Robust controller design for input‐delayed systems using predictive feedback and an uncertainty estimator

This paper deals with the problem of stabilizing a class of input‐delayed systems with (possibly) nonlinear uncertainties by using explicit delay compensation. It is well known that plain predictive schemes lack robustness with respect to uncertain model parameters. In this work, an uncertainty estimator is derived for input‐delay systems and combined with a modified state predictor, which uses current available information of the estimated uncertainties. Furthermore, based on Lyapunov–Krasovskii functionals, a computable criterion to check robust stability of the closed‐loop is developed and cast into a minimization problem constrained to an LMI. Additionally, for a given input delay, an iterative‐LMI algorithm is proposed to design stabilizing tuning parameters. The main results are illustrated and validated using a numerical example with a second‐order dynamic system. Copyright © 2016 John Wiley & Sons, Ltd.     

Robust control of uncertain nonlinear systems with state delays based on an uncertainty and disturbance estimator

In this article, one linear and one nonlinear robust control strategies are proposed for uncertain nonlinear continuous‐time systems with disturbances and state delays. The approaches are based on the uncertainty and disturbance estimator (UDE) introduced in 2004. In the case of a linear controller, the terms containing the nonlinear functions and time delays are treated as additional disturbances to the system. In the case of a nonlinear controller, both known and unknown delay scenarios are considered. In the case of an unknown time delay, the terms containing the delay are treated as additional disturbances to the system. The algorithms provide excellent tracking and disturbance rejection performance. Simulations are given to show the effectiveness of the strategies, first via a simple example and second via an application to a continuous stirred tank reactor system. Copyright © 2010 John Wiley & Sons, Ltd.     

Robust output feedback control of polynomial growth nonlinear systems with measurement uncertainty

Even in the presence of uncertainty in both state and output equations, we prove that global asymptotic stabilization is still possible by output feedback for a family of uncertain nonlinear systems dominated by a triangular system with a polynomial output‐dependent growth rate. In contrast to the linear growth requirement in the recent work the nonlinear perturbations in this paper are allowed to satisfy a linear growth condition with a polynomial output‐dependent rate. To handle simultaneously the polynomial nonlinearities and unknown parameter in the system output, we propose a high‐gain estimator with a dynamic gain that is updated online through a Riccati‐type dynamic equation. Then, an estimator‐based controller is designed by a recursive algorithm that makes it possible to assign the controller gains step by step. The globally stabilizing output‐feedback controller developed in this paper is robust with respect to uncertainties in the system dynamics and output equations.     

Robust control for nonlinear systems with unknown perturbations using simplified robust right coprime factorisation

This article is concerned with operator-based robust control for nonlinear feedback system with unknown perturbations with simplified robust right coprime factorisation method. By the designed simplified robust controllers, the Bezout identity for the nominal nonlinear system and the perturbed nonlinear system are guaranteed to be simply satisfied, which means that not only the nonlinear system with nominal plant but also that with perturbed plant can be stabilised. Moreover, the plant output can be guaranteed to asymptotically track the reference output. The effectiveness of the proposed design scheme is verified by the simulation results.     

Robust control for uncertain nonlinear systems with state‐dependent control direction

The control direction is the multiplier of the control term. In this study, the control direction is considered as an unknown function of state and allowed to cross zero and change its sign smoothly. Based on the analysis of system dynamics at the points where the control direction is zero, a robust controller is proposed by integrating with a bounding function and a Nussbaum‐type gain. Under the proposed controller, the system approaches zero if zero is accessible, or to the accessible point closest to zero if zero is not accessible by any control. The control performance is illustrated by simulated examples. Copyright © 2010 John Wiley & Sons, Ltd.     

