Experimental validation of model predictive control stability for autonomous driving

Lima, Pedro F.; Pereira, Gonçalo Collares; Mårtensson, Jonas; Wahlberg, Bo

doi:10.1016/j.conengprac.2018.09.021

Cited by 38 publications

(22 citation statements)

References 30 publications

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“…On the other hand, hard constraints imposed on predicted state or controlled variables are likely to lead to an empty set of feasible solution of the whole MPC optimization task. Hence, in place of the hard state constraints (17) their soft versions are used…”

Section: Derivation Of the Computationally Efficient Mpc Algorithmentioning

confidence: 99%

“…Hence, there are n u N u + 3 = 2N u + 3 decision variables. Since it is straightforward that we allow violation of the original hard state constraints (17) and (19) only to find a feasible solution, it is necessary to minimize the necessary degree of violation. Hence, in the cost-function of the optimization problem (22) there are 3 penalty terms the objective of which is to keep the values of the decision variables ε min (k), ε max (k) and ε x (k) as low as possible.…”

Section: Derivation Of the Computationally Efficient Mpc Algorithmentioning

confidence: 99%

“…First of all, MPC algorithms provide very powerful and fast low-level control of engines [14]- [16]. Secondly, MPC methods are able to manage difficult problems such as: autonomous driving [17], autonomous racing [18], urban road traffic network control [19], and traction control [21]. The most advanced MPC applications in automotive systems have complex objectives.…”

Section: Introductionmentioning

confidence: 99%

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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: Derivation Of the Computationally Efficient Mpc Algorithmentioning

confidence: 99%

Section: Derivation Of the Computationally Efficient Mpc Algorithmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Nebeluk

Ławryńczuk

2021

IEEE Access

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“…As a result, MPC methods have been used to numerous industrial processes [7], e.g., chemical reactors [8], distillation columns [9], waste water treatment plants [10], solar power stations [11], cement kilns [12], pasteurization plants [13] and pulp digesters [14]. In addition to that, MPC algorithms are more and more popular in other areas; example applications are: fuel cells [15], active vibration attenuation [16], combustion engines [17], robots [18], synchronous motors [19], mechanical systems [20], freeway traffic congestion control [21] and autonomous driving [22].…”

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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“…Improved driving safety could as well be reached by control including a suitable tire-model adaptation while driving, based on measurements of driving conditions [20]. Examples of additional ways to make autonomous vehicles safer include: motion planning with focus on safe stop trajectories [21]; a computationally efficient departure prediction algorithm [22]; and a model predictive controller formulation for trucks with included factors related to controller stability [23]. Especially for large vehicles, questions on how to model and formulate motion problems [24,25] and how to keep trip time while decreasing fuel consumption in a computationally efficient way [26][27][28][29] are important as well.…”

Section: Introductionmentioning

confidence: 99%