1. A computer-implemented parallel kinematic machine trajectory planning method, comprising the steps of: (a) selecting new trajectory parameters from an offline dataset having a plurality of training trajectories; (b) performing a trajectory initialization; (c) formulating, off-line, a mathematical description of a constrained optimization problem for trajectory planning of a parallel kinematic machine (PKM), the mathematical description including: a cost minimization function and augmented Lagrange multipliers utilized in an augmented Lagrange decoupling procedure (ALD) to transform the problem into a non-constrained problem; an inertia matrix invertibility condition control law specification; a discrete dynamic model approximation using a multi-step Adams predictive-corrective formulation initialized with a fourth order Runge-Kutta; and a stopping criteria, wherein an iterative solution to the optimization problem describes at least one optimized trajectory; (d) saving the at least one optimized trajectory; (e) initializing membership function parameters defining fuzzy sets and number of rules for a Tsukamoto neuro-fuzzy inference procedure; (f) optimizing the membership function parameters for inputs extracted from the at least one optimized trajectory and outputs of the Tsukamoto neuro-fuzzy inference procedure; (g) performing on-line the Tsukamoto neuro-fuzzy inference procedure to further optimize the trajectory of the PKM, the on-line Tsukamoto neuro-fuzzy inference procedure accepting Cartesian 2-dimensional positions and velocities as fuzzy inputs, and outputting joint torques and sampling periods representing further optimization of the at least one optimized trajectory; and (h) transferring to the parallel kinetic machine a resultant trajectory planning program based on the further optimization; whereby, the PKM executes motion consistent with the further optimization of the at least one optimized trajectory so that time and energy of the PKM motion is optimally minimized.