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Safety Filter for Robust Disturbance Rejection via Online Optimization
Joyce Lai, Peter Seiler
Disturbance rejection in high-precision control applications can be significantly improved upon via online convex optimization (OCO). This includes classical techniques such as rec…
Stability Margins of Neural Network Controllers
Neelay Junnarkar, Murat Arcak, Peter Seiler
We present a method to train neural network controllers with guaranteed stability margins. The method is applicable to linear time-invariant plants interconnected with uncertaintie…
Robust Online Convex Optimization for Disturbance Rejection
Joyce Lai, Peter Seiler
Online convex optimization (OCO) is a powerful tool for learning sequential data, making it ideal for high precision control applications where the disturbances are arbitrary and u…
Synthesizing Neural Network Controllers with Closed-Loop Dissipativity Guarantees
Neelay Junnarkar, Murat Arcak, Peter Seiler
This paper presents a method to synthesize neural network controllers to maximize reward subject to the hard constraint that the feedback system of plant and controller be dissipat…