Tuning of Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance

Nebeluk, Robert; Ławryńczuk, Maciej

doi:10.1007/978-3-030-50936-1_83

Cited by 2 publications

(5 citation statements)

References 16 publications

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“…When MPC algorithms are used for vehicle obstacle avoidance, nonlinear optimization is used on-line [20], [22], [25]- [29]. Typically, vehicle obstacle avoidance and fuel usage minimization tasks are treated separately.…”

Section: Discussionmentioning

confidence: 99%

“…The algorithm presented in [24] addresses the problem of backward driving control. The problem of obstacle avoidance, very important in numerous applications, is considered in [22], [25]- [29]. Lastly, the problem of energy minimization may be also taken into account [30], which is relatively simple in the case of electric cars [31], [32].…”

Section: Introductionmentioning

confidence: 99%

“…The most important practical problem that characterizes the existing MPC algorithms used for obstacle avoidance is the necessity of solving in real-time nonlinear optimization tasks. The gradient-based Sequential Quadratic Programming (SQP) algorithm may be used for this purpose [20], [25]- [29]. An alternative is to use heuristic Particle Swarm Optimization (PSO) algorithm [22].…”

Section: Introductionmentioning

confidence: 99%

“…Numerous works, e.g. [20], [22], [25]- [29], use for the first of the mentioned tasks nonlinear on-line optimization, in this work much simpler quadratic programming is only necessary. As pointed out previously, fuel usage optimization is typically treated separately while in this work these two objectives are possible in MPC.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

Nebeluk

Ławryńczuk

2021

IEEE Access

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This work is concerned with Model Predictive Control (MPC) algorithm for vehicle obstacle avoidance. The second objective of the algorithm is on-line minimization of fuel utilization. At first, the rudimentary nonlinear MPC optimization problem is formulated. Next, the constraints related to the predicted process state variables are formulated as soft ones to guarantee computational safety. Furthermore, in order to obtain a computationally simple procedure, the process dynamics and the fuel utilization model are linearized on-line and used for prediction in MPC. It leads to a quadratic programming MPC task, the necessity of nonlinear optimization performed in real-time is eliminated. In order to stress advantages of the discussed computationally uncomplicated MPC method it is compared with the basic scheme with on-line nonlinear optimization in terms of control quality and computational time. Additionally, effectiveness of the MPC algorithm is discussed in presence of modeling errors and measurement noise. Finally, additional constraints imposed on the rate of change of the manipulated variables are considered.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

Nebeluk

Ławryńczuk

2021

IEEE Access

Self Cite

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“…It needs only a very limited number of simulations. The considered tuning method was discussed in [35] for the MPC employed for vehicle obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

Tuning of Multivariable Model Predictive Control for Industrial Tasks

Nebeluk

Ławryńczuk

2021

Algorithms

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This work is concerned with the tuning of the parameters of Model Predictive Control (MPC) algorithms when used for industrial tasks, i.e., compensation of disturbances that affect the process (process uncontrolled inputs and measurement noises). The discussed simulation optimisation tuning procedure is quite computationally simple since the consecutive parameters are optimised separately, and it requires only a very limited number of simulations. It makes it possible to perform a multicriteria control assessment as a few control quality measures may be taken into account. The effectiveness of the tuning method is demonstrated for a multivariable distillation column. Two cases are considered: a perfect model case and a more practical case in which the model is characterised by some error. It is shown that the discussed tuning approach makes it possible to obtain very good control quality, much better than in the most common case in which all tuning parameters are constant.

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Tuning of Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance

Cited by 2 publications

References 16 publications

Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

Tuning of Multivariable Model Predictive Control for Industrial Tasks

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