Robust control of multi‐axis magnetic bearing systems

This paper proposes robust control schemes for multi‐axis magnetic bearing systems under current control and voltage control. The control schemes are based on a model which characterizes not only the electromagnetic interaction but also secondary effects such as flux leakage, fringing fluxes, and finite core permeance in the magnetic bearing. In the current control case, a sliding‐mode‐based robust controller, whose control parameters have to be deliberately chosen to account for the uni‐directionality of magnetic forces, is proposed to provide robust performance against uncertain dynamics. In the voltage control case, a control scheme that combines the sliding methodology and the backstepping technique is used to provide robust performance under the influence of the electrical dynamics. It is demonstrated through simulations and experiments that, because the nonlinearity and the uncertainty inherently residing in the system behaviour are directly tackled, the proposed controllers achieve superior tracking performances compared to control lers based on linearization. Copyright © 2001 John Wiley & Sons, Ltd.     

Robust iterative learning control for systems with norm‐bounded uncertainties

A new robust iterative learning control scheme is presented for state tracking control of nonlinear MIMO systems. The main characteristic of the proposed controller lies in its ability to deal with unstructured uncertainties that are norm‐bounded but not globally or locally Lipschitz continuous as usual. The classical resetting condition of iterative learning control is removed and replaced with more practical alignment condition. The class of systems to be considered is further extended to more general scenarios, in which input distribution uncertainties are included. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed control scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

Robust model predictive control for polytopic uncertain systems with state saturation nonlinearities under Round‐Robin protocol

This paper is concerned with the robust model predictive control (RMPC) problem for polytopic uncertain systems with state saturation nonlinearities under the Round‐Robin (RR) protocol. With respect to the practical application, one of the most commonly encountered obstacles that stem from the physical limitation of system components, ie, state saturation, is adequately taken into consideration. In order to reduce the network transmission burden and improve the utilization of the network from the controller nodes to the actuator node, a so‐called RR protocol is employed to orchestrate the data transmission order. At each transmission instant, only one controller node that obtains the priority is accessible to the shared communication network. Our aim of the underlying problem is to design a set of controllers in the framework of RMPC such that the closed‐loop system is asymptotically stable. By taking the influence of the RR protocol and the state saturation precisely into account, some sufficient criteria are established in terms of the token‐dependent Lyapunov‐like approach. Then, an online optimization problem subjected to some matrix inequality constraints is provided, and the desired controllers can be obtained by solving the certain upper bound of the objective addressed. Finally, a distillation process example is provided to illustrate the effectiveness of the proposed RMPC approach.     

Robust optimal tracking control for multiplayer systems by off‐policy Q‐learning approach

In this article, a novel off‐policy cooperative game Q‐learning algorithm is proposed for achieving optimal tracking control of linear discrete‐time multiplayer systems suffering from exogenous dynamic disturbance. The key strategy, for the first time, is to integrate reinforcement learning, cooperative games with output regulation under the discrete‐time sampling framework for achieving data‐driven optimal tracking control and disturbance rejection. Without the information of state and input matrices of multiplayer systems, as well as the dynamics of exogenous disturbance and command generator, the coordination equilibrium solution and the steady‐state control laws are learned using data by a novel off‐policy Q‐learning approach, such that multiplayer systems have the capability of tolerating disturbance and follow the reference signal via the optimal approach. Moreover, the rigorous theoretical proofs of unbiasedness of coordination equilibrium solution and convergence of the proposed algorithm are presented. Simulation results are given to show the efficacy of the developed approach.     

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.     

Worst‐case control policies for (terminal) linear–quadratic control problems under disturbances

We consider linear control systems under uncertainties. For such systems we solve the problem of constructing worst‐case feedback control policies that are allowed to be corrected at m fixed intermediate time moments. We propose two types of the approximative control policies. All of them guarantee that for all admissible uncertainties the terminal system state lies in a prescribed neighborhood of a given state x_* at a given final moment, and the value of the cost function does not exceed a given estimate. It is shown that computation of the estimate for each policy is equivalent to solving a corresponding convex mathematical programming (MP) problem with m decision variables. Based on the solution of the MP problem, we derive simple explicit rules (which can be easily implemented on‐line) for constructing the corresponding control policy in the original control problem. Copyright © 2009 John Wiley & Sons, Ltd.     

