18,366 research outputs found
Body randomization reduces the sim-to-real gap for compliant quadruped locomotion
Designing controllers for compliant, underactuated robots is challenging and usually requires a learning procedure. Learning robotic control in simulated environments can speed up the process whilst lowering risk of physical damage. Since perfect simulations are unfeasible, several techniques are used to improve transfer to the real world. Here, we investigate the impact of randomizing body parameters during learning of CPG controllers in simulation. The controllers are evaluated on our physical quadruped robot. We find that body randomization in simulation increases chances of finding gaits that function well on the real robot
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
Information-theoretic approach to the study of control systems
We propose an information-theoretic framework for analyzing control systems
based on the close relationship of controllers to communication channels. A
communication channel takes an input state and transforms it into an output
state. A controller, similarly, takes the initial state of a system to be
controlled and transforms it into a target state. In this sense, a controller
can be thought of as an actuation channel that acts on inputs to produce
desired outputs. In this transformation process, two different control
strategies can be adopted: (i) the controller applies an actuation dynamics
that is independent of the state of the system to be controlled (open-loop
control); or (ii) the controller enacts an actuation dynamics that is based on
some information about the state of the controlled system (closed-loop
control). Using this communication channel model of control, we provide
necessary and sufficient conditions for a system to be perfectly controllable
and perfectly observable in terms of information and entropy. In addition, we
derive a quantitative trade-off between the amount of information gathered by a
closed-loop controller and its relative performance advantage over an open-loop
controller in stabilizing a system. This work supplements earlier results [H.
Touchette, S. Lloyd, Phys. Rev. Lett. 84, 1156 (2000)] by providing new
derivations of the advantage afforded by closed-loop control and by proposing
an information-based optimality criterion for control systems. New applications
of this approach pertaining to proportional controllers, and the control of
chaotic maps are also presented.Comment: 18 pages, 7 eps figure
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