  Piecewise Polynomials (PPs) are utilized in several engineering disciplines,
like trajectory planning, to approximate position profiles given in the form of
a set of points. While the approximation target along with domain-specific
requirements, like Ck -continuity, can be formulated as a system of equations
and a result can be computed directly, such closed-form solutions posses
limited flexibility with respect to polynomial degrees, polynomial bases or
adding further domain-specific requirements. Sufficiently complex optimization
goals soon call for the use of numerical methods, like gradient descent. Since
gradient descent lies at the heart of training Artificial Neural Networks
(ANNs), modern Machine Learning (ML) frameworks like TensorFlow come with a set
of gradient-based optimizers potentially suitable for a wide range of
optimization problems beyond the training task for ANNs. Our approach is to
utilize the versatility of PP models and combine it with the potential of
modern ML optimizers for the use in function approximation in 1D trajectory
planning in the context of electronic cam design. We utilize available
optimizers of the ML framework TensorFlow directly, outside of the scope of
ANNs, to optimize model parameters of our PP model. In this paper, we show how
an orthogonal polynomial basis contributes to improving approximation and
continuity optimization performance. Utilizing Chebyshev polynomials of the
first kind, we develop a novel regularization approach enabling clearly
improved convergence behavior. We show that, using this regularization
approach, Chebyshev basis performs better than power basis for all relevant
optimizers in the combined approximation and continuity optimization setting
and demonstrate usability of the presented approach within the electronic cam
domain.