Adaptive model predictive control of nonlinear systems with state‐dependent uncertainties

This paper studies adaptive model predictive control (AMPC) of systems with time‐varying and potentially state‐dependent uncertainties. We propose an estimation and prediction architecture within the min‐max MPC framework. An adaptive estimator is presented to estimate the set‐valued measures of the uncertainty using piecewise constant adaptive law, which can be arbitrarily accurate if the sampling period in adaptation is small enough. Based on such measures, a prediction scheme is provided that predicts the time‐varying feasible set of the uncertainty over the prediction horizon. We show that if the uncertainty and its first derivatives are locally Lipschitz, the stability of the system with AMPC can always be guaranteed under the standard assumptions for traditional min‐max MPC approaches, while the AMPC algorithm enhances the control performance by efficiently reducing the size of the feasible set of the uncertainty in min‐max MPC setting. Copyright © 2017 John Wiley & Sons, Ltd.     

Robust consensus for uncertain multi‐agent systems with discrete‐time dynamics

This paper investigates robust consensus for multi‐agent systems with discrete‐time dynamics affected by uncertainty. In particular, the paper considers multi‐agent systems with single and double integrators, where the weighted adjacency matrix is a polynomial function of uncertain parameters constrained into a semialgebraic set. Firstly, necessary and sufficient conditions are provided for robust consensus based on the existence of a Lyapunov function polynomially dependent on the uncertainty. In particular, an upper bound on the degree required for achieving necessity is provided. Secondly, a necessary and sufficient condition is provided for robust consensus with single integrator and nonnegative weighted adjacency matrices based on the zeros of a polynomial. Lastly, it is shown how these conditions can be investigated through convex programming by exploiting linear matrix inequalities and sums of squares of polynomials. Some numerical examples illustrate the proposed results. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust adaptive output feedback control of a class of discrete‐time nonlinear systems with nonlinear uncertainties and unknown control directions

In this paper, robust adaptive output feedback control is studied for a class of discrete‐time nonlinear systems with functional nonlinear uncertainties of the Lipschitz type and unknown control directions. In order to construct an output feedback control, the system is transformed into the form of a nonlinear autoregressive moving average with eXogenous inputs (NARMAX) model. In order to avoid the noncausal problem in the control design, future output prediction laws and parameter update laws with the dead‐zone technique are constructed on the basis of the NARMAX model. With the employment of the predicted future outputs, a constructive output feedback adaptive control is proposed, where the discrete Nussbaum gain technique and the dead‐zone technique are used in parameter update laws. The effect of the functional nonlinear uncertainties is compensated for, such that an asymptotic tracking performance is achieved, whereas other signals in the closed‐loop systems are guaranteed to be bounded. Simulation studies are performed to demonstrate the effectiveness of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Worst‐case optimal control for an electrical drive system with time‐delay

This paper considers an optimal control developed for an electrical drive system with a DC motor. Since it is a linear control system with input time‐delay subject to unknown but bounded disturbances, we construct a worst‐case feedback control policy, which can guarantee that, for all admissible uncertain disturbances, the real system state should be in a prescribed neighborhood of a desired value at the given final time, and the cost functional takes the best guarantee value. The worst‐case feedback control policy is allowed to be corrected at a given set of correction points between the initial and the final time, which is equivalent to solving a (m‐1)‐level min‐max problem. Since the min‐max problem at each stage does not yield a simple analytical solution, construction of the optimal policy is computationally prohibitive. This is why we consider an approximate control policy which is more convenient for computation. The simulation results illustrate that this proposed approach is feasible. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Event‐triggered observer‐based robust H∞ control for networked control systems with unknown disturbance

In this article, the event‐triggered robust H_∞ control is studied for a class of uncertain networked control systems (NCSs) subject to unknown state and variable disturbance. First, aiming to decrease the unnecessary transmissions of sampled data, an efficient adaptive event‐triggered scheme (AETS) is presented, which can reflect the full real‐time variation of addressed NCSs and help to reduce the conservativeness. Second, based on the triggered output signals and disturbance model, two effective observers are, respectively, exploited to estimate the state and disturbance, which are further utilized to reject the disturbance and design the controller. By using the overall closed‐loop system and selecting an augmented Lyapunov‐Krasovskii functional, two sufficient conditions on jointly designing the adaptive event scheme, observers, and controller are established via linear matrix inequality forms, which can guarantee the global exponential stability and ensure H_∞ performance. Finally, some simulations and comparisons in a numerical example are provided to demonstrate the effectiveness of the derived results.     

