Robust Control of a Low-Cost Mobile Robot Using a Neural Network Uncertainty Compensator

2014 ◽  
Author(s):  
Aliasghar Arab ◽  
Jingang Yi ◽  
Mohammad Mahdi Fateh ◽  
Soroush Arabshahi
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Mobile Robot ◽  
Feedback Linearization ◽  
Low Cost ◽  
Control Design ◽  
External Disturbances ◽  
Model Free ◽  
Comparison Results ◽  
Modeling Uncertainties

This paper presents a robust control design for a low-cost mobile robot under modeling uncertainties and external disturbances. We use a radial basis function neural network (RBFNN) to estimate and compensate for the model uncertainties and external disturbances. The proposed control design is model-free with guaranteed stability and good path-following performance. The RBFNN weight regulation and adaptive gains are designed based on the Lypanov method. Simulation and experimental results illustrate the design and demonstrate the strength of the proposed control applied to a nonholonomic wheeled mobile robot driven by low-cost permanent magnet dc motors without shaft encoders. The comparison results between proposed control and feedback linearization control confirm the effective role of the compensator in terms of precision, simplicity of design and computations.

scholarly journals Wheeled mobile robot design with robustness properties

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401774525 ◽  
Author(s):  
Yung Yue Chen ◽  
Yung Hsiang Chen ◽  
Chiung Yau Huang
Keyword(s):  
Robust Control ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Control Design ◽  
Wheeled Mobile Robot ◽  
Tracking Problem ◽  
Wheeled Mobile Robots ◽  
Modeling Uncertainties ◽  
Nonlinear Robust

A trajectory tracking design for wheeled mobile robots is presented in this article. The design objective is to develop one nonlinear robust control law for the trajectory tracking problem of wheeled mobile robots in the presence of modeling uncertainties. The main contribution of this investigation is as follows. Under the effects of modeling uncertainties, an effective control design which can quickly converge tracking errors between the controlled wheeled mobile robot and the desired trajectory is derived mathematically. Generally, it is difficult to develop a nonlinear robust control design for the trajectory tracking problem of wheeled mobile robots due to the complexity and nonlinearity of the wheeled mobile robots’ dynamics. Fortunately, based on a series analysis for the tracking error dynamics of the controlled wheeled mobile robot, one promising solution is obtained. For verifying the trajectory tracking performance of this proposed method, two scenarios are utilized in the simulations and the practical tests.

Robust Control-Oriented Modeling of a Feedback-Linearized Powered Pediatric Lower-Limb Orthosis

2019 ◽  
Author(s):  
Curt A. Laubscher ◽  
Jerzy T. Sawicki
Keyword(s):  
Robust Control ◽  
Lower Limb ◽  
Feedback Linearization ◽  
Performance Criteria ◽  
Model Uncertainties ◽  
Control Techniques ◽  
Response Data ◽  
Modeling Uncertainties ◽  
Recorded Data ◽  
Linearized System

Abstract Linear robust control techniques such as μ-synthesis can be used to design controllers for linear systems to guarantee specified performance criteria in the presence of modeling uncertainties, disturbances, and sensor noise. However, these techniques are rather uncommon in robotics due to the nonlinear nature of the plant where direct application would require large model uncertainties and therefore may only create a satisfactory controller if using lenient performance criteria. The inclusion of feedback linearization can rectify this by effectively converting the plant from a nonlinear system to a linear one, resulting in smaller model uncertainties. This paper proposes the use of feedback linearization to enable the use of linear robust control techniques on nonlinear systems. This approach is applied to a provisional version of a powered pediatric lower-limb orthosis. Sine sweep experiments are conducted to determine frequency response data for the system with and without feedback linearization. Models are identified to match the recorded data using optimization for both cases. Uncertainties are manually applied such that they encapsulate the observed measurements. The amount of uncertainties in the two models are quantified and a comparison shows that the uncertainties in the feedback-linearized system are smaller than in the system without feedback linearization.

