A discrete‐time indirect adaptive multiple‐model actuator failure compensation scheme

A new discrete‐time actuator failure compensation control scheme is developed, using a multiple‐model adaptive control approach which has the capacity to achieve faster and more accurate compensation of failure uncertainties. An individual adaptive system, for each possible failure pattern in a failure pattern set of interest for compensation, is designed using an indirect model reference adaptive control scheme for actuator failure compensation. A multiple‐model control switching mechanism for discrete‐time systems is set up by finding the minimal performance index to select the most appropriate control law. The performance indices are based on the adaptive estimation errors of individual parameterized systems with actuator failures. Simulation results from an aircraft flight control system example are presented to show the desired closed‐loop system stability and tracking performance despite the presence of uncertain actuator failures. Copyright © 2014 John Wiley & Sons, Ltd.     

Transient performance improvement in indirect model reference adaptive control using perturbation‐based extremum seeking identifier

One of the main drawbacks of model reference adaptive control (MRAC) is the weakness of its transient performance. The key reason of this imperfection is parameter's estimation error convergence. For many cases in the closed‐loop control, the plant input signal cannot satisfy the persistence of excitation (PE) condition which yields poor parameters estimation error convergence. In this paper, we use a fast perturbation‐based extremum seeking (PES) scheme without steady‐state oscillation as the parameter identifier in indirect MRAC. The estimated parameters through the PES identifier contain the additive sinusoidal signals with distinct frequencies in the transient, which satisfy the PE condition of the plant input. Therefore, convergence of the parameters estimation error to zero will be guaranteed that results in improvement of transient performance for indirect MRAC. Also, the contrary effects on the steady‐state behaviour is eliminated since the sinusoidal excitation signals amplitude exponentially converge to zero and reinitiate with every change in the unknown parameters. Simulation results for a second order example have been presented to illustrate the effectiveness of the proposed scheme.     

State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors

This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems. It is well known that the extended Kalman filter (EKF) is proposed based on the Taylor expansion to linearize the nonlinear model. In this paper, we show that the EKF method is not suitable for bilinear systems because the linearization method for bilinear systems cannot describe the behavior of the considered system. Therefore, this paper proposes a state filtering method for the single‐input–single‐output bilinear systems by minimizing the covariance matrix of the state estimation errors. Moreover, the state estimation algorithm is extended to multiple‐input–multiple‐output bilinear systems. The performance analysis indicates that the state estimates can track the true states. Finally, the numerical examples illustrate the specific performance of the proposed method.     

Nonlinear reference tracking control of a gas turbine with load torque estimation

Input–output linearization‐based adaptive reference tracking control of a low‐power gas turbine model is presented in this paper. The gas turbine is described by a third‐order nonlinear input‐affine state‐space model, where the manipulable input is the fuel mass flowrate and the controlled output is the rotational speed. The stability of the one‐dimensional zero dynamics of the controlled plant is investigated via phase diagrams. The input–output linearizing feedback is extended with a load torque estimator algorithm resulting in an adaptive feedback scheme. The tuning of controller parameters is performed considering three main design goals: appropriate settling time, robustness against environmental disturbances and model parameter uncertainties, and avoiding the saturation of the actuator. Simulations show that the closed‐loop system is robust with respect to the variations in uncertain model and environ‐mental parameters and its performance satisfies the defined requirements. Copyright © 2007 John Wiley & Sons, Ltd.     

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.     

An adaptive feedback linearized model predictive controller design for a nonlinear multi-input multi-output system

In this work, an adaptive feedback linearized model predictive control (AFLMPC) scheme is proposed to compensate system uncertainty for a class of nonlinear multi-input multi-output system. Initially, a feedback linearization technique is used to transform the nonlinear dynamics into an exact linear model, thereafter, a model predictive control scheme is designed to obtain the desired tracking performance. A suitable constraint mapping algorithm has been developed to map input constraints to the new virtual input of the proposed control scheme. The proposed control scheme utilizes multiple estimation model and the concept of second-level adaptation technique Pandey et al. (2014) to handle the parametric uncertainty in real-time. Hence, the adaptive term in the control scheme is used to counteract the effect of model uncertainties and parameter adaptation errors. The effectiveness of the proposed AFLMPC control algorithm has been verified successfully in simulation as well as the experimental setup of the TRMS model. The unavailable states of the nonlinear system have been estimated using an extended Kalman filter based state observer. The performance of the proposed control algorithm has been compared with other existing nonlinear control techniques in simulation and experimental validation.     

