Continuous State Feedback Control Based on Intelligent Optimization for First-Order Nonholonomic Systems

2020 ◽  
Vol 50 (7) ◽  
pp. 2534-2540 ◽  
Author(s):  
Xuzhi Lai ◽  
Pan Zhang ◽  
Yawu Wang ◽  
Luefeng Chen ◽  
Min Wu
2018 ◽  
Vol 120 (5) ◽  
pp. 2466-2483 ◽  
Author(s):  
Frederic Crevecoeur ◽  
Isaac Kurtzer

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.


2008 ◽  
Vol 19 (10) ◽  
pp. 1477-1494 ◽  
Author(s):  
GAMAL M. MAHMOUD ◽  
MANSOUR E. AHMED ◽  
EMAD E. MAHMOUD

This paper introduces and analyzes new hyperchaotic complex Lorenz systems. These systems are 6-dimensional systems of real first order autonomous differential equations and their dynamics are very complicated and rich. In this study we extend the idea of adding state feedback control and introduce the complex periodic forces to generate hyperchaotic behaviors. The fractional Lyapunov dimension of the hyperchaotic attractors of these systems is calculated. Bifurcation analysis is used to demonstrate chaotic and hyperchaotic behaviors of our new systems. Dynamical systems where the main variables are complex appear in many important fields of physics and communications.


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