Disturbance observer–based fixed-time robust control for constrained air-breathing hypersonic vehicle

2021 ◽  
pp. 095965182110652
Author(s):  
Jicheng Liu ◽  
Ju Jiang ◽  
Chaojun Yu ◽  
Bing Han
Keyword(s):  
Robust Control ◽  
Feedback Linearization ◽  
Disturbance Observer ◽  
Control Method ◽  
Sliding Mode ◽  
Uniform Boundedness ◽  
Fixed Time ◽  
External Disturbances ◽  
Simulation Experiments ◽  
Sliding Surfaces

This article studies the fixed-time robust control problem for the longitudinal dynamics of hypersonic vehicles in the presence of parametric uncertainties, external disturbances and input constraints. First, the dynamic model is transformed into two fourth-order integral chain subsystems by feedback linearization technology. Four novel fast integrating sliding surfaces are designed for each subsystem to guarantee the fixed time convergence of the errors and the derivatives. The double power reaching law is investigated to accelerate the convergence of sliding surfaces. Furthermore, the fixed-time disturbance observer technique is applied to estimate the lumped disturbance precisely. A novel fixed-time anti-saturation auxiliary system is designed to tackle the saturation caused by constraints of actuators. Then the semi-global uniform boundedness of the closed-loop system in a fixed time is proved by Lyapunov’s stability theory. Finally, comparison simulation experiments with the existing higher order sliding mode control method are carried out to verify the proposed method’s effectiveness and superiority.

FULL-STATE TRACKING CONTROL OF A MOBILE ROBOT USING NEURAL NETWORKS

2005 ◽  
Vol 15 (05) ◽  
pp. 403-414 ◽  
Author(s):  
V. SREE KRISHNA CHAITANYA
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Layer Structure ◽  
Single Layer ◽  
Lyapunov Theory ◽  
System Stability ◽  
Robot Dynamics ◽  
The Neural Network ◽  
Comparison Results ◽  
Highly Nonlinear

In this paper a nonholonomic mobile robot with completely unknown dynamics is discussed. A mathematical model has been considered and an efficient neural network is developed, which ensures guaranteed tracking performance leading to stability of the system. The neural network assumes a single layer structure, by taking advantage of the robot regressor dynamics that expresses the highly nonlinear robot dynamics in a linear form in terms of the known and unknown robot dynamic parameters. No assumptions relating to the boundedness is placed on the unmodeled disturbances. It is capable of generating real-time smooth and continuous velocity control signals that drive the mobile robot to follow the desired trajectories. The proposed approach resolves speed jump problem existing in some previous tracking controllers. Further, this neural network does not require offline training procedures. Lyapunov theory has been used to prove system stability. The practicality and effectiveness of the proposed tracking controller are demonstrated by simulation and comparison results.

scholarly journals Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Xiangjian Chen ◽  
Di Li ◽  
Pingxin Wang ◽  
Xibei Yang ◽  
Hongmei Li
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Robust Control ◽  
Sliding Mode ◽  
Robotic Exoskeleton ◽  
Adaptive Sliding Mode ◽  
Model Free ◽  
Control Scheme ◽  
System Uncertainties ◽  
Adaptive Control Scheme

A new model-free adaptive robust control method has been proposed for the robotic exoskeleton, and the proposed control scheme depends only on the input and output data, which is different from model-based control algorithms that require exact dynamic model knowledge of the robotic exoskeleton. The dependence of the control algorithm on the prior knowledge of the robotic exoskeleton dynamics model is reduced, and the influence of the system uncertainties are compensated by using the model-free adaptive sliding mode controller based on data-driven methodology and neural network estimator, which improves the robustness of the system. Finally, real-time experimental results show that the control scheme proposed in this paper achieves better control performances with good robustness with respect to system uncertainties and external wind disturbances compared with the model-free adaptive control scheme and model-free sliding mode adaptive control scheme.

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