Reach Control on Simplices by Continuous State Feedback

2010 ◽  
Vol 48 (5) ◽  
pp. 3482-3500 ◽  
Author(s):  
Mireille E. Broucke
Keyword(s):  
State Feedback ◽  
Continuous State ◽  
Reach Control

scholarly journals Long-latency reflexes for inter-effector coordination reflect a continuous state feedback controller

2018 ◽  
Vol 120 (5) ◽  
pp. 2466-2483 ◽  
Author(s):  
Frederic Crevecoeur ◽  
Isaac Kurtzer
Keyword(s):  
Feedback Control ◽  
State Feedback ◽  
Feedback Controller ◽  
State Feedback Control ◽  
Comprehensive Treatment ◽  
State Feedback Controller ◽  
Long Latency ◽  
Continuous State ◽  
Long Latency Reflex ◽  
Long Latency Reflexes

Successful performance in many everyday tasks requires compensating unexpected mechanical disturbance to our limbs and body. The long-latency reflex plays an important role in this process because it is the fastest response to integrate sensory information across several effectors, like when linking the elbow and shoulder or the arm and body. Despite the dozens of studies on inter-effector long-latency reflexes, there has not been a comprehensive treatment of how these reveal the basic control organization that sets constraints on any candidate model of neural feedback control such as optimal feedback control. We considered three contrasting ways that controllers can be organized: multiple independent controllers vs. a multiple-input multiple-output (MIMO) controller, a continuous feedback controller vs. an intermittent feedback controller, and a direct MIMO controller vs. a state feedback controller. Following a primer on control theory and review of the relevant evidence, we conclude that continuous state feedback control best describes the organization of inter-effector coordination by the long-latency reflex.

scholarly journals Exponential stabilization of spin-1/2 systems based on switching state feedback

2021 ◽  
Author(s):  
Jie Wen ◽  
Yuanhao Shi ◽  
Jianfang Jia ◽  
Jianchao Zeng
Keyword(s):  
State Feedback ◽  
Control Parameter ◽  
Exponential Convergence ◽  
Quantum Spin ◽  
The State ◽  
Exponential Stabilization ◽  
Feedback Controls ◽  
Feedback Strategy ◽  
Continuous State ◽  
Switching State

The exponential stabilization of eigenstates by using switching state feedback strategy for quantum spin-$\frac{1}{2}$ systems is considered in this paper. In order to obtain faster state exponential convergence, we divide the state space into two subspaces, and use two different continuous state feedback controls in the corresponding subspace. The two continuous state feedback controls form the switching state feedback, under which the state convergence is faster than that under continuous state feedback. The exponential convergence and the superiority of switching state feedback are proved in theory and verified in numerical simulations. Besides, the influence of the control parameter on the state convergence rate is also studied.

scholarly journals Learning Output Reference Model Tracking for Higher-Order Nonlinear Systems with Unknown Dynamics

Algorithms ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 121 ◽  
Author(s):  
Mircea-Bogdan Radac ◽  
Timotei Lala
Keyword(s):  
Nonlinear Systems ◽  
State Feedback ◽  
Reference Model ◽  
The State ◽  
State Transitions ◽  
Practical Implementation ◽  
General Function ◽  
State Action ◽  
Model Free ◽  
Continuous State

This work suggests a solution for the output reference model (ORM) tracking control problem, based on approximate dynamic programming. General nonlinear systems are included in a control system (CS) and subjected to state feedback. By linear ORM selection, indirect CS feedback linearization is obtained, leading to favorable linear behavior of the CS. The Value Iteration (VI) algorithm ensures model-free nonlinear state feedback controller learning, without relying on the process dynamics. From linear to nonlinear parameterizations, a reliable approximate VI implementation in continuous state-action spaces depends on several key parameters such as problem dimension, exploration of the state-action space, the state-transitions dataset size, and a suitable selection of the function approximators. Herein, we find that, given a transition sample dataset and a general linear parameterization of the Q-function, the ORM tracking performance obtained with an approximate VI scheme can reach the performance level of a more general implementation using neural networks (NNs). Although the NN-based implementation takes more time to learn due to its higher complexity (more parameters), it is less sensitive to exploration settings, number of transition samples, and to the selected hyper-parameters, hence it is recommending as the de facto practical implementation. Contributions of this work include the following: VI convergence is guaranteed under general function approximators; a case study for a low-order linear system in order to generalize the more complex ORM tracking validation on a real-world nonlinear multivariable aerodynamic process; comparisons with an offline deep deterministic policy gradient solution; implementation details and further discussions on the obtained results.

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