Preview Enhanced Rule-Optimized Fuzzy Logic Damper Controller

Kaldas, Mina M.S.; Çalışkan, Kemal; Henze, Roman; Küçükay, Ferit

doi:10.4271/2014-01-0868

Cited by 12 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From a theoretical viewpoint, the closed-loop stability of the proposed e-MPC can be achieved by including the term ( + ) ( + ) into the objective function (16) via (15), where is the solution of the algebraic Riccati equation for the system in (7), along with adding the constraint ( + ) ∈ Θ into, where Θ is a positive invariant set for the system. However, stability can also be achieved by appropriately choosing the state and input weighting coefficients in (16).…”

Section: E-mpc Stabilitymentioning

confidence: 99%

“…A wide range of preview suspension controllers has been proposed in the literature, including feedforward compensators [7], fuzzy logic controllers [8], gain scheduled controllers [9] and neural network implementations [10]. Linear quadratic regulators (LQRs) and linear quadratic Gaussian (LQG) controllers are frequently adopted optimal control strategies for preview suspensions, because of their simple formulations and the common assumption of linear suspension dynamics ( [11], [12], [13], [14], [15], [16], [17]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Theunissen¹,

Sorniotti

Gruber

et al. 2020

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

Latest advances in road profile sensors make the implementation of pre-emptive suspension control a viable option for production vehicles. From the control side, model predictive control (MPC) in combination with preview is a powerful solution for this application. However, the significant computational load associated with conventional implicit model predictive controllers (i-MPCs) is one of the limiting factors to the widespread industrial adoption of MPC. As an alternative, this paper proposes an explicit model predictive controller (e-MPC) for an active suspension system with preview. The MPC optimization is run offline, and the online controller is reduced to a function evaluation. To overcome the increased memory requirements, the controller uses the recently developed regionless e-MPC approach. The controller was assessed through simulations and experiments on a sport utility vehicle demonstrator with controllable hydraulic suspension actuators. For frequencies <4 Hz, the experimental results with the regionless e-MPC without preview show a ~10% reduction of the root mean square value of the vertical acceleration of the sprung mass with respect to the same vehicle with a skyhook controller. In the same frequency range, the addition of preview improves the heave and pitch acceleration performance by a further 8% to 21%.

show abstract

Section: E-mpc Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Theunissen¹,

Sorniotti

Gruber

et al. 2020

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

show abstract

“…The parameters of Fuzzy Logic inference rules and membership functions are tuned by the optimizer to handle the conflicting requirements of ride comfort, road holding and suspension deflection. Furthermore, the authors examine the performance enhancement of the developed Rule-Optimized Fuzzy Logic controller for semi-active suspension system by adding the road preview capability in [13].…”

Section: Introductionmentioning

confidence: 99%

Triple-Control-Mode for Semi-Active Suspension System

Kaldas

Çalışkan

Henze

et al. 2015

SAE Int. J. Commer. Veh.

Self Cite

View full text Add to dashboard Cite

“…The optimum RB and scaling factors are obtained in Stage 1 using the assumed MFs; then, in stage 2, the parameters of these MFs are modified, using the optimum RB and scaling factors. These parameters are adjusted based on the assumptions given in [38]. Thus, each input and output variable has six and eight MF parameters to be adjusted, respectively, as illustrated in Figure 4b.…”

Section: Optimisation Processmentioning

confidence: 99%

“…Alternatively, the FA-FLC and 4W-FLC are assumed to be composed of sub-FLCs [38], such that each sub-FLC requires only two inputs, as shown in Figure 3b,c, respectively. Thus, the FA-FLC consists of two sub-FLCs, namely FA1-FLC and FA2-FLC, in which the former uses the preview information from the front-left vehicle suspension and generates the sub-control force F a1 .…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

The Control of an Active Seat Suspension Using an Optimised Fuzzy Logic Controller, Based on Preview Information from a Full Vehicle Model

2018

View full text Add to dashboard Cite

Abstract:The use of suspension preview information obtained from a quarter vehicle model (QvM) to control an active seat has been shown by the authors to be very promising, in terms of improved ride comfort. However, in reality, a road vehicle will be subjected to disturbances from all four wheels, and therefore the concept of preview enhanced control should be applied to a full vehicle model. In this paper, different preview scenarios are examined, in which suspension data is taken from all or limited axles. Accordingly, three control strategies are hypothesized-namely, front-left suspension (FLS), front axle (FA), and four wheel (4W). The former utilises suspension displacement and velocity preview information from the vehicle suspension nearest to the driver's seat. The FA uses similar preview information, but from both the front-left and front-right suspensions. The 4W controller employs similar preview information from all of the vehicle suspensions. To cope with friction non-linearities, as well as constraints on the active actuator displacement and force capabilities, three optimal fuzzy logic controllers (FLCs) are developed. The structure of each FLC, including membership functions, scaling factors, and rule base, was sequentially optimised based on improving the seat effective amplitude transmissibility (SEAT) factor in the vertical direction, using the particle swarming optimisation (PSO) algorithm. These strategies were evaluated in simulation according to ISO 2631-1, using different road disturbances at a range of vehicle forward speeds. The results show that the proposed controllers are very effective in attenuating the vertical acceleration at the driver's seat, when compared with a passive system. The controller that utilised suspension preview information from all four corners of the car provided the best seat isolation performance, independent of vehicle speed. Finally, to reduce the implementation cost of the "four suspension" controller, a practical alternative is developed that requires less measured preview information.

show abstract

Preview Enhanced Rule-Optimized Fuzzy Logic Damper Controller

Cited by 12 publications

References 11 publications

Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Triple-Control-Mode for Semi-Active Suspension System

The Control of an Active Seat Suspension Using an Optimised Fuzzy Logic Controller, Based on Preview Information from a Full Vehicle Model

Contact Info

Product

Resources

About