scholarly journals Self-Learning Low-Level Controllers

2021 ◽  
Author(s):  
Dang Xuan Ba ◽  
Joonbum Bae
Keyword(s):  
Sliding Mode ◽  
Humanoid Robots ◽  
Nonlinear Functions ◽  
Low Level ◽  
Unstructured Environments ◽  
Learning Rules ◽  
Whole Systems ◽  
Self Learning ◽  
Control Criterion ◽  
Adaptation Ability

Humanoid robots are complicated systems both in hardware and software designs. Furthermore, the robots normally work in unstructured environments at which unpredictable disturbances could degrade control performances of whole systems. As a result, simple yet effective controllers are favorite employed in low-level layers. Gain-learning algorithms applied to conventional control frameworks, such as Proportional-Integral-Derivative, Sliding-mode, and Backstepping controllers, could be reasonable solutions. The adaptation ability integrated is adopted to automatically tune proper control gains subject to the optimal control criterion both in transient and steady-state phases. The learning rules could be realized by using analytical nonlinear functions. Their effectiveness and feasibility are carefully discussed by theoretical proofs and experimental discussion.

Robust stabilization control of a spatial inverted pendulum using integral sliding mode controller

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ishan Chawla ◽  
Vikram Chopra ◽  
Ashish Singla
Keyword(s):  
Inverted Pendulum ◽  
Sliding Mode ◽  
Robust Stabilization ◽  
Linear Quadratic Regulator ◽  
Humanoid Robots ◽  
Sliding Mode Controller ◽  
Linear Quadratic ◽  
Integral Sliding Mode ◽  
Wide Range ◽  
Lqr Controller

AbstractFrom the last few decades, inverted pendulums have become a benchmark problem in dynamics and control theory. Due to their inherit nature of nonlinearity, instability and underactuation, these are widely used to verify and implement emerging control techniques. Moreover, the dynamics of inverted pendulum systems resemble many real-world systems such as segways, humanoid robots etc. In the literature, a wide range of controllers had been tested on this problem, out of which, the most robust being the sliding mode controller while the most optimal being the linear quadratic regulator (LQR) controller. The former has a problem of non-robust reachability phase while the later lacks the property of robustness. To address these issues in both the controllers, this paper presents the novel implementation of integral sliding mode controller (ISMC) for stabilization of a spatial inverted pendulum (SIP), also known as an x-y-z inverted pendulum. The structure has three control inputs and five controlled outputs. Mathematical modeling of the system is done using Euler Lagrange approach. ISMC has an advantage of eliminating non-robust reachability phase along with enhancing the robustness of the nominal controller (LQR Controller). To validate the robustness of ISMC to matched uncertainties, an input disturbance is added to the nonlinear model of the system. Simulation results on two different case studies demonstrate that the proposed controller is more robust as compared to conventional LQR controller. Furthermore, the problem of chattering in the controller is dealt by smoothening the controller inputs to the system with insignificant loss in robustness.

Low-level Autonomy of the Humanoid Robots H6 & H7

2007 ◽  
pp. 83-97 ◽  
Author(s):  
Satoshi Kagami ◽  
Koichi Nishiwaki ◽  
James Kuffner ◽  
Kei Okada ◽  
Yasuo Kuniyoshi ◽  
...  
Keyword(s):  
Humanoid Robots ◽  
Low Level

Whole-Body Balancing Walk Controller for Position Controlled Humanoid Robots

2016 ◽  
Vol 13 (01) ◽  
pp. 1650011 ◽  
Author(s):  
Seung-Joon Yi ◽  
Byoung-Tak Zhang ◽  
Dennis Hong ◽  
Daniel D. Lee
Keyword(s):  
Hierarchical Control ◽  
Humanoid Robots ◽  
Whole Body ◽  
Appropriate Behavior ◽  
Low Level ◽  
Push Recovery ◽  
Hierarchical Control Architecture ◽  
High Level ◽  
Experimental Trials ◽  
Decision Boundaries

Bipedal humanoid robots are intrinsically unstable against unforeseen perturbations. Conventional zero moment point (ZMP)-based locomotion algorithms can reject perturbations by incorporating sensory feedback, but they are less effective than the dynamic full body behaviors humans exhibit when pushed. Recently, a number of biomechanically motivated push recovery behaviors have been proposed that can handle larger perturbations. However, these methods are based upon simplified and transparent dynamics of the robot, which makes it suboptimal to implement on common humanoid robots with local position-based controllers. To address this issue, we propose a hierarchical control architecture. Three low-level push recovery controllers are implemented for position controlled humanoid robots that replicate human recovery behaviors. These low-level controllers are integrated with a ZMP-based walk controller that is capable of generating reactive step motions. The high-level controller constructs empirical decision boundaries to choose the appropriate behavior based upon trajectory information gathered during experimental trials. Our approach is evaluated in physically realistic simulations and on a commercially available small humanoid robot.

scholarly journals Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller

Micromachines ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 968 ◽  
Author(s):  
Juntao Fei ◽  
Yunmei Fang ◽  
Zhuli Yuan
Keyword(s):  
Fuzzy Control ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Learning Ability ◽  
Fuzzy Sliding Mode Control ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy Sliding Mode ◽  
Fuzzy Sliding Mode ◽  
Self Learning ◽  
Fuzzy Parameters

This paper developed an adaptive backstepping fuzzy sliding control (ABFSC) approach for a micro gyroscope. Based on backstepping design, an adaptive fuzzy sliding mode control was proposed to adjust the fuzzy parameters with self-learning ability and reject the system nonlinearities. With the Lyapunov function analysis of error function and sliding surface function, a comprehensive controller is derived to ensure the stability of the proposed control system. The proposed fuzzy control scheme does not need to know the system model in advance and could approximate the system nonlinearities well. The adaptive fuzzy control method has self-learning ability to adjust the fuzzy parameters. Simulation studies were implemented to prove the validity of the proposed ABFSMC strategy, showing that it can adapt to the changes of external disturbance and model parameters and has a satisfactory performance in tracking and approximation.

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