PSO-Based Model Predictive Control for Nonlinear Processes

Wang, Xihuai; Xiao, Jing

doi:10.1007/11539117_30

Cited by 24 publications

(5 citation statements)

References 7 publications

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“…Once the constraint awareness has been infused into the filtering procedure, we can apply the reweighted particle smoother without change. This can be seen from the fact that (13) will not change if we consider p(x t | r k:k+H , z k:k+H ) for the backward smoothing. Summarizing the above, we can readily formulate the new sampling-based NMPC algorithm based on the proposed constraint-aware particle filtering/smoothing method, which is named as CAP-NMPC outlined in Algorithm 1.…”

Section: Constraint-aware Particle Filtering/smoothing For Nmpcmentioning

confidence: 99%

“…The continuation/GMRES method and various other proposed approaches of leveraging the structures of NMPC have proven useful in improving the computational speed [8]- [12]. Evolutionary algorithms, e.g., the particle swarm optimization method, have also received some attention in the literature [13], [14]. This is mainly due to their ability to achieve global Despite the advancements, the high computational costs of nonlinear constrained optimization remains a bottleneck for the application of NMPC, especially when it comes to highdimensional or highly nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Model Predictive Control Based on Constraint-Aware Particle Filtering/Smoothing

Askari,

Zeng,

Fang

2022

Preprint

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Nonlinear model predictive control (NMPC) has gained widespread use in many applications. Its formulation traditionally involves repetitively solving a nonlinear constrained optimization problem online. In this paper, we investigate NMPC through the lens of Bayesian estimation and highlight that the Monte Carlo sampling method can offer a favorable way to implement NMPC. We develop a constraint-aware particle filtering/smoothing method and exploit it to implement NMPC. The new sampling-based NMPC algorithm can be executed easily and efficiently even for complex nonlinear systems, while potentially mitigating the issues of computational complexity and local minima faced by numerical optimization in conventional studies. The effectiveness of the proposed algorithm is evaluated through a simulation study.

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Section: Constraint-aware Particle Filtering/smoothing For Nmpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control Based on Constraint-Aware Particle Filtering/Smoothing

Askari,

Zeng,

Fang

2022

Preprint

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“…On the other hand, from (23), the constraints on the inputs magnitude could also be written as follows:…”

Section: Outputs Constraintsmentioning

confidence: 99%

“…PSO algorithms require much less computational effort than the GA and their implementation is relatively easy [19]. A lot of papers have considered the PSO to solve the optimization problem of NMPC [20][21][22][23]. In these papers, a large number of particles and iterations were used which increases the computational burden of the control algorithm.…”

Section: Introductionmentioning

confidence: 99%

Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

Sahed

Kara

Hadjili³

2015

Applied Computational Intelligence and Soft Computing

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A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.

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“…PSO is used in [17] and [18] to solve the optimization problem in the predictive control formulation, and good results are obtained when compared with GA and quasi-Newton methods. Solis et al [19] compare the application of PSO with GA in the predictive control of a benchmark process, obtaining better results with PSO.…”

mentioning

confidence: 99%

Hybrid predictive control design with mixed inputs based on PSO and its application for control of a Batch Reactor

Muñoz‐Carpintero

Sáez

Škrjanc

2010

IEEE Congress on Evolutionary Computation

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In this work, we propose a combined approach based on Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) for solving the real-time optimization problem appearing in the hybrid predictive control of systems with continuous and discrete inputs. The conventional PSO for continuous problems is mixed with binary PSO for handling discrete variables, and after iterations of the algorithm have ended, SQP is applied for refining the continuous variables. This new method (PSO+SQP) is favorably compared with a conventional non-linear optimization techniques (based on Branch and Bound and Explicit Enumeration) and PSO. Also, a multi-objective approach is considered as a tuning method for the population size and number of iterations for PSO. All these algorithms are applied for the temperature control of a Batch Reactor.

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PSO-Based Model Predictive Control for Nonlinear Processes

Cited by 24 publications

References 7 publications

Nonlinear Model Predictive Control Based on Constraint-Aware Particle Filtering/Smoothing

Nonlinear Model Predictive Control Based on Constraint-Aware Particle Filtering/Smoothing

Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

Hybrid predictive control design with mixed inputs based on PSO and its application for control of a Batch Reactor

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