Adaptive actuator failure compensation design for unknown chaotic multi‐input systems

In this paper, an adaptive control approach is designed for compensating the faults in the actuators of chaotic systems and maintaining the acceptable system stability. We propose a state‐feedback model reference adaptive control scheme for unknown chaotic multi‐input systems. Only the dimensions of the chaotic systems are required to be known. Based on Lyapunov stability theory, new adaptive control laws are synthesized to accommodate actuator failures and system nonlinearities. An illustrative example is studied. The simulation results show the effectiveness of the design method. Copyright © 2014 John Wiley & Sons, Ltd.     

Adaptive state‐feedback control with sensor failure compensation for asymptotic output tracking

This paper addresses a new adaptive output tracking problem in the presence of uncertain plant dynamics and uncertain sensor failures. A new unified nominal state‐feedback control law is developed to deal with various sensor failures, which is directly constructed by state sensor outputs. Such a new state‐feedback compensation control law is able to ensure the desired plant‐model matching properties under different failure patterns. Based on the nominal compensation control design, a new adaptive compensation control scheme is proposed, which guarantees closed‐loop signal boundedness and asymptotic output tracking. The new adaptive compensation scheme not only expands the sensor failures types that the system could tolerate but also avoids some signal processing procedures that the traditional fault‐tolerant control techniques are forced to encounter. A complete stability analysis and a representative simulation study are conducted to evaluate the effectiveness of the proposed adaptive compensation control scheme.     

A new decentralized model reference adaptive control for a class of interconnected dynamic systems using variable structure design

This paper presents a new decentralized model reference adaptive control for a class of large-scale interconnected dynamic systems. Interconnections among subsystems may be time-invariant or time-varying and linear or non-linear. The scheme proposed here only takes input and output measurements from each subsystem for input synthesis. Using a variable structure design concept, we show that the tracking errors will converge to zero in finite time despite the interconnections with any possible strengths.     

Actuator fault‐tolerant control based on set separation

In this paper, an actuator fault‐tolerant control (FTC) strategy based on set separation is presented. The proposed scheme employs a standard configuration consisting of a bank of observers which match the different fault situations that can occur in the plant. Each of these observers has an associated estimation error with a distinctive behaviour when a estimator matches the current fault situation of the plant. With this information from each observer, a fault diagnosis and isolation (FDI) module is able to reconfigure the control loop by selecting the appropriate stabilising controller from a bank of precomputed control laws, each of them related to one of the considered fault models. The control law consists of a reference feedforward term and a feedback gain multiplying the state estimate provided by the matching observer. A novel feature of the proposed scheme resides in the decision criteria of the FDI, which is based on the computation of sets towards which the output estimation errors related to each fault scenario and for each control configuration converge. Conditions for the design of the FDI module and for fault tolerant closed‐loop stability are given, and the effectiveness of the approach is illustrated by means of a numerical example. Copyright © 2010 John Wiley & Sons, Ltd.     

Adaptive robust output‐feedback motion control of hydraulic actuators

Most previous advanced motion control of hydraulic actuators used full‐state feedback control techniques. However, in many cases, only position feedback is available, and thus, there are imperious demands for output‐feedback control for hydraulic systems. This paper firstly transforms a hydraulic model into an output feedback–dependent form. Thus, the K‐filter can be employed, which provides exponentially convergent estimates of the unmeasured states. Furthermore, this observer has an extended filter structure so that online parameter adaptation can be utilized. In addition, it is a well‐known fact that any realistic model of a hydraulic system suffers from significant extent of uncertain nonlinearities and parametric uncertainties. This paper constructs an adaptive robust controller with backstepping techniques, which is able to take into account not only the effect of parameter variations coming from various hydraulic parameters but also the effect of hard‐to‐model nonlinearities such as uncompensated friction forces, modeling errors, and external disturbances. Moreover, estimation errors that come from initial state estimates and uncompensated disturbances are dealt with via certain robust feedback at each step of the adaptive robust backstepping design. After that, a detailed stability analysis for the output‐feedback closed‐loop system is scrupulously checked, which shows that all states are bounded and that the controller achieves a guaranteed transient performance and final tracking accuracy in general and asymptotic output tracking in the presence of parametric uncertainties only. Extensive experimental results are obtained for a hydraulic actuator system and verify the high‐performance nature of the proposed output‐feedback control strategy.     

