, is proposed, which combined with an ADSC design, results in a modified scheme possessing significantly increased robustness properties. Simulation studies illustrate the approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Dynamic modeling and adaptive robust boundary control of a flexible robotic arm with 2‐dimensional rigid body rotation

This paper investigates the control of a single‐link flexible robot manipulator with a tip payload appointed to rotate about 2 perpendicular axes in space. The control objective is to regulate the rigid body rotation of the manipulator with guaranteeing the stability of its vibration in the presence of exogenous disturbances. To achieve this, a Lyapunov‐based control design procedure is used and accomplished in some steps. First, the partial differential equation (PDE) dynamic model governing the rigid‐flexible hybrid motion of the arm is derived by applying Hamilton's principle. Next, based on the developed PDE model, an adaptive robust boundary control is established using the Lyapunov redesign approach. To this end, an adaptation mechanism is proposed so that the robust boundary control gains are dynamically updated online and there is no need for prior knowledge of disturbance upper bounds. The actuators and sensors are fully implemented at the arm boundary without using distributed actuators or sensors. Furthermore, in order to avoid control errors resulting from the spillover, control design is directly based on infinite‐dimensional PDE model without resorting to model truncation. Simulation results illustrate the efficacy of the considered method.     

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.     

Robust adaptive sliding mode control for uncertain delta operator systems

In this paper, a robust adaptive sliding mode controller is presented for delta operator systems with mismatched uncertainties and exogenous disturbances. The parameters of the delta operator system are taken for norm‐bounded uncertainties. The exogenous disturbance is also assumed to be bounded. After the statement of a sufficient condition for the existence of linear sliding surface based on linear matrix inequality technique, a robust reaching motion control method for delta operator systems is presented. Afterwards, an adaptive sliding mode controller for delta operator systems is designed. A bridge between the robust adaptive sliding mode control and the delta operator system framework is made. Numerical example is given to illustrate the effectiveness of the developed techniques. Copyright © 2009 John Wiley & Sons, Ltd.     

Model‐based vs data‐driven adaptive control: An overview

In this paper, we present an overview of adaptive control by contrasting model‐based approaches with data‐driven approaches. Indeed, we propose to classify adaptive controllers into two main subfields, namely, model‐based adaptive control and data‐driven adaptive control. In each subfield, we cite monographs, survey papers, and recent research papers published in the last few years. We also include a few simple examples to illustrate some general concepts in each subfield.     

Robust adaptive tracking control of uncertain discrete time systems

In this paper, the problem of robust adaptive tracking for uncertain discrete‐time systems is considered from the slowly varying systems point of view. The class of uncertain discrete‐time systems considered is subjected to both 𝓁_∞ to 𝓁_∞ bounded unstructured uncertainty and external additive bounded disturbances. A priori knowledge of the dynamic model of the reference signal to be tracked is not completely known. For such problem, an indirect adaptive tracking controller is obtained by frozen‐time controllers that at each time optimally robustly stabilize the estimated models of the plant and minimize the worst‐case steady‐state absolute value of the tracking error of the estimated model over the model uncertainty. Based on 𝓁_∞ to 𝓁_∞ stability and performance of slowly varying system found in the literature, the proposed adaptive tracking scheme is shown to have good robust stability. Moreover, a computable upper bound on the size of the unstructured uncertainty permitted by the adaptive system and a computable tight upper bound on asymptotic robust steady‐state tracking performance are provided. Copyright © 2005 John Wiley & Sons, Ltd.     

Accommodation of unknown actuator faults using output feedback‐based adaptive robust control

In this paper, we solve the problem of output tracking for linear uncertain systems in the presence of unknown actuator failures using discontinuous projection‐based output feedback adaptive robust control (ARC). The faulty actuators are characterized as unknown inputs stuck at unknown values experiencing bounded disturbance and actuators losing effectiveness at unknown instants of time. Many existing techniques to solve this problem use model reference adaptive control (MRAC), which may not be well suited for handling various disturbances and modeling errors inherent to any realistic system model. Robust control‐based fault‐tolerant schemes have guaranteed transient performance and are capable of dealing with modeling errors to certain degrees. But, the steady‐state tracking accuracy of robust controllers, e.g. sliding mode controller, is limited. In comparison, the backstepping‐based output feedback adaptive robust fault‐tolerant control (ARFTC) strategy presented here can effectively deal with such uncertainties and overcome the drawbacks of individual adaptive and robust controls. Comparative simulation studies are performed on a linearized Boeing 747 model, which shows the effectiveness of the proposed scheme. Copyright © 2011 John Wiley & Sons, Ltd.     

Adaptive controller design and disturbance attenuation for SISO linear systems with zero relative degree under noisy output measurements

In this paper, we present robust adaptive controller design for SISO linear systems with zero relative degree under noisy output measurements. We formulate the robust adaptive control problem as a nonlinear H^∞‐optimal control problem under imperfect state measurements, and then solve it using game theory. By using the a priori knowledge of the parameter vector, we apply a soft projection algorithm, which guarantees the robustness property of the closed‐loop system without any persistency of excitation assumption of the reference signal. Owing to our formulation in state space, we allow the true system to be uncontrollable, as long as the uncontrollable part is stable in the sense of Lyapunov, and the uncontrollable modes on the jω‐axis are uncontrollable from the exogenous disturbance input. This assumption allows the adaptive controller to asymptotically cancel out, at the output, the effect of exogenous sinusoidal disturbance inputs with unknown magnitude, phase, and frequency. These strong robustness properties are illustrated by a numerical example. Copyright © 2009 John Wiley & Sons, Ltd.     

Rejection of unknown periodic disturbances for continuous‐time MIMO systems with dynamic uncertainties

Rejection of unknown periodic disturbances in multi‐channel systems has several industrial applications that include aerospace, consumer electronics, and many other industries. This paper presents a design and analysis of an output‐feedback robust adaptive controller for multi‐input multi‐output continuous‐time systems in the presence of modeling errors and broadband output noise. The trade‐off between robust stability and performance improvement as well as practical design considerations for performance improvements are presented. It is demonstrated that proper shaping of the open‐loop plant singular values as well as over‐parameterizing the controller parametric model can significantly improve performance. Numerical simulations are performed to demonstrate the effectiveness of the proposed scheme. Copyright © 2016 John Wiley & Sons, Ltd.     

Indirect adaptive control of a class of marine vehicles

A nonlinear adaptive framework for bounded‐error tracking control of a class of non‐minimum phase marine vehicles is presented. The control algorithm relies on a special set of tracking errors to achieve satisfactory tracking performance while guaranteeing stable internal dynamics. First, the design of a model‐based nonlinear control law, guaranteeing asymptotic stability of the error dynamics, is presented. This control algorithm solves the tracking problem for the considered class of marine vehicles, assuming full knowledge of the system model. Then, the analysis of the zero‐dynamics is carried out, which illustrates the efficacy of the chosen set of tracking errors in stabilizing the internal dynamics. Finally, an indirect adaptive technique, relying on a partial state predictor, is used to address parametric uncertainties in the model. The resulting adaptive control algorithm guarantees Lyapunov stability of the errors and parameter estimates, as well as asymptotic convergence of the errors to zero. Numerical simulations illustrate the performance of the adaptive algorithm. Copyright © 2009 John Wiley & Sons, Ltd.