Showing papers in "International Journal of Robust and Nonlinear Control in 2019"
152 citations
TL;DR: This paper provides a time-varying feedback alternative to control of finite-time systems, referred to as “prescribed-time control,” exhibiting several superior features, including building upon regular state feedback rather than fractional-power state feedback.
Abstract: Funding information National Natural Science Foundation of China, Grant/Award Number: 61773081; technology transformation program of Chongqing higher education university, Grant/Award Number: KJZH17102 Summary This paper provides a time-varying feedback alternative to control of finite-time systems, which is referred to as “prescribed-time control,” exhibiting several superior features: (i) such time-varying gain–based prescribed-time control is built upon regular state feedback rather than fractional-power state feedback, thus resulting in smooth (Cm) control action everywhere during the entire operation of the system; (ii) the prescribed-time control is characterized with uniformly prespecifiable convergence time that can be preassigned as needed within the physically allowable range, making it literally different from not only the traditional finite-time control (where the finite settling time is determined by a system initial condition and a number of design parameters) but also the fixed-time control (where the settling time is subject to certain constraints and thus can only be specified within the corresponding range); and (iii) the prescribed-time control relies only on regular Lyapunov differential inequality instead of fractional Lyapunov differential inequality for stability analysis and thus avoids the difficulty in controller design and stability analysis encountered in the traditional finite-time control for high-order systems.
147 citations
TL;DR: The purpose of the addressed problem is to develop a distributed filtering strategy such that, in the presence of multiplicative stochastic link noises and switching topology, the resulting filtering error dynamics is exponentially stable in the mean square sense and also satisfies the prespecified weighted disturbance attenuation level.
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TL;DR: In this article, the convergence time analysis of a class of fixed-time stable systems with the aim to provide a new non-conservative upper bound for its settling time is discussed. But the convergence times are not directly considered.
Abstract: This paper deals with the convergence time analysis of a class of fixed-time stable systems with the aim to provide a new non-conservative upper bound for its settling time. Our contribution is fourfold. First, we revisit the well-known class of fixed-time stable systems, given in (Polyakov et al.,2012}, while showing the conservatism of the classical upper estimate of the settling time. Second, we provide the smallest constant that uniformly upper bounds the settling time of any trajectory of the system under consideration. Third, introducing a slight modification of the previous class of fixed-time systems, we propose a new predefined-time convergent algorithm where the least upper bound of the settling time is set a priori as a parameter of the system. At last, predefined-time controllers for first order and second order systems are introduced. Some simulation results highlight the performance of the proposed scheme in terms of settling time estimation compared to existing methods.
TL;DR: The proposed solution method is significantly superior to a standard random sampling method for stochastic optimal control in terms of computational requirements and the moment-based surrogate for the JCC is shown to be substantially less conservative than the widely used distributionally robust Cantelli-Chebyshev inequality for chance constraint approximation.
Abstract: Correspondence Ali Mesbah, Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA. Email: mesbah@berkeley.edu Summary Stochastic model predictive control hinges on the online solution of a stochastic optimal control problem. This paper presents a computationally efficient solution method for stochastic optimal control for nonlinear systems subject to (time-varying) stochastic disturbances and (time-invariant) probabilistic model uncertainty in initial conditions and parameters. To this end, new methods are presented for joint propagation of time-varying and time-invariant probabilistic uncertainty and the nonconservative approximation of joint chance constraint (JCC) on the system state. The proposed uncertainty propagation method relies on generalized polynomial chaos and conditional probability rules to obtain tractable expressions for the state mean and covariance matrix. A moment-based surrogate is presented for JCC approximation to circumvent construction of the full probability distribution of the state or the use of integer variables as required when using the sample average approximation. The proposed solution method for stochastic optimal control is illustrated on a nonlinear semibatch reactor case study in the presence of probabilistic model uncertainty and stochastic disturbances. It is shown that the proposed solution method is significantly superior to a standard random sampling method for stochastic optimal control in terms of computational requirements. Furthermore, the moment-based surrogate for the JCC is shown to be substantially less conservative than the widely used distributionally robust Cantelli-Chebyshev inequality for chance constraint approximation.
TL;DR: A novel tan‐type barrier Lyapunov function combined with manipulating sign functions is proposed to develop a systematic approach that guides us to construct a state feedback finite‐time stabilizer for high‐order nonlinear systems while preventing the violation of a prespecified output constraint during operation.
TL;DR: A novel adaptive neural integral sliding mode control (ANISMC) to enhance the tracking performance of fully actuated uncertain surface vessels is developed based on an integrating between the benefits of the approximation capability of neural network (NN) and the high robustness and precision of the integral sliding Mode control (ISMC).
Abstract: This paper develops a novel adaptive neural integral sliding mode control (ANISMC) to enhance the tracking performance of fully actuated uncertain surface vessels. The proposed method is built based on an integrating between the benefits of the approximation capability of neural network (NN) and the high robustness and precision of the integral sliding mode control (ISMC). In this paper, the design of NN, which is used to approximate the unknown dynamics, is simplified such that just only one simple adaptive rule is needed. The ISMC, which can eliminate the reaching phase and offer higher tracking performance compared to the conventional sliding mode control, is designed such that the system robust against the approximation error and stabilize the whole system. The design procedure of the proposed controller is constructed according to the backstepping control technique so that the stability of the closed-loop system is guaranteed based on Lyapunov criteria. The proposed method is then tested on a simulated vessel system using computer simulation and compared with other state-of-the-art methods. The comparison results demonstrate the superior performance of the proposed approach.
TL;DR: In this article, the problem of sliding mode control design for nonlinear plant is studied and necessary and sufficient conditions of quadratic-like stability (stabi-lizability) for a nonlinear homogeneous (control) system are derived.
Abstract: The problem of sliding mode control design for nonlinear plant is studied. Necessary and sufficient conditions of quadratic-like stability (stabi-lizability) for nonlinear homogeneous (control) system are obtained. Sufficient conditions of robust stability/stabilizability are deduced. The results are supported with academic examples of sliding mode control design.
TL;DR: A review of existing estimation methods focusing on sliding mode techniques, followed by the development of a novel friction estimation technique which is used to design an efficient ABS control system and demonstrated that the sliding mode observer paradigm is an important tool in developing next generation ABS systems for EVs.
Abstract: The introduction of electric braking via brake‐by‐wire systems in electric vehicles) has reduced the high transportation delays usually involved in conventional friction braking systems. This has facilitated the design of more efficient and advanced control schemes for antilock braking systems (ABSs). However, accurate estimation of the tire‐road friction coefficient, which cannot be measured directly, is required. This paper presents a review of existing estimation methods, focusing on sliding‐mode techniques, followed by the development of a novel friction estimation technique, which is used to design an efficient ABS control system. This is a novel slip‐based estimation method, which accommodates the coupling between the vehicle dynamics, wheel dynamics, and suspension dynamics in a cascaded structure. A higher‐order sliding‐mode observer–based scheme is designed, considering the nonlinear relationship between friction and slip. A first‐order sliding‐mode observer is also designed based on a purely linear relationship. A key feature of the proposed estimation schemes is the inclusion of road slope and the effective radius of the tire as an estimated state. These parameters impact significantly on the accuracy of slip and friction estimation. The performance of the proposed estimation schemes are validated and benchmarked against a Kalman filter (KF) by a series of simulation tests. It is demonstrated that the sliding‐mode observer paradigm is an important tool in developing the next generation ABS systems for electric vehicles.