scholarly journals Quantization-Mitigation-Based Trajectory Control for Euler-Lagrange Systems with Unknown Actuator Dynamics

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3974
Author(s):  
Yi Lyu ◽  
Qiyu Yang ◽  
Patrik Kolaric
Keyword(s):  
Adaptive Control ◽  
Control Problem ◽  
Control Method ◽  
Direct Method ◽  
Trajectory Control ◽  
Time Varying ◽  
Actuator Dynamics ◽  
Lyapunov’S Direct Method ◽  
Pass Through

In this paper, we investigate a trajectory control problem for Euler-Lagrange systems with unknown quantization on the actuator channel. To address such a challenge, we proposed a quantization-mitigation-based trajectory control method, wherein adaptive control is employed to handle the time-varying input coefficients. We allow the quantized signal to pass through unknown actuator dynamics, which results in the coupled actuator dynamics for Euler-Lagrange systems. It is seen that our method is capable of driving the states of networked Euler-Lagrange systems to the desired ones via Lyapunov’s direct method. In addition, the effectiveness and advantage of our method are validated with a comparison to the existing controller.

Adaptive Control for Time Varying Systems with Actuator Dynamics and Sensor Noise

2022 ◽  
Author(s):  
John K. Zelina ◽  
Kadriye Merve Dogan ◽  
Richard J. Prazenica ◽  
Troy Henderson
Keyword(s):  
Adaptive Control ◽  
Time Varying ◽  
Sensor Noise ◽  
Actuator Dynamics ◽  
Time Varying Systems

scholarly journals Data-Driven Model-Free Adaptive Control Based on Error Minimized Regularized Online Sequential Extreme Learning Machine

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3241 ◽  
Author(s):  
Xiaofei Zhang ◽  
Hongbin Ma
Keyword(s):  
Adaptive Control ◽  
Extreme Learning Machine ◽  
Discrete Time ◽  
Control Method ◽  
Time Varying ◽  
Varying Parameters ◽  
Model Free ◽  
Time Varying Parameters ◽  
Learning Machine ◽  
Linearized Model

Model-free adaptive control (MFAC) builds a virtual equivalent dynamic linearized model by using a dynamic linearization technique. The virtual equivalent dynamic linearized model contains some time-varying parameters, time-varying parameters usually include high nonlinearity implicitly, and the performance will degrade if the nonlinearity of these time-varying parameters is high. In this paper, first, a novel learning algorithm named error minimized regularized online sequential extreme learning machine (EMREOS-ELM) is investigated. Second, EMREOS-ELM is used to estimate those time-varying parameters, a model-free adaptive control method based on EMREOS-ELM is introduced for single-input single-output unknown discrete-time nonlinear systems, and the stability of the proposed algorithm is guaranteed by theoretical analysis. Finally, the proposed algorithm is compared with five other control algorithms for an unknown discrete-time nonlinear system, and simulation results show that the proposed algorithm can improve the performance of control systems.

scholarly journals Entropy Analysis and Neural Network-Based Adaptive Control of a Non-Equilibrium Four-Dimensional Chaotic System with Hidden Attractors

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 156 ◽  
Author(s):  
Hadi Jahanshahi ◽  
Maryam Shahriari-Kahkeshi ◽  
Raúl Alcaraz ◽  
Xiong Wang ◽  
Vijay Singh ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Chaotic System ◽  
Adaptive Learning ◽  
Control Method ◽  
Direct Method ◽  
Dynamical Behavior ◽  
Hidden Attractors ◽  
Uncertain Dynamics ◽  
Non Equilibrium

Today, four-dimensional chaotic systems are attracting considerable attention because of their special characteristics. This paper presents a non-equilibrium four-dimensional chaotic system with hidden attractors and investigates its dynamical behavior using a bifurcation diagram, as well as three well-known entropy measures, such as approximate entropy, sample entropy, and Fuzzy entropy. In order to stabilize the proposed chaotic system, an adaptive radial-basis function neural network (RBF-NN)–based control method is proposed to represent the model of the uncertain nonlinear dynamics of the system. The Lyapunov direct method-based stability analysis of the proposed approach guarantees that all of the closed-loop signals are semi-globally uniformly ultimately bounded. Also, adaptive learning laws are proposed to tune the weight coefficients of the RBF-NN. The proposed adaptive control approach requires neither the prior information about the uncertain dynamics nor the parameters value of the considered system. Results of simulation validate the performance of the proposed control method.

On Robustness of Concurrent Learning Adaptive Control to Time-Varying Disturbances and System Uncertainties

2017 ◽  
Author(s):  
S. Burak Sarsilmaz ◽  
A. Turker Kutay ◽  
Tansel Yucelen
Keyword(s):  
Adaptive Control ◽  
Direct Method ◽  
Time Varying ◽  
Closed Loop System ◽  
Control Approach ◽  
Concurrent Learning ◽  
Adaptive Law ◽  
System Uncertainties ◽  
Model Reference Adaptive ◽  
Ultimate Bound

In this paper, we study the robustness characteristics of a recently developed concurrent learning model reference adaptive control approach to time-varying disturbances and system uncertainties. Specifically, the commonly-used constant (or slowly time-varying) assumption on disturbances and system uncertainties for this particular adaptive control approach is replaced with its bounded counterpart with piecewise continuous and bounded derivatives. Based on the Lyapunov’s direct method, we then show that the solutions of the closed-loop system are uniformly ultimately bounded, without requiring a modification term in the adaptive law. Estimates for the ultimate bound and the exponential convergence rate to that ultimate bound are further provided. According to these estimates and illustrative numerical examples, similarities and differences between concurrent learning and one of the well-known robustness modifications in adaptive control, namely σ modification, are explored.

scholarly journals Manipulator Trajectory Tracking with a Neural Network Adaptive Control Method

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wenbin Zha ◽  
Hui Zhang ◽  
Xiangrong Xu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Method ◽  
Angular Displacement ◽  
Angular Acceleration ◽  
Control Law ◽  
Time Varying ◽  
Dynamics Model ◽  
The Neural Network ◽  
Input Torque

In order to solve the joint chattering problem of the manipulator in the process of motion, a novel dynamics model is established based on the dynamics model of the manipulator, by fitting parameters of the neural network and combining with the estimated value of the inertia matrix. We proposed a neural network adaptive control method with a time-varying constraint state based on the dynamics model of estimation. We design the control law, establish the Lyapunov function equation and the asymmetric term, and derive the convergence of the control law. According to the joint state tracking results of the manipulator, the angular displacement, angular velocity, angular acceleration, input torque, and disturbance fitting of the manipulator are analyzed by using the Simulink and Gazebo. The simulation results show that the proposed method can effectively suppress the chattering amplitude under the environment disturbances.

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