5. The computer-implemented parallel kinematic machine trajectory planning method according to claim 4 , wherein said step of performing fuzzification of the crisp inputs into linguistic variables further comprises the step of defining said Gaussian functions by the relation: μ A t j ⁡ ( x t ) = exp ⁢ - 1 2 ⁢ { ( x t - a t j ) 2 ( b t j ) 2 } , t = 1 , … ⁢ , 4 , j = 1 , … ⁢ , J , where a i j and b i j are a mean and standard deviation of the j th membership function of the input variable x i , and c t j are fuzzy sets defining the consequence of the j th rule such that: 
 c t j =f −1 (μ A l j ( x t )), f: →[ 0,1 ],t= 1, . . . ,3 ,j= 1 , . . . J wherein f is a continuous strong monotone function defined as f ⁡ ( z ) = 1 1 + ⅇ - ( ρ ⁢ ⁢ z - σ ) , where ρ and σ are real numbers affecting the position and slope of the inflection point of f.
4. The computer-implemented parallel kinematic machine trajectory planning method according to claim 1 , wherein the step of performing the Tsukamoto neuro-fuzzy inference procedure further comprises the steps of: characterizing a crisp end effector (EE) position and velocity utilizing a 4-input, first neural layer; performing fuzzification of the crisp inputs into linguistic variables through Gaussian transfer functions utilizing a second neural layer; applying a product T-norm to produce firing strengths of each rule utilizing a third rule-defining neural layer; normalizing firing strengths by calculating a ratio of each rule's firing strength to a sum of all rules' firing strengths, the normalizing firing strengths occurring in a fourth neural layer; inverting results of the firing strength normalizing step in a fifth neural layer; and aggregating and defuzzifying results from the inverting step, the aggregating and defuzzifying occurring in a sixth neural layer providing crisp outputs including joint torques and sampling periods.