Designing a Low Order Robust Controller for an Active Suspension System Thank LMI, Genetic Algorithm and Gradient Search

Mauff, Frédéric Le; Duc, Gilles

doi:10.3166/ejc.9.29-38

Cited by 10 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To improve the values of the opt g , a hybrid GA (HGA) was then developed. This HGA combines one of the GA variants with a gradient direct search method 26. The HGA uses the current optimum (found by a GA variant) as an initial estimate (or starting point) in a gradient‐based algorithm to find a more accurate solution (i.e., opt g ) for the given problem; therefore, GA‐ v 1 and GA‐ v 2 are converted into HGA‐ v 1 and HGA‐ v 2, respectively.…”

Section: Methodsmentioning

confidence: 99%

New experimental design method for highly nonlinear and dimensional processes

Zhang¹,

Olsen²,

Block³

2007

AIChE Journal

View full text Add to dashboard Cite

in Wiley InterScience (www.interscience.wiley.com).A novel nonlinear experimental design scheme to identify the optimum of a multidimensional system was computationally evaluated. The method, a hybrid neural networkgenetic algorithm coupled with a fuzzy clustering technique, was developed to accumulate former experimental knowledge and suggest optimal operating conditions for future experimentation. Because of the uncertainty associated with complex systems, several response surfaces with various degrees of complexity and dimensionality were used as case studies for evaluating the new methodology to gain insight into the challenges that may be presented by arbitrary experimental optimization problems in practice. The long-term objective of this work is to develop an experimental design scheme that will discover the best process operating conditions for any difficult process optimization problem in as few experiments as possible. The simulation results demonstrate that the algorithm performance is not significantly affected by parameters such as the number of experiments in a batch of experimentation and convergence criteria for the genetic algorithm as well as for various noise levels below 15%. However, higher noise levels, complexity, and dimensionality decrease its performance. Even at higher levels of noise, complexity, and dimensionality, though, the new approach finds significantly better solutions than those found with traditional statistical experimental design methods, with similar or only slightly more experiments.

show abstract

Section: Methodsmentioning

confidence: 99%

New experimental design method for highly nonlinear and dimensional processes

Zhang¹,

Olsen²,

Block³

2007

AIChE Journal

View full text Add to dashboard Cite

show abstract

“…[34] used a GA to optimally tune a fractional-order PID-controller to manage an automatic voltage regulator system. Other examples of the implementation of a Genetic Algorithm to optimize feedback gains can be found for (quarter) car active suspension control [35,36], multi-machine power systems [37], (double-parallel) inverted pendulum systems [38] or hovercraft control [39]. The papers mentioned above applied the Genetic Algorithm only to determine the optimal feedback gains.…”

Section: A Genetic Algorithm Meets the Optimization Problem Requirementsmentioning

confidence: 99%

Towards a generic optimal co-design of hardware architecture and control configuration for interacting subsystems

et al. 2019

View full text Add to dashboard Cite

In plants consisting of multiple interacting subsystems, the decision on how to optimally select and place actuators and sensors and the accompanying question on how to control the overall plant is a challenging task. Since there is no theoretical framework describing the impact of sensor and actuator placement on performance, an optimization method exploring the possible configurations is introduced in this paper to find a trade-off between implementation cost and achievable performance.Moreover, a novel model-based procedure is presented to simultaneously co-design the optimal

show abstract

“…Therefore, in the case of our LMI region, the necessary and sufficient condition for D-stability is the existence of P cl > 0 that simultaneously satisfies the above LMI conditions in Eqs. (14) and (15). It may be emphasized here that the pole region is normally treated as a constraint in the LMI-based design method.…”

Section: Regional Pole Assignment Via Linear Matrix Inequalitiesmentioning

confidence: 99%

“…There are a few precedents for LMI-based design combined with GAs (4), (15), (21), (24) . However, almost all of them use the H ∞ synthesis, and no studies have been reported regarding the design of critical systems.…”

Section: Introductionmentioning

confidence: 99%

Design of Critical Control Systems Utilizing Genetic Algorithms and LMI-Based Regional Pole Assignment Technique

Satoh

Saito

2006

JSME Int. J., Ser. C

View full text Add to dashboard Cite

A method for designing critical systems subject to magnitude-and-rate-limited reference inputs is proposed. The worst-case tracking error is minimized under the constraint on the control effort. The LMI-based technique developed by Chilali and Gahinet is utilized to assign the resulting closed-loop poles into the intersection of a vertical strip and a conic sector. The optimal pole region is searched via the simple genetic algorithm. An illustrative design example of the position control system for a two-inertia system is provided to demonstrate the proposed design method. The simulation and experimental results show that a critical position control system is successfully designed on the basis of the proposed method.

show abstract

Designing a Low Order Robust Controller for an Active Suspension System Thank LMI, Genetic Algorithm and Gradient Search

Cited by 10 publications

References 2 publications

New experimental design method for highly nonlinear and dimensional processes

New experimental design method for highly nonlinear and dimensional processes

Towards a generic optimal co-design of hardware architecture and control configuration for interacting subsystems

Design of Critical Control Systems Utilizing Genetic Algorithms and LMI-Based Regional Pole Assignment Technique

Contact Info

Product

Resources

About