Parallel Constrained Predictive Control based on the Improved Particle Swarm Optimization for Nonlinear Fast Dynamic Systems
Main Article Content
Abstract
As the nonlinear predictive control model (NMPC) has evolved so far, most studies are confined to the slow dynamic nonlinear method, the study difficulty for the general nonlinear systems is mainly derived from optimization algorithm analysis. In fact, most reality control systems are nonlinear and are likely to have limitations. This paper proposed the population selection based improved particle swarm optimization (PS-IPSO) to minimize the computational time of the NMPC algorithm. In the PS-IPSO, the population selection step based on the ranking of population accordance with _tness function evaluation is implemented.
Via simulation results, the improved algorithm's effectiveness is determined by applying it to the highly nonlinear fast dynamic single rotary inverted pendulum (SRIP)system. The solution presented in the paper is computationally feasible for smaller sampling times
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.