Static output feedback control for active suspension using PSO-DE/LMI approach

Kong, Yongsu; Zhao, Ding; Yang, Bin; Shen, Tao; Li, Hang; Han, Kyongwon

doi:10.1109/icma.2012.6282871

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…The boundary defined by the set of all points mapped from the Pareto-optimal set is called the Pareto-optimal front. A particle swarm optimization (PSO) algorithm is a wellknown swarm-based stochastic algorithm, and its canonical mechanism is formulated as follows [34], [35]. A set of n p particles is considered a population, where each particle j has a position vector (i.e., design variable vector)…”

Section: B Multi-objective Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Multi-Objective Finite-Frequency H _∞/GH ₂ Static Output-Feedback Control Synthesis for Full-Vehicle Active Suspension Systems: A Metaheuristic Optimization Approach

et al. 2023

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In this paper, the problem of multi-objective control for active suspension systems with polytopic uncertainty is addressed via H ∞ /GH 2 static output feedback with a limited-frequency characteristic. For the overall analysis of the performance demanding both vehicle-ride comfort related to vertical-and transversal-directional dynamics and the time-domain constraints related to the driving maneuverability, a seven-degree-of-freedom full-vehicle model with an active suspension system is investigated. The robust static output-feedback control strategy is adopted because some state variables may not be directly measured in a realistic implementation. In designing this control, the finite-frequency H ∞ performance using the generalized Kalman-Yakubovich-Popov lemma is optimized to improve the passenger's ride comfort, while the GH 2 performance is optimized to guarantee the constraints concerning the suspension deflection limitation, road-holding ability, and actuator saturation problem. This control synthesis problem is formulated as nonconvex bilinear matrix inequalities and requires simultaneous consideration of different finite-frequency domain ranges for vertical and transversal motions for evaluating the H ∞ performance. These design difficulties are overcome by the proposed multi-objective quantum-behaved particle swarm optimizer, which efficiently explores the relevant trade-offs between the considered multiple performance objectives and eventually provides the desired Pareto-optimal control set. Further, the numerical simulation cases of a full-vehicle active suspension system are presented to illustrate the effectiveness of the proposed control synthesis methodology in frequency and time domains.INDEX TERMS Active suspension system, static output feedback, finite frequency, generalized Kalman-Yakubovich-Popov lemma, bilinear matrix inequalities, multi-objective metaheuristic algorithm.

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Section: B Multi-objective Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Multi-Objective Finite-Frequency H _∞/GH ₂ Static Output-Feedback Control Synthesis for Full-Vehicle Active Suspension Systems: A Metaheuristic Optimization Approach

et al. 2023

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“…Thus it can be easily extended to other control design problems without necessitating any complicated mathematical manipulations as required in the other cases. Application of hybrid swarm, evolutionary and LMI based techniques can be found in other control applications as well for example in active suspension system (Kong et al 2012).…”

Section: Computational Complexity Of the Bmi Problemmentioning

confidence: 99%

Design of hybrid regrouping PSO–GA based sub-optimal networked control system with random packet losses

Pan

Das

2013

Memetic Comp.

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In this paper, a new approach has been presented to design sub-optimal state feedback regulators over Networked Control Systems (NCS) with random packet losses. The optimal regulator gains, producing guaranteed stability are designed with the nominal discrete time model of a plant using Lyapunov technique which produces a few set of Bilinear Matrix Inequalities (BMIs). In order to reduce the computational complexity of the BMIs, a Genetic Algorithm (GA) based approach coupled with the standard interior point methods for LMIs has been adopted. A Regrouping Particle Swarm Optimization (RegPSO) based method is then employed to optimally choose the weighting matrices for the state feedback regulator design that gets passed through the GA based stability checking criteria i.e. the BMIs. This hybrid optimization methodology put forward in this paper not only reduces the computational difficulty of the feasibility checking condition for optimum stabilizing gain selection but also minimizes other time domain performance criteria like expected value of the set-point tracking error with optimum weight selection based LQR design for the nominal system.

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“…Another research line is state/ output feedback and H ∞ controllers. Kong et al [17] designed an H ∞ -static output feedback controller (SOFC) based on particle swarm optimization (PSO) and differential evolution (DE) algorithms. Li et al [18] applied multiobjective control to synthesize an H ∞ /H 2 SOFC, whereas Suzuki et al [19] reported a robust H 2 control to improve comfort and stability.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Rejection in a One‐Half Semiactive Vehicle Suspension by means of a Fuzzy-H_∞ Controller

Félix-Herrán

Mehdi

Rodríguez-Ortiz

et al. 2019

Shock and Vibration

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A fuzzy-H ∞ control, improved with weighting functions, has been designed and applied to a novel model of a one-half semiactive lateral vehicle (OHSLV) suspension. e herein contribution resides in the development and computation of an H ∞ controller with parallel distributed compensation (PDC) designed from a highly nonlinear system modelled via the Takagi-Sugeno (T-S) fuzzy approach. A fuzzy-H ∞ controller is synthesized for an OHSLV T-S fuzzy model of a suspension with two magnetorheological (MR) dampers including actuators' nonlinear dynamics. e realism of results has been improved by considering the MR damper's behaviours (viscoplasticity, hysteresis, and saturation) and the handling of the phase angle of the sinusoidal disturbance, not included in other reported work. Time-domain tests remark transient time achievements, whereas precise performance criterion indices in the frequency domain are employed to assess the generated outcomes. e proposed solution complies with all performance criteria compared with a benchmark passive average suspension that fails in satisfying most of the performance criteria.

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Static output feedback control for active suspension using PSO-DE/LMI approach

Cited by 5 publications

References 10 publications

Multi-Objective Finite-Frequency H _∞/GH ₂ Static Output-Feedback Control Synthesis for Full-Vehicle Active Suspension Systems: A Metaheuristic Optimization Approach

Multi-Objective Finite-Frequency H _∞/GH ₂ Static Output-Feedback Control Synthesis for Full-Vehicle Active Suspension Systems: A Metaheuristic Optimization Approach

Design of hybrid regrouping PSO–GA based sub-optimal networked control system with random packet losses

Disturbance Rejection in a One‐Half Semiactive Vehicle Suspension by means of a Fuzzy-H_∞ Controller

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Static output feedback control for active suspension using PSO-DE/LMI approach

Cited by 5 publications

References 10 publications

Multi-Objective Finite-Frequency H ∞/GH 2 Static Output-Feedback Control Synthesis for Full-Vehicle Active Suspension Systems: A Metaheuristic Optimization Approach

Multi-Objective Finite-Frequency H ∞/GH 2 Static Output-Feedback Control Synthesis for Full-Vehicle Active Suspension Systems: A Metaheuristic Optimization Approach

Design of hybrid regrouping PSO–GA based sub-optimal networked control system with random packet losses

Disturbance Rejection in a One‐Half Semiactive Vehicle Suspension by means of a Fuzzy-H∞ Controller

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Multi-Objective Finite-Frequency H _∞/GH ₂ Static Output-Feedback Control Synthesis for Full-Vehicle Active Suspension Systems: A Metaheuristic Optimization Approach

Multi-Objective Finite-Frequency H _∞/GH ₂ Static Output-Feedback Control Synthesis for Full-Vehicle Active Suspension Systems: A Metaheuristic Optimization Approach

Disturbance Rejection in a One‐Half Semiactive Vehicle Suspension by means of a Fuzzy-H_∞ Controller