Saturated control of an uncertain nonlinear system with input delay

Fischer, N.; Dani, Ashwin P.; Sharma, Nitin; Dixon, Warren E.

doi:10.1016/j.automatica.2013.02.013

Cited by 90 publications

(41 citation statements)

References 26 publications

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“…Such systems serve as models for the dynamics of traffic [22], [47], teleoperators [24] and robotic manipulators [2], [20], motors [34], [50], multi-agent systems [1], [18], [40], autonomous ground vehicles [39], unmanned aerial vehicles [23] and planar vertical take-off and landing aircrafts [21], [48], and the human musculoskeletal system in applications such as neuromuscular electrical stimulation [32], [38], [51], to name only a few. Motivated by the negative effects of input delays on the stability and performance of such control systems, in this article we present control designs that achieve delay compensation.…”

Section: Introductionmentioning

confidence: 99%

Predictor-Feedback Stabilization of Multi-Input Nonlinear Systems

Bekiaris-Liberis

Krstić

2017

IEEE Trans. Automat. Contr.

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We develop a predictor-feedback control design for multi-input nonlinear systems with distinct input delays, of arbitrary length, in each individual input channel. Due to the fact that different input signals reach the plant at different time instants, the key design challenge, which we resolve, is the construction of the predictors of the plant's state over distinct prediction horizons such that the corresponding input delays are compensated. Global asymptotic stability of the closed-loop system is established by utilizing arguments based on Lyapunov functionals or estimates on solutions. We specialize our methodology to linear systems for which the predictor-feedback control laws are available explicitly and for which global exponential stability is achievable. A detailed example is provided dealing with the stabilization of the nonholonomic unicycle, subject to two different input delays affecting the speed and turning rate, for the illustration of our methodology.

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Section: Introductionmentioning

confidence: 99%

Predictor-Feedback Stabilization of Multi-Input Nonlinear Systems

Bekiaris-Liberis

Krstić

2017

IEEE Trans. Automat. Contr.

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“…In this work, the delay is removed since we only would like to investigate the development of control to tackle multiple constraint problem. The method to cope with input delay is very trivial and many state-of-the-art works can be found [27][28][29].…”

Section: Remarkmentioning

confidence: 99%

Neural Network Based Central Heating System Load Prediction and Constrained Control

Wang

Tu³

et al. 2018

Mathematical Problems in Engineering

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A neural network (NN) based heating system load prediction and control scheme are proposed. Different from traditional physical principle based load calculation method, a multilayer NN is incorporated with selected input features and trained to predict the heating load as well as the desired supply water temperature in heating supply loop. In this manner, a complicated load calculation model can be replaced by simple but efficient data-driven scheme and the response time to outdoor temperature variation can be enhanced. Moreover, in order to handle the input and output constraints in valve opening degree control task to achieve desired supply water temperature, Barrier Lyapunov candidate function and axillary system technique are involved. An additional NN is employed to approximate the system transfer function with reliable accuracy. The stability of the system is guaranteed through rigorous mathematical analysis. The excellent performance of the novelly proposed control over traditional PID is demonstrated via extensive simulation study. A quantitative case study is also conducted to verify the flexibility and validity of proposed load prediction strategy.

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“…Compared with [20][21][22][23][24][25][26][27][28][29][30][31][32][33], there are three advantages. Firstly, output feedback is considered in this paper.…”

Section: Remarkmentioning

confidence: 99%

“…Predictive control is a good method with the ability to handle constraints and time-delays [19][20][21][22][23]. Now, it has become one of the most popular control methodologies no matter in theory or the reality (see [24][25][26][27]). The NN predictive control for nonlinear dynamic systems with input delay was studied in [24], but the considered predictive model is required for linear ones and this condition is removed in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…The predictor-based control algorithm for an uncertain input delay Euler-Lagrange system was studied in [26], but the controller is an iteration form. To overcome the problem of input delay in Euler-Lagrange dynamical systems directly, a predictor with uncertain system dynamics was proposed in [27]. Recently, predictive control has been applied in many kinds of practical systems [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Predictive Control for Autonomous Underwater Vehicle with Input Delay

Zhao

2018

Journal of Control Science and Engineering

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A path tracking controller is designed for an autonomous underwater vehicle (AUV) with input delay based on neural network (NN) predictive control algorithm. To compensate for the time-delay in control system and realize the purpose of path tracking, a predictive control algorithm is proposed. An NN is used to estimate the nonlinear uncertainty of AUV induced by hydrodynamic coefficients and the coupling of the surge, sway, and yaw angular velocity. By Lyapunov theorem, stability analysis is also given. Simulation results show the effectiveness of the proposed control strategy.

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Saturated control of an uncertain nonlinear system with input delay

Cited by 90 publications

References 26 publications

Predictor-Feedback Stabilization of Multi-Input Nonlinear Systems

Predictor-Feedback Stabilization of Multi-Input Nonlinear Systems

Neural Network Based Central Heating System Load Prediction and Constrained Control

Neural Network Predictive Control for Autonomous Underwater Vehicle with Input Delay

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