Robust control approach for input–output linearizable nonlinear systems using high‐gain disturbance observer

This paper addresses a robust control approach for a class of input–output linearizable nonlinear systems with uncertainties and modeling errors considered as unknown inputs. As known, the exact feedback linearization method can be applied to control input–output linearizable nonlinear systems, if all the states are available and modeling errors are negligible. The mentioned two prerequisites denote important problems in the field of classical nonlinear control. The solution approach developed in this contribution is using disturbance rejection by applying feedback of the uncertainties and modeling errors estimated by a specific high‐gain disturbance observer as unknown inputs. At the same time, the nonmeasured states can be calculated from the estimation of the transformed system states. The feasibility and conditions for the application of the approach on mechanical systems are discussed. A nonlinear multi‐input multi‐output mechanical system is taken as a simulation example to illustrate the application. The results show the robustness of the control design and plausible estimations of full‐rank disturbances.Copyright © 2012 John Wiley & Sons, Ltd.     

Robust adaptive fault‐tolerant quantized control of nonlinear systems with constraints on system behaviors and states

In this work, we develop a robust adaptive fault‐tolerant tracking control scheme for a class of input‐quantized strict‐feedback nonlinear systems in the presence of error/state constraints and actuation faults. The problem is rather complicated yet challenging if nonparametric uncertainties and unknown quantization parameters as well as time‐varying yet completely undetectable actuation faults are involved in the considered systems. Compared with the most existing approaches in the literature, the proposed control exhibits several attractive advantages: (1) upon using a nonlinear decomposition for quantized input and employing the robust technique for actuation fault, not only the exact knowledge of quantization parameters are not required, but also the actuation fault can be easily compensated since neither fault detection and diagnosis/fault detection and identification nor controller reconfiguration is needed; (2) based on the error/state‐dependent unified nonlinear function, the constraints on tracking error and system states are directly handled and the cases with or without constraints can also be addressed in a unified manner without changing the control structure; and (3) the utilization of unified nonlinear function‐based dynamic surface control not only avoids the problem of the explosion of complexity in traditional backstepping design, but also bypasses the demanding feasibility conditions of virtual controllers. Furthermore, by using the Lyapunov analysis, it is ensured that all signals in the closed‐loop systems are uniformly ultimately bounded. The effectiveness of the developed control algorithm is confirmed by numerical simulations.     

Robust adaptive control for a class of uncertain strict‐feedback nonlinear systems

In this paper, robust adaptive control is presented for a class of perturbed strict‐feedback nonlinear systems with both completely unknown control coefficients and parametric uncertainties. The proposed design method does not require the a priori knowledge of the signs of the unknown control coefficients. For the first time, the key technical Lemma is proven when the Nussbaum function is chosen by N(ζ)=ζ²cos(ζ), based on which the proposed robust adaptive scheme can guarantee the global uniform ultimate boundedness of the closed‐loop system signals. Simulation results show the validity of the proposed scheme. Copyright © 2008 John Wiley & Sons, Ltd.     

Robust nonovershooting tracking control for fractional‐order systems

A robust tracking control is proposed for the fractional‐order systems (FOSs) to achieve a tracking response with no overshoot, even in the presence of a class of disturbances. The control proposed makes use of a newly designed integral sliding mode technique for FOSs, which is capable of rejecting the bounded disturbances acting through the input channel. The proposed integral sliding mode control design has two components: a nominal control component and a discontinuous control component. The overshoot in the system response is avoided by the nominal control designed with the use of Moore's eigenstructure assignment algorithm. The sliding mode technique is used for the design of discontinuous part of the control that imparts the desired robustness properties.