The Multivariable Continuous-time Generalised Predictive Controller (CGPC) is recast in a state-space form and shown to include Generalised Minimum Variance (GMV) and an new algorithm, Predictive GMV (PGMV) as special cases. Comparisons are drawn with the exact linearisation methods of nonlinear control and it is noted that, unlike the transfer function approach, the state-space approach extend...

In this research, an intelligent multi-objective nonlinear model predictive control (NMPC) scheme is proposed for its application in the ‘on-line’ optimization of dynamical gas turbine model. The scheme proposed belongs to the sub-optimal NMPC strategies where near-optimal, instead of global optimal, control solutions are obtained at each control sampling time. The complexity of NMPC implementa...

In recent years, nonlinear model predictive control schemes have been derived that guarantee stability of the closed loop under the assumption of full state information. However, only limited advances have been made with respect to output feedback in connection to nonlinear predictive control. Most of the existing approaches for output feedback nonlinear model predictive control do only guarant...

This paper presents a stochastic nonlinear model predictive control technique for discrete-time uncertain nonlinear systems with particular focus on the batch polymerization reactor application. We consider a nonlinear dynamical system subject to chance constraints (i.e. need to be satisfied probabilistically up to a pre-assigned level). This formulation leads to a finite-horizon chance-constra...

A new technique named as model predictive spread acceleration guidance (MPSAG) is proposed in this paper. It combines nonlinear model predictive control and spread acceleration guidance philosophies. This technique is then used to design a nonlinear suboptimal guidance law for a constant speed missile against stationary target with impact angle constraint. MPSAG technique can be applied to a cl...

A method is developed for model predictive control of nonlinear stochastic partial differential equations (PDEs) to regulate the state variance, which physically represents the roughness of a surface in a thin film growth process, to a desired level. Initially a nonlinear stochastic PDE is formulated into a system of infinite nonlinear stochastic ordinary differential equations by using Galerki...

Laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. Therefore, it is widely adopted for complex industrial process control. In this work, Laguerre function based adaptive model predictive control algorithm (AMPC) was implemented to control a nonlinear process. Simula...

-A novel approach for the implementation of Nonlinear Model Predictive Control (NMPC) using Particle Swarm Optimization (PSO) technique is proposed. Two different approaches are made in the PSO algorithms, Random PSO (RPSO) and knowledge based PSO (KPSO) for the determination of optimum controller gain in MPC structure In order to test the performance of the proposed PSO based MPC system a nonl...

Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...

the present sought to draw a comparison between model predictive control performance and two other controllers named simple pi and selective pi in controlling large-scale natural gas transport networks. a nonlinear dynamic model of representative gas pipeline was derived from pipeline governing rules and simulated in simulink® environment of matlab®. control schemes were designed to provide a s...