Proceedings ArticleDOI
Finite-time robust control of robot manipulator: a SDDRE based approach
TLDR
A finite-time robust control law is proposed for a nonlinear, uncertain robot manipulator to ensure the stability analytically and numerically in the presence of bounded uncertainty.Abstract:
This paper proposes a finite-time robust control law for a nonlinear, uncertain robot manipulator. Load variations and unmodeled system dynamics of manipulator are the primary sources of uncertainties. The dynamics of the manipulator is modeled in State-Dependent Coefficient (SDC) form to consider all nonlinear term in system dynamics. To control such uncertain system a robust control law is essential. An optimal control approach is adopted to design the proposed robust control law. The control input is generated by solving a State-Dependent Differential Riccati Equation (SDDRE) in forward in time. Here the analytical solution of SDDRE is used to compute control law. The designed control law ensures the stability analytically and numerically in the presence of bounded uncertainty.read more
Citations
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Reinforcement Learning and Adaptive Dynamic Programming for Feedback Control
TL;DR: A transversal view through microfluidics theory and applications, covering different kinds of phenomena, from continuous to multiphase flow, and a vision of two phasemicrofluidic phenomena is given through nonlinear analyses applied to experimental time series.
Journal ArticleDOI
Fuzzy Finite-Time Command Filtering Output Feedback Control of Nonlinear Systems
TL;DR: In this article , a fast convergent output feedback control algorithm based on backstepping finite-time command filtering is developed for a class of nonlinear systems, where a fuzzy state observer is designed to measure the unknown state and a compensation mechanism is also introduced to compensate for the error caused by the filter.
Journal ArticleDOI
Rigid spacecraft robust optimal attitude stabilization under actuator misalignments
Zhong Wang,Yan Li +1 more
TL;DR: It is proved that the robust attitude stabilization problem under actuator misalignments and disturbances can be reformulated into the problem of solving the Hamilton-Jacobi-Bellman (HJB) equation, and the computational burden of implementing the controller is significantly reduced.
Journal ArticleDOI
Combination of terminal sliding mode and finite-time state-dependent Riccati equation: Flapping-wing flying robot control
TL;DR: In this article , a terminal sliding mode control is introduced to control a class of nonlinear uncertain systems in finite time, where the sliding surface of the introduced controller is equipped with a finite-time gain that finishes the control task in the desired predefined time.
Dissertation
Uncertainty modelling and motion planning of an inchworm robot navigating in complex structural environments
TL;DR: This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, as to provide real-time information about the response of the immune system to natural disasters.
References
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Roger A. Horn,Charles R. Johnson +1 more
TL;DR: In this article, the authors present results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrate their importance in a variety of applications, such as linear algebra and matrix theory.
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TL;DR: This book discusses Classical and Modern Control Optimization Optimal Control Historical Tour, Variational Calculus for Discrete-Time Systems, and more.
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Frank L. Lewis,Draguna Vrabie +1 more
TL;DR: This work describes mathematical formulations for reinforcement learning and a practical implementation method known as adaptive dynamic programming that give insight into the design of controllers for man-made engineered systems that both learn and exhibit optimal behavior.
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Robot Manipulator Control: Theory and Practice
TL;DR: This thoroughly up-to-date Second Edition of Robot Manipulator Control explicates theoretical and mathematical requisites for controls design and summarizes current techniques in computer simulation and implementation of controllers.
Journal ArticleDOI
Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers
TL;DR: In this article, the authors describe the use of reinforcement learning to design feedback controllers for discrete and continuous-time dynamical systems that combine features of adaptive control and optimal control, which are not usually designed to be optimal in the sense of minimizing user-prescribed performance functions.