Jun Ma

TL;DR: A dual-drive H-gantry design is converted to a constrained projection gradient-based optimization problem, which can be efficiently solved by direct computation of projection gradient and line search of optimal step length by formulating a constrained linear–quadratic optimization problem.

TL;DR: In this article, the authors investigated the optimal decentralized control problem in the presence of parameter uncertainties and showed that a set of stabilizing decentralized controller gains for the uncertain system is parameterized in a convex set through appropriate convex restriction and then an approximated conic optimization problem is constructed.

TL;DR: This work treats the integrated mechatronic design problem as a controller optimization problem with structural constraints as well as a class of optimal control problems, such as controller synthesis problem with prescribed sparsity pattern, decentralized control problem with/without structural constraints, etc.

TL;DR: A novel data-driven multiObjective optimization approach is proposed that is able to automatically estimate the gradient and Hessian purely based on the measured motion data; the multiobjective cost function is suitably designed to take into account both smooth and accurate trajectory tracking.

TL;DR: In this article, a variational Bayesian Gaussian mixture model (vBGMM) framework is employed to predict the future trajectory of moving obstacles, and then a trajectory generation framework is proposed which will efficiently and effectively address trajectory generation in the presence of the uncertainty within a prediction horizon.