A Robust Delay Compensator for Uncertain Nonlinear Systems With Unknown Time-Varying Input Delay

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Ashish Kumar Jain ◽  
Shubhendu Bhasin
Keyword(s):  
Stability Analysis ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Tracking Error ◽  
Input Delay ◽  
Control Law ◽  
Time Varying ◽  
Uncertain Nonlinear Systems ◽  
Uniformly Ultimately Bounded ◽  
Simulation Results

This paper proposes a robust compensator for a class of uncertain nonlinear systems subjected to unknown time-varying input delay. The proposed control law is based on the integral of past values of control and a novel filtered tracking error. Sufficient gain conditions dependent on the known bound of the delay are derived using a Lyapunov-based stability analysis, where Lyapunov–Krasovskii (LK) functionals are used to achieve a global uniformly ultimately bounded (GUUB) tracking result. Simulation results for a nonlinear system are used to evaluate the performance and robustness of the controller for different values of time-varying input delay.

Unknown time-varying input delay compensation for uncertain nonlinear systems

Automatica ◽  
2017 ◽  
Vol 76 ◽  
pp. 222-229 ◽  
Author(s):  
Serhat Obuz ◽  
Justin R. Klotz ◽  
Rushikesh Kamalapurkar ◽  
Warren Dixon
Keyword(s):  
Nonlinear Systems ◽  
Input Delay ◽  
Time Varying ◽  
Uncertain Nonlinear Systems ◽  
Delay Compensation

A novel global harmony search method based off-line tuning of RFNN for adaptive control of uncertain nonlinear systems

2015 ◽  
Vol 8 (1) ◽  
pp. 69-98 ◽  
Author(s):  
Fouad Allouani ◽  
Djamel Boukhetala ◽  
Fares Boudjema ◽  
Gao Xiao-Zhi
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Control Method ◽  
Harmony Search ◽  
Optimization Method ◽  
Uncertain Nonlinear Systems ◽  
Content Type ◽  
Supervisory Controller ◽  
Fuzzy Neural ◽  
Simulation Results

Purpose – The two main purposes of this paper are: first, the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search (GHS) which is a stochastic optimization algorithm recently developed, with the ant colony optimization (ACO) algorithm. Second, design of a new indirect adaptive recurrent fuzzy-neural controller (IARFNNC) for uncertain nonlinear systems using the developed optimization method (GHSACO) and the concept of the supervisory controller. Design/methodology/approach – The novel optimization method introduces a novel improvization process, which is different from that of the GHS in the following aspects: a modified harmony memory representation and conception. The use of a global random switching mechanism to monitor the choice between the ACO and GHS. An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the IARFNNC global structure. Findings – First, to analyze the performance of GHSACO method and shows its effectiveness, some benchmark functions with different dimensions are used. Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search (HS), GHS, improved HS (IHS) and conventional ACO algorithm. In addition, simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS, its variants, particle swarm optimization, and genetic algorithms applied to the same problem. Originality/value – The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS. The proposed control method is applicable to any uncertain nonlinear system belongs in the class of systems treated in this paper.

Robust Adaptive Control of Uncertain Nonlinear Systems With Time-Varying Input Delay

2017 ◽  
Author(s):  
W. X. Deng ◽  
J. Y. Yao
Keyword(s):  
Nonlinear Systems ◽  
Controller Design ◽  
Adaptive Controller ◽  
Robust Adaptive Control ◽  
Input Delay ◽  
Time Varying ◽  
Uncertain Nonlinear Systems ◽  
Bounded Disturbances ◽  
Robust Adaptive ◽  
Adaptive Feedforward

In this paper, a robust adaptive controller is proposed for a class of uncertain nonlinear systems subject to time-varying input delay, parametric uncertainties and additive bounded disturbances. The desired trajectory based adaptive feedforward technique and a predictor-like robust delay compensating term are integrated via backstepping in the controller design. The proposed controller theoretically ensures semi-global uniformly ultimately bounded tracking performance based on Lyapunov stability analysis by employing Lyapunov-Krasovskii (LK) functionals. Simulation results are obtained to illustrate the effectiveness of the proposed control strategy.

Observer-Based State Tracking Adaptive Controller for a Class of Non-Affine Nonlinear Systems: An Intelligent Approach

2012 ◽  
Vol 488-489 ◽  
pp. 1798-1802
Author(s):  
R. Ghasemi ◽  
M.B. Menhaj ◽  
B. Abdi
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Nonlinear Observer ◽  
Asymptotic Convergence ◽  
Affine Nonlinear Systems ◽  
State Tracking ◽  
Simulation Results ◽  
Intelligent Approach

This paper proposes a new method for designing both nonlinear observer and adaptive controller for a class of non-affine nonlinear systems with unknown functions of the system. The states of the nonlinear system are assumed to be unavailable for measurement. The merits of this paper is as: asymptotic convergence of the observer and tracking error to zero, boundedness of all signals involved, and robustness. The simulation results illustrate the promising performance of the proposed algorithm.

Modified integral action with time-varying forgetting function for tracking control

2019 ◽  
Vol 234 (6) ◽  
pp. 715-725
Author(s):  
Fernando Villegas ◽  
Rogelio Hecker ◽  
Miguel Peña
Keyword(s):  
Stability Analysis ◽  
Tracking Error ◽  
Control Law ◽  
Time Varying ◽  
Forgetting Factor ◽  
Robust Controller ◽  
Integral Action ◽  
Abrupt Changes ◽  
New Type ◽  
And Control

While the use of integral action in control is quite common, in part due to its benefits for output regulation, it can also be counterproductive when abrupt changes in disturbance occur during tracking. In order to mitigate its counterproductive effect while at the same time maintaining its advantages for regulation, this work proposes a new type of integral action, including a time-varying forgetting factor suited to the expected behavior of the disturbance during tracking. Also, Lyapunov stability techniques are used to derive general results aiming to reduce the complexity of stability analysis and control design when the proposed integral action is included in a control law. In particular, these results are used for stability analysis when the proposed integral action is implemented in a deterministic robust controller for a linear motor system. Furthermore, the controller is implemented in the corresponding experimental setup, resulting in an improvement on maximum tracking error of up to 32%.

Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Systems with Time-Varying Input Delays Using the Quadratic Functions

2018 ◽  
Vol 26 (01) ◽  
pp. 109-142 ◽  
Author(s):  
Hongyun Yue ◽  
Junmin Li ◽  
Jiarong Shi ◽  
Wei Yang
Keyword(s):  
Nonlinear Systems ◽  
Tracking Error ◽  
Adaptive Fuzzy Control ◽  
Design Procedure ◽  
Quadratic Functions ◽  
Input Delay ◽  
Stochastic Nonlinear Systems ◽  
Fourth Moment ◽  
Time Varying ◽  
Adaptive Fuzzy

In this paper, for the stochastic nonlinear systems the adaptive fuzzy tracking controllers are constructed by using the fuzzy logic systems (FLS) and the classical quadratic functions. Compared with the existing results for adaptive fuzzy control, the stochastic nonlinear systems investigated in this paper are much more complex since the systems not only have distributed state time-varying delays in the noise jamming intensity terms but also have the time-varying delays in the input signals. During the controller design procedure, through appropriate assumptions and a state transformation the system with time-varying input delay can be easily transformed into a system without input delay. The other main advantage is that quadratic functions are used as Lyapunov functions to analyze the stability of systems, other than the fourth moment approach proposed by H. Deng and M. Krstic, and the hyperbolic tangent functions are introduced to deal with the Hessian terms. The proposed adaptive fuzzy controller guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error can converge to a small residual set around the origin in the mean square sense.

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