30,986 research outputs found
Validation and Verification of Aircraft Control Software for Control Improvement
Validation and Verification are important processes used to ensure software safety and reliability. The Cooper-Harper Aircraft Handling Qualities Rating is one of the techniques developed and used by NASA researchers to verify and validate control systems for aircrafts. Using the Validation and Verification result of controller software to improve controller\u27s performance will be one of the main objectives of this process. Real user feedback will be used to tune PI controller in order for it to perform better. The Cooper-Harper Aircraft Handling Qualities Rating can be used to justify the performance of the improved system
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor
Neuromorphic computing is a new paradigm for design of both the computing
hardware and algorithms inspired by biological neural networks. The event-based
nature and the inherent parallelism make neuromorphic computing a promising
paradigm for building efficient neural network based architectures for control
of fast and agile robots. In this paper, we present a spiking neural network
architecture that uses sensory feedback to control rotational velocity of a
robotic vehicle. When the velocity reaches the target value, the mapping from
the target velocity of the vehicle to the correct motor command, both
represented in the spiking neural network on the neuromorphic device, is
autonomously stored on the device using on-chip plastic synaptic weights. We
validate the controller using a wheel motor of a miniature mobile vehicle and
inertia measurement unit as the sensory feedback and demonstrate online
learning of a simple 'inverse model' in a two-layer spiking neural network on
the neuromorphic chip. The prototype neuromorphic device that features 256
spiking neurons allows us to realise a simple proof of concept architecture for
the purely neuromorphic motor control and learning. The architecture can be
easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference
Learning an Approximate Model Predictive Controller with Guarantees
A supervised learning framework is proposed to approximate a model predictive
controller (MPC) with reduced computational complexity and guarantees on
stability and constraint satisfaction. The framework can be used for a wide
class of nonlinear systems. Any standard supervised learning technique (e.g.
neural networks) can be employed to approximate the MPC from samples. In order
to obtain closed-loop guarantees for the learned MPC, a robust MPC design is
combined with statistical learning bounds. The MPC design ensures robustness to
inaccurate inputs within given bounds, and Hoeffding's Inequality is used to
validate that the learned MPC satisfies these bounds with high confidence. The
result is a closed-loop statistical guarantee on stability and constraint
satisfaction for the learned MPC. The proposed learning-based MPC framework is
illustrated on a nonlinear benchmark problem, for which we learn a neural
network controller with guarantees.Comment: 6 pages, 3 figures, to appear in IEEE Control Systems Letter
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