Periodic event-triggered robust output feedback control for nonlinear uncertain systems with time-varying disturbance

Summary Multiple degrees‐of‐freedom (multi‐DOF) cell orientation control is a vital important technique involved in single cell surgery applications. Currently, few studies have been performed toward automation of multi‐DOF cell orientation control using robotically controlled optical tweezers. In this paper, a robust control framework is developed to perform multi‐DOF cell rotational control with consideration of model uncertainties and external disturbances. Both simulation and experimental studies are presented to illustrate the performance of the proposed control strategy. The main contributions of this work lie in that this is the first time to develop a unified framework to achieve multi‐DOF cell orientation control without the need for accurate dynamic model parameters and/or any knowledge about uncertainty characteristic, which greatly enhances the robustness of the overall system.

“…Based on the results reported in other works, [28][29][30][31][32][33] the uncertainties T d are approximately estimated byT d with the expressionT…”

Section: Ude-based Feedback Linearized Controllermentioning

confidence: 98%

“…Based on the results reported in other works, the uncertainties T d are approximately estimated by

{\overset{T}{true}}_{d}

with the expression

{\overset{T}{true}}_{d} = G false(s false) ((), \overset{θ}{true} - v - T_{d}^{'}),

where

G false(s false) = diag ({}, G_{1} false(s false), G_{2} false(s false), G_{3} false(s false))

is a transfer function matrix of the first‐order low‐pass filter, and

G_{i} false(s false) = \frac{1}{1 + T_{i} s}, i = 1, 2, 3 .

…”

Section: Robust Controller Design and Stability Analysismentioning

confidence: 99%

Robust orientation control of multi‐DOF cell based on uncertainty and disturbance estimation

Xie

Shakoor

et al. 2019

“…Note that we can determine the value of as desired, which influences the convergence property of̃(t) according to (21).…”

Section: Key Information Observermentioning

confidence: 99%

“…Periodic disturbances are commonly encountered in various control engineering applications, [1][2][3] where the rejection of such disturbances attracted significant research efforts in past years and is still an active area to explore to date. To reject or compensate periodic disturbances for different applications, many theoretical results have been developed in the literatures for reference, such as the internal model based methods, [4][5][6] the adaptive methods, [7][8][9][10][11] active disturbance-rejection control rejection control, [12][13][14] disturbance-observer-based control (DOBC), [15][16][17][18][19][20][21][22][23] and other techniques. 24,25 Apparently, it is desirable to compensate the disturbances if the disturbance parameters can be obtained and utilized to construct compensation signal, such that less conservative control can be deployed to achieve high precision performance.…”

Section: Introductionmentioning

confidence: 99%

Disturbance‐prediction–based control of input time delay systems for rejection of unknown frequency disturbances

Yan

2019

Summary In this paper, a novel disturbance rejection approach is presented for a class of input time‐delay systems subject to sinusoidal disturbances with unknown frequency. In particular, an auxiliary observer is proposed to represent the periodic disturbance in a parametric uncertainty form, where the unknown factor related to disturbance frequency can be estimated. Furthermore, the correlation between the future disturbance and the auxiliary observer output is analyzed, such that the future disturbances can be predicted and rejected through the input channel. Based on the aforementioned observer and predictor structure, the overall control architecture can be established as a framework of disturbance‐prediction–based control for systems with input time delays, where the conditions on the asymptotic stability of the closed‐loop systems are also derived. Finally, numerical examples are provided to illustrate the effectiveness of the proposed control approach.

“…The SMC law (6) is called an adaptive scheme because the time-varying item u a (t) is employed. By the two key steps, namely, (1) establishing a direct relationship between the disturbance (t) and u a (t), and (2) guaranteeing u a (t) to be exponentially reduced down, the negative influence of (t) may be reduced by the SMC law (6).…”

Section: Sliding-mode Controlmentioning

confidence: 99%

Chattering‐free adaptive sliding mode control for continuous‐time systems with time‐varying delay and process disturbance

Cui

2019

This paper concerns the controller design for continuous-time linear systems with time-varying delay and process disturbance. A novel adaptive sliding mode control law is mainly proposed to attract the sliding mode to first-order sliding surface within a finite time; afterwards, the uniformly ultimately bounded stability of the closed-loop system on the sliding surface is simultaneously guaranteed. In addition, the chattering phenomena can be conveniently excluded if the disturbance is a low-intensity process. Once the high-intensity disturbance is involved, the state variation can be significantly reduced as well. Furthermore, by the technique of a novel exponential free-matrix technique, the convergence rate of the closed-loop system can be conveniently preregulated. Numerical example is provided to demonstrate the effectiveness of the proposed method. KEYWORDSadaptive sliding mode control, chattering free, process disturbance, time-varying delay, uniformly ultimately bounded stable INTRODUCTIONMany real-world applications are difficult to be accurately described by the mathematical models because their full-scale intrinsic and extrinsic dynamics are usually hard or even barely impossible to be acquired. The practical systems usually contain some unclear system variations including mismatches between idealized model with the reality, unmodeled system dynamic, unknown operating mechanism, etc. If these system variations are completely neglected, the system control performances will be deteriorated; moreover, the system stability may be threatened. For describing the unknown and unmeasurable variations, the process disturbances are usually employed in the system dynamic models. [1][2][3][4][5] Although the detailed origination and transmutation dynamics of process disturbances still remain unrevealed, some statistical models are successful to describe their macroscopically principles. For example, the disturbances are regarded as the stochastic distributions, 6,7 the energy-bounded processes, 8-11 the peak-bounded procedures, 12-14 etc. For alleviating the negative impacts of the process disturbances, many robust control schemes are gradually proposed in the past decades. [15][16][17][18][19][20] One of the successful robust control scheme is called the sliding mode control (SMC), also known as the variable structure control, which received intensive attentions due to its prominent features such as convenient to be performed, easy implementation, insensitivity to dynamic switching of system modes, reduction of the order of system state, and especially the robustness in the presence of external disturbances. [21][22][23][24] Int J Robust Nonlinear Control. 2019;29:3389-3404.wileyonlinelibrary.com/journal/rnc