Design of a fast real-time LPV model predictive control system for semi-active suspension control of a full vehicle

Morato, Marcelo Menezes; Nguyen, Minh-Khai; Sename, Olivier; Dugard, Luc

doi:10.1016/j.jfranklin.2018.11.016

Cited by 67 publications

(48 citation statements)

References 27 publications

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“…With respect to the control goal and the considered scenario, the simulation consists of a full-vehicle nonlinear model, which has been validated on a real car 67 . As stated, this full nonlinear model is Euler-discretized considering a sampling period of = 0.5 s. The control horizon is synthesized with a prediction horizon of = 10 steps, as suggested in previous papers 66,36 .…”

Section: Full Nonlinear Modelmentioning

confidence: 99%

An input‐to‐state stable model predictive control framework for Lipschitz nonlinear parameter varying systems

Morato

Normey-Rico

Sename

2020

Intl J Robust & Nonlinear

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This paper develops a state-feedback Model Predictive Control (MPC) framework for Nonlinear Parameter Varying (NLPV) systems with explicit Lipschitz nonlinearities along the input trajectory. Considering a guess for the evolution of the scheduling parameters along the prediction horizon, the proposed optimization procedure for the MPC design includes a terminal stage cost, a contracting terminal region constraint and nominal predictions scheduled with respect to this guess. The terminal region is taken so that it is monotonically decreasing to a predetermined set, which guarantees recursive feasibility of the algorithm with respect to a bound on the admissible uncertainties (introduced from the scheduling parameter guess). The terminal set is a scheduled robust control invariant set for Lipschitz nonlinear systems, computed through some proposed LMIs. The inclusion of the terminal ingredient also serves to demonstrate Input-to-State Quadratic Stability. This paper ends with a successful numerical simulation example of the technique applied to the control of a Semi-Active automotive suspension system equipped with Electro-Rheological dampers.

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Section: Full Nonlinear Modelmentioning

confidence: 99%

An input‐to‐state stable model predictive control framework for Lipschitz nonlinear parameter varying systems

Morato

Normey-Rico

Sename

2020

Intl J Robust & Nonlinear

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“…In general, MPC for convex systems has been a topic that has received an intense interest by the research community in the last few years, for which the interested reader is referred to Refs. [104][105][106][107] and the references therein.…”

Section: Model Predictive Control For Convex Systemsmentioning

confidence: 99%

A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems

2019

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This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).

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“…Some of the most modern techniques have been tested, such as clipped optimal LQRs in [5],

H_{\infty}

techniques [6], non‐linear and linear parameter‐varying (LPV) strategies [7]. There are also some results proving the application of model predictive control (MPC) to this problem [8–10], which is a topic increasingly sought by the automotive industry, in particular when considering more complex, non‐linear models.…”

Section: Introductionmentioning

confidence: 99%

“…With respect to our previous works [9, 10], fast MPC algorithms were developed considering full‐car models. The input non‐linearity and the dissipativity constraints were handled with the use of a pre‐filter, which made the model, from the MPC viewpoint, Linear Time‐Invariant (LTI).…”

Section: Introductionmentioning

confidence: 99%

Sub‐optimal recursively feasible Linear Parameter‐Varying predictive algorithm for semi‐active suspension control

Morato

Normey‐Rico

Sename

2020

IET Control Theory & Appl

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This paper proposed a control algorithm to enhance the comfort of passengers in a vehicle equipped with Semi-Active Suspension systems, manipulating the force delivered by the Semi-Active damper. For this goal, the vertical dynamics of the car are represented through a quasi-Linear Parameter Varying (qLPV) model. Then, the algorithm resides in solving a set-constrained Model Predictive Control (MPC) problem, embedding a comfort performance index to the MPC optimization function. The sub-optimality of this algorithm resides in the fact that the MPC is synthesized considering a frozen guess for the evolution of the qLPV scheduling parameters along the prediction horizon. Assuming bounds on the variation rates of the qLPV scheduling parameters, the method enables a replacement of the original complex nonlinear LPV MPC optimization by a much simpler Quadratic Program (QP). This QP includes a Lyapunov-decreasing stage cost and embeds set-based terminal ingredients, which guarantee that the domain of attraction of the controller is enlarged and that recursive feasibility can be maintained despite the non-exact model scheduling (and suboptimality). The control structure is tested and compared to other optimal control approaches, such as a clipped Linear Quadratic Regulator. The paper ends with successful realistic nonlinear simulations of a 1/5-scaled car with Electro-Rheological suspensions, which illustrate the overall good operation and behaviour achieved with the proposed regulation algorithm. With the proposed method, the comfort of the passengers is substantially improved.

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Design of a fast real-time LPV model predictive control system for semi-active suspension control of a full vehicle

Cited by 67 publications

References 27 publications

An input‐to‐state stable model predictive control framework for Lipschitz nonlinear parameter varying systems

An input‐to‐state stable model predictive control framework for Lipschitz nonlinear parameter varying systems

A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems

Sub‐optimal recursively feasible Linear Parameter‐Varying predictive algorithm for semi‐active suspension control

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