One of the critical open issues in the nonlinear model predictive control(NMPC)scheme is the computational burden associated with the solution of the optimization problem

since at every sampling time a nonlinear dynamic optimization problem must be solved in real-time.To alleviate the aforementioned problem

a new NMPC algorithm using the global orthogonal collocation method is proposed.Higher order interpolation polynomial is used to simultaneously discrete state variables and control variables over the optimization horizon and the original continuous dynamic optimization is transcribed to a nonlinear programming problem(NLP).The NLP problem has a fixed structure with certain computational advantages and can be solved by an appropriate numerical optimization algorithm.Taking full advantage of the features of the global orthogonal collocation

the proposed algorithm provides the potential to reduce the scale of NLP and thereby reduce computational burden efficiently

even it works with a long optimization horizon.The effectiveness of the proposed algorithm is demonstrated by its application to a continuous polymerization process.It is found the algorithm achieves a smooth transition for large-magnitude setpoint changes and behaves well in the presence of disturbances.