TY - JOUR N2 - As nonlinear optimization techniques are computationally expensive, their usage in the real-time era is constrained. So this is the main challenge for researchers to develop a fast algorithm that is used in real-time computations. This work proposes a fast nonlinear model predictive control approach based on particle swarm optimization for nonlinear optimization with constraints. The suggested algorithm divide and conquer technique improves computing speed and disturbance rejection capability, demonstrating its suitability for real-time applications. The performance of this approach under constraints is validated using a highly nonlinear fast and dynamic real-time inverted pendulum system. The solution presented through work is computationally feasible for smaller sampling times and it gives promising results compared to the state of art PSO algorithm L1 - http://journals.pan.pl/Content/123303/PDF/2335_BPASTS_2022_70_4.pdf L2 - http://journals.pan.pl/Content/123303 PY - 2022 IS - 4 EP - e140696 DO - 10.24425/bpasts.2022.140696 KW - nonlinear model predictive control KW - particle swarm optimization KW - fast dynamic systems KW - rotary inverted pendulum KW - divide and conquer approach A1 - Diwan, Supriya P. A1 - Deshpande, Shraddha S. VL - 70 DA - 06.06.2022 T1 - Computationally efficient nonlinear model predictive controller using parallel particle swarm optimization SP - e140696 UR - http://journals.pan.pl/dlibra/publication/edition/123303 T2 - Bulletin of the Polish Academy of Sciences Technical Sciences ER -