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This paper provides an overview of nonlinear model predictive control (NMPC) applications in industry, focusing primarily on recent applications reported by NMPC vendors. A brief summary of NMPC theory is presented to highlight issues pertinent to NMPC applications. Five industrial NMPC implementations are then discussed with reference to modeling, control, optimization, and implementation issues. Results from several industrial applications are presented to illustrate the benefits possible with NMPC technology. A discussion of future needs in NMPC theory and practice is provided to conclude the paper.
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Model-Predictive Control (MPC) is one of the most advanced control techniques nowadays. Indeed, MPC approaches are well known for their robustness and stability properties. Nevertheless, Nonlinear Model-Predictive Control (NMPC), the extension of MPC in the nonlinear world, still poses challenging theoretical, computational and implementation issues. By the help of validated simulation, which can handle nonlinear models, a new algorithm for a robust by-construction control strategy based on NMPC is proposed.
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This paper considers Nonlinear Model Predictive Control (NMPC) in the context of snake robot locomotion. NMPC is an optimal control technique that offers the ability of handling constraints although requiring relatively significant computational resources. In detail, we show how NMPC can be applied to achieve straight line path following control of snake robots. The paper presents simulation results which illustrate the performance of the proposed control approach. Moreover, we discuss advantages and disadvantages of NMPC as a control approach for snake robots.
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Nonlinear Model Predictive Control (NMPC) has great appeal for vehicle path control due to its ability to easily handle nonlinear dynamic models with constraints while achieving near-optimal control. The drawback is that a straight-forward application results in computations that take too long for real-time use. In this paper, we explore these issues in the use of NMPC for vehicle control. Methods for speeding up the computations are discussed.
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The theory and technology of model predictive control(MPC) have been developed rapidly in recent years. With the successful applications of model predictive control for linear systems, predictive control for nonlinear models(NMPC)has received wide attention and achieved rich results. Based on the fundamental principle and characteristics of NMPC, in this paper, the current hot issues in the field and the results obtained were summarized, and it was point out that to study the NMPC for uncertainty systems and time-delay systems would be significant for further development of MPC theory and broadening MPC application fields.
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This chapter provides an overview of nonlinear model predictive control (NMPC) applications in industry, focusing primarily on recent applications reported by NMPC vendors. A brief summary of NMPC theory is presented to highlight issues pertinent to NMPC applications. Several industrial NMPC implementations are then discussed with reference to modelling, control, optimisation and implementation issues. Results from several industrial applications are presented to illustrate the benefits possible with NMPC technology. The chapter concludes with a discussion of future needs in NMPC theory and practice.
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This paper studies the application of Economic Nonlinear Model Predictive Control (ENMPC) and conventional NMPC to a biochemical reactor system with variation in reaction kinetics and disturbance values. The controlled variable for this study is the biomass concentration of the outlet stream leaving the reactor. To control it, conventional NMPC scheme which minimizes the controlled variable deviation to a desired set point and ENMPC scheme which optimizes the biomass production of the system are simulated against disturbance in reactor feed substrate concentration. Result shows that the conventional NMPC schemes are able to bring or maintain the controlled variable to a desired set point. However, the ENMPC scheme outperform the conventional NMPC in cumulative biomass production along the simulation period of up to 57% at the cost of higher computational time.
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Nonlinear model predictive control (NMPC) has been introduced in commercial applications. Since mid 1996 approximately 50 applications have been commissioned in polymers, chemicals, food, pulp and paper, and oil refining. This industrial presentation provides a brief overview of the commercial NMPC package and presents a summary of a specific polymers application that was chosen to demonstrate the different nonlinear models that can be used in a NMPC application.
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Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC is interpreted as an approximat
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