scholarly journals Event-Triggered Compound Learning Tracking Control of Nonstrict-Feedback Nonlinear Systems in Sensor-to-Controller Channel

2021 ◽  
Author(s):  
Yingjie Deng ◽  
Tao Ni ◽  
Jiantao Wang
Keyword(s):  
Tracking Control ◽  
Learning Algorithm ◽  
Approximation Error ◽  
Learning Performance ◽  
Time Varying ◽  
Adaptive Model ◽  
Control Laws ◽  
Virtual Control ◽  
Control Scheme ◽  
Event Triggered

Abstract This paper investigates the event-triggered tracking control of the nonstrict-feedback nonlinear system with the time-varying disturbances. While the fuzzy logic systems (FLSs) serve as the approximators to the unknown dynamics, the compound disturbance is comprised of the time-varying disturbance and the approximation error of the FLS. An event-triggered compound learning algorithm is originally developed to accurately estimate the total uncertainties. By referring to an event-triggered adaptive model, the control laws are derived without provoking the problem of "algebraic loop", seeing Remark 3. The command filters are employed to generate the continuous substitutes for both the virtual control laws and their derivatives, so as to solve the recently proposed problem of "jumps of virtual control laws" arising in the backstepping-based event-triggered control (ETC) that functions in the channel of sensor to controller. The triggering condition is constructed to guarantee the similarity between the adaptive model and the original system. While the satisfactory learning performance of the FLSs and the compound disturbances estimation are maintained, the proposed control scheme can guarantee the semi-globally uniformly ultimate boundedness (SGUUB) of all the tracking errors. Finally, a numerical experiment is carried out to exemplify the effectiveness of the proposed control scheme.

scholarly journals Formation Tracking Control and Obstacle Avoidance of Unicycle-Type Robots Guaranteeing Continuous Velocities

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4374
Author(s):  
Jose Bernardo Martinez ◽  
Hector M. Becerra ◽  
David Gomez-Gutierrez
Keyword(s):  
Obstacle Avoidance ◽  
Tracking Control ◽  
Global Coordinate System ◽  
Avoidance Task ◽  
Time Varying ◽  
Control Laws ◽  
Formation Tracking ◽  
Control Scheme ◽  
First Time ◽  
Hierarchical Scheme

In this paper, we addressed the problem of controlling the position of a group of unicycle-type robots to follow in formation a time-varying reference avoiding obstacles when needed. We propose a kinematic control scheme that, unlike existing methods, is able to simultaneously solve the both tasks involved in the problem, effectively combining control laws devoted to achieve formation tracking and obstacle avoidance. The main contributions of the paper are twofold: first, the advantages of the proposed approach are not all integrated in existing schemes, ours is fully distributed since the formulation is based on consensus including the leader as part of the formation, scalable for a large number of robots, generic to define a desired formation, and it does not require a global coordinate system or a map of the environment. Second, to the authors’ knowledge, it is the first time that a distributed formation tracking control is combined with obstacle avoidance to solve both tasks simultaneously using a hierarchical scheme, thus guaranteeing continuous robots velocities in spite of activation/deactivation of the obstacle avoidance task, and stability is proven even in the transition of tasks. The effectiveness of the approach is shown through simulations and experiments with real robots.

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

Event-triggered tracking control for nonlinear systems subject to time-varying external disturbances

Automatica ◽  
2020 ◽  
Vol 119 ◽  
pp. 109070
Author(s):  
Ting Li ◽  
Changyun Wen ◽  
Jun Yang ◽  
Shihua Li ◽  
Lei Guo
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Time Varying ◽  
External Disturbances ◽  
Event Triggered

scholarly journals Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1291
Author(s):  
Zhuan Shen ◽  
Fan Yang ◽  
Jing Chen ◽  
Jingxiang Zhang ◽  
Aihua Hu ◽  
...  
Keyword(s):  
Closed Loop ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Time Varying ◽  
Time Varying Delay ◽  
Control Scheme ◽  
The Stability ◽  
High Level ◽  
Varying Delay ◽  
Event Triggered

This paper investigates the problem of adaptive event-triggered synchronization for uncertain FNNs subject to double deception attacks and time-varying delay. During network transmission, a practical deception attack phenomenon in FNNs should be considered; that is, we investigated the situation in which the attack occurs via both communication channels, from S-C and from C-A simultaneously, rather than considering only one, as in many papers; and the double attacks are described by high-level Markov processes rather than simple random variables. To further reduce network load, an advanced AETS with an adaptive threshold coefficient was first used in FNNs to deal with deception attacks. Moreover, given the engineering background, uncertain parameters and time-varying delay were also considered, and a feedback control scheme was adopted. Based on the above, a unique closed-loop synchronization error system was constructed. Sufficient conditions that guarantee the stability of the closed-loop system are ensured by the Lyapunov-Krasovskii functional method. Finally, a numerical example is presented to verify the effectiveness of the proposed method.

scholarly journals Adaptive Fuzzy H∞ Robust Tracking Control for Nonlinear MIMO Systems

2015 ◽  
Vol 15 (1) ◽  
pp. 34-45
Author(s):  
Sanxiu Wang ◽  
Kexin Xing ◽  
Zhengchu Wang
Keyword(s):  
Tracking Control ◽  
Mimo Systems ◽  
Tracking Error ◽  
Approximation Error ◽  
Nonlinear Function ◽  
Robust Tracking ◽  
Robust Tracking Control ◽  
Fuzzy Logic Systems ◽  
Adaptive Fuzzy ◽  
Control Scheme

Abstract In this paper an adaptive fuzzy H∞ robust tracking control scheme is developed for a class of uncertain nonlinear Multi-Input and Multi-Output (MIMO) systems. Firstly, fuzzy logic systems are introduced to approximate the unknown nonlinear function of the system by an adaptive algorithm. Next, a H∞ robust compensator controller is employed to eliminate the effect of the approximation error and external disturbances. Consequently, a fuzzy adaptive robust controller is proposed, such that the tracking error of the resulting closed-loop system converges to zero and the tracking robustness performance can be guaranteed. The simulation results performed on a two-link robotic manipulator demonstrate the validity of the proposed control scheme.

scholarly journals Stochastically exponential synchronization for Markov jump neural networks with time-varying delays via event-triggered control scheme

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiaoman Liu ◽  
Haiyang Zhang ◽  
Jun Yang ◽  
Hao Chen
Keyword(s):  
Neural Networks ◽  
Convex Combination ◽  
Analytical Techniques ◽  
Exponential Synchronization ◽  
Time Varying ◽  
Markov Jump ◽  
Synchronization Problem ◽  
Control Scheme ◽  
Reciprocally Convex Combination ◽  
Event Triggered

AbstractThis paper focuses on the stochastically exponential synchronization problem for one class of neural networks with time-varying delays (TDs) and Markov jump parameters (MJPs). To derive a tighter bound of reciprocally convex quadratic terms, we provide an improved reciprocally convex combination inequality (RCCI), which includes some existing ones as its particular cases. We construct an eligible stochastic Lyapunov–Krasovskii functional to capture more information about TDs, triggering signals, and MJPs. Based on a well-designed event-triggered control scheme, we derive several novel stability criteria for the underlying systems by employing the new RCCI and other analytical techniques. Finally, we present two numerical examples to show the validity of our methods.

Sign in / Sign up

Export Citation Format

Share Document