Extending the Frisch scheme for errors‐in‐variables identification to correlated output noise

Several estimation methods have been proposed for identifying errors‐in‐variables systems, where both input and output measurements are corrupted by noise. One of the promising approaches is the so‐called Frisch scheme. In its standard form, it is designed to handle white measurement noise on the input and output sides. As the output noise comprises both effects of measurement errors and of process disturbances, it is much more realistic to allow correlated output noise. It is described in the paper how the Frisch scheme can be extended to such cases. Copyright © 2007 John Wiley & Sons, Ltd.     

Extended fuzzy function model with stable learning methods for online system identification

The aim of the online nonlinear system identification is the accurate modeling of the current local input‐output behavior of the plant without using any prior knowledge and offline modeling phase. It is a challenging task for many intelligent systems when used for real‐time control applications. In this paper, we propose a novel computationally efficient extended fuzzy functions (EFF) model for system identification of unknown nonlinear discrete‐time systems. The main contributions are to introduce an effective quasi‐nonlinear model (EFF) and propose adaptive learning rates (ALR) for recursive least squares (RLS) and gradient‐descent (GD) methods. The asymptotic convergence of the modeling errors and boundedness of the parameters are proved by using the input‐to‐state stability (ISS) approach. Numerical simulations are performed for Box–Jenkins gas furnace system and a nonlinear dynamic system. The benefits of its accuracy, stability and simple implementation in practice indicate that EFF model is a promising technique for online identification of nonlinear systems. Copyright © 2010 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.     

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.     

On the need of projections in input‐error model reference adaptive control

The main objective of this note is to contribute, if modestly, toward the understanding of the input‐error model reference adaptive control scheme revealing an instability mechanism that arises if the projection of the plant high‐frequency gain coefficient estimate is omitted. In addition, a self‐contained proof of global convergence of the scheme with the projections for a simple first‐order plant is given.     

Mapping filtered forwarding‐based robust adaptive control for uncertain nonlinear systems with input constraint

This work develops a robust adaptive control algorithm for uncertain nonlinear systems with parametric uncertainties and external disturbances satisfying an extended matching condition. This control method is implemented in the framework of a mapping filtered forwarding‐based technique. As an attractive alternative of the adaptive backstepping method, this bottom‐up strategy forms a virtual controller and a parameter updated law at each step of the design, where Lyapunov functions and the prior knowledge of system parameters are not required. The boundedness of all signals is guaranteed by using Barbalat's lemma. According to immersion relationship, a compliant behavior of systems behaves accordingly to the lower‐order target dynamics. Furthermore, input constraints are handled by estimating a saturated scaling. A spring, mass, and damper system is used to demonstrate the controller performances via simulation results.     

An adaptive design for quantized feedback control of uncertain switched linear systems

This paper addresses the problem of asymptotic tracking for switched linear systems with parametric uncertainties and dwell‐time switching, when input measurements are quantized due to the presence of a communication network closing the control loop. The problem is solved via a dynamic quantizer with dynamic offset that, embedded in a model reference adaptive control framework, allows the design of the adaptive adjustments for the control parameters and for the dynamic range and dynamic offset of the quantizer. The overall design is carried out via a Lyapunov‐based zooming procedure, whose main feature is overcoming the need for zooming out at every switching instant, in order to compensate for the possible increment of the Lyapunov function at the switching instants. It is proven analytically that the resulting adjustments guarantee asymptotic state tracking. The proposed quantized adaptive control is applied to the piecewise linear model of the NASA Generic Transport Model aircraft linearized at multiple operating points.     

Fault‐tolerant control using command‐filtered adaptive back‐stepping technique: Application to hypersonic longitudinal flight dynamics

In this paper, the adaptive back‐stepping controller is investigated for a class of strict‐feedback systems using the command filter technique. Adaptive laws are designed for updating the controller parameters when both the plant parameters and actuator‐failure parameters are unknown. Furthermore, the auxiliary dynamics is developed to deal with the input constraints. Closed‐loop stability and asymptotic‐state tracking are ensured. The method is applied to the longitudinal dynamics of a generic hypersonic aircraft in the presence of actuator faults and input constraints. Based on the parameter estimation, the command‐filtered adaptive back‐stepping control is presented. Simulation results on the control‐oriented model show that the proposed approach achieves good tracking performance. Copyright © 2015 John Wiley & Sons, Ltd.