scholarly journals A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Roni Hogri ◽  
Simeon A. Bamford ◽  
Aryeh H. Taub ◽  
Ari Magal ◽  
Paolo Del Giudice ◽  
...  
Keyword(s):  
Closed Loop ◽  
Learning Function ◽  
Model Based ◽  
Anesthetized Rats ◽  
Cerebellar Learning

scholarly journals Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators

2019 ◽  
Vol 35 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Thomas George Thuruthel ◽  
Egidio Falotico ◽  
Federico Renda ◽  
Cecilia Laschi
Keyword(s):  
Reinforcement Learning ◽  
Closed Loop ◽  
Dynamic Control ◽  
Robotic Manipulators ◽  
Model Based

scholarly journals Stabilization of the Magnetic Levitation System

2021 ◽  
Vol 11 (21) ◽  
pp. 10369
Author(s):  
Štefan Chamraz ◽  
Mikuláš Huba ◽  
Katarína Žáková
Keyword(s):  
Closed Loop ◽  
Magnetic Levitation ◽  
Internal Model Control ◽  
Pass Filter ◽  
Low Pass Filter ◽  
First Order ◽  
Model Based ◽  
Model Control ◽  
Levitation System ◽  
Magnetic Levitation System

This paper contributes toward research on the control of the magnetic levitation plant, representing a typical nonlinear unstable system that can be controlled by various methods. This paper shows two various approaches to the solution of the controller design based on different closed loop requirements. Starting from a known unstable linear plant model—the first method is based on the two-step procedure. In the first step, the transfer function of the controlled system is modified to get a stable non-oscillatory system. In the next step, the required first-order dynamic is defined and a model-based PI controller is proposed. The closed loop time constant of this first-order model-based approach can then be used as a tuning parameter. The second set of methods is based on a simplified ultra-local linear approximation of the plant dynamics by the double-integrator plus dead-time (DIPDT) model. Similar to the first method, one possible solution is to stabilize the system by a PD controller combined with a low-pass filter. To eliminate the offset, the stabilized system is supplemented by a simple static feedforward, or by a controller proposed by means of an internal model control (IMC). Another possible approach is to apply for the DIPDT model directly a stabilizing PID controller. The considered solutions are compared to the magnetic levitation system, controlled via the MATLAB/Simulink environment. It is shown that, all three controllers, with integral action, yield much slower dynamics than the stabilizing PD control, which gives one motivation to look for alternative ways of steady-state error compensation, guaranteeing faster setpoint step responses.

An Efficient Computational Algorithm of Adaptive Control for Closed-Loop Robots and Experiments

1998 ◽  
Vol 10 (2) ◽  
pp. 147-153
Author(s):  
Yasuhito Oooka ◽  
◽  
Haruhisa Kawasaki ◽  
Nobuhito Takemura
Keyword(s):  
Adaptive Control ◽  
Closed Loop ◽  
Computational Algorithm ◽  
Trajectory Control ◽  
Experimental Results ◽  
Serial Link ◽  
Model Based ◽  
Link Mechanism ◽  
Parallel Link

This paper presents an efficient computational algorithm of model-based adaptive control for closed-loop robots. The algorithm is an extension of the computational algorithm for serial-link robots, which was derived by Kawasaki and Bito. The proposed algorithm is implemented to a 6 DOF robot with a parallel-link mechanism using a 32-bit DSP. Experimental results of trajectory control are also shown.

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