scholarly journals Co-Design of an Active Suspension Using Simultaneous Dynamic Optimization

Author(s):  
James T. Allison ◽  
Zhi Han

Design of physical systems and associated control systems are coupled tasks; design methods that manage this interaction explicitly can produce system-optimal designs, whereas conventional sequential processes may not. Here we explore a new technique for combined physical system and control design (co-design) based on a simultaneous dynamic optimization approach known as direct transcription, which transforms infinite-dimensional control design problems into finite dimensional nonlinear programming problems. While direct transcription problem dimension is often large, sparse problem structures and fine-grained parallelism (among other advantageous properties) can be exploited to yield computationally efficient implementations. Extension of direct transcription to co-design gives rise to a new problem structures and new challenges. Here we illustrate direct transcription for co-design using a new automotive active suspension design example developed specifically for testing co-design methods. This example builds on prior active suspension problems by incorporating a more realistic physical design component that includes independent design variables and a broad set of physical design constraints, while maintaining linearity of the associated differential equations.

2014 ◽  
Vol 136 (8) ◽  
Author(s):  
James T. Allison ◽  
Tinghao Guo ◽  
Zhi Han

Design of physical systems and associated control systems are coupled tasks; design methods that manage this interaction explicitly can produce system-optimal designs, whereas conventional sequential processes may not. Here, we explore a new technique for combined physical and control system design (co-design) based on a simultaneous dynamic optimization approach known as direct transcription, which transforms infinite-dimensional control design problems into finite-dimensional nonlinear programming problems. While direct transcription problem dimension is often large, sparse problem structures and fine-grained parallelism (among other advantageous properties) can be exploited to yield computationally efficient implementations. Extension of direct transcription to co-design gives rise to new problem structures and new challenges. Here, we illustrate direct transcription for co-design using a new automotive active suspension design example developed specifically for testing co-design methods. This example builds on prior active suspension problems by incorporating a more realistic physical design component that includes independent design variables and a broad set of physical design constraints, while maintaining linearity of the associated differential equations. A simultaneous co-design approach was implemented using direct transcription, and numerical results were compared with conventional sequential optimization. The simultaneous optimization approach achieves better performance than sequential design across a range of design studies. The dynamics of the active system were analyzed with varied level of control authority to investigate how dynamic systems should be designed differently when active control is introduced.


1997 ◽  
Vol 122 (3) ◽  
pp. 567-569 ◽  
Author(s):  
Ricardo H. C. Takahashi ◽  
Juan F. Camino and ◽  
Douglas E. Zampieri ◽  
Pedro L. D. Peres

A methodology for the multiobjective design of controllers is presented, motivated by the problem of designing an active suspension controller. This problem has, as a particular feature, the possibility of being defined with two design variables only. The multiobjective controller is searched inside the space of “optimal controllers” defined by a weighted cost functional. The weightings are taken as the optimization variables for the multiobjective design. The method leads to (local) Pareto-optimal solutions and allows the direct specification of controller constraints in terms of some primary objectives which are taken into account in the multiobjective search. [S0022-0434(00)01403-9]


2010 ◽  
Vol 139-141 ◽  
pp. 2401-2404
Author(s):  
Xian Rong Qin ◽  
Jun Jie Shen ◽  
Jian Jie Zhang ◽  
Yong Feng Li ◽  
Qing Zhang

In this paper, a two-stage optimization approach for structural dynamic design was proposed on the basis of Component Mode Synthesis (CMS in short) technique. To improve the convergence of the optimization, the optimization was decomposed to two stages, i.e. the global and the local stages. The dynamic performance of the global assembled structures was optimized at the global stage by minimizing the total mass, taking the position of nodes as design variables. At the local stage, the performance of the local components was optimized by keeping the component mass less than the initial value, and getting the performance of the global structure improved. The CMS technique was integrated in the optimization to reduce the modeling cost and to determine the objective function of the local stage optimization. The dynamic optimization of the metal structure of an escalator was taken as an example to illustrate the feasibility and effectiveness of the proposed approach.


2021 ◽  
Vol 149 ◽  
pp. 107292
Author(s):  
Cristian Pablos ◽  
Alejandro Merino ◽  
Luis Felipe Acebes ◽  
José Luis Pitarch ◽  
Lorenz T. Biegler

Author(s):  
Wenqing Zheng ◽  
Hezhen Yang

Reliability based design optimization (RBDO) of a steel catenary riser (SCR) using metamodel is investigated. The purpose of the optimization is to find the minimum-cost design subjecting to probabilistic constraints. To reduce the computational cost of the traditional double-loop RBDO, a single-loop RBDO approach is employed. The performance function is approximated by using metamodel to avoid time consuming finite element analysis during the dynamic optimization. The metamodel is constructed though design of experiments (DOE) sampling. In addition, the reliability assessment is carried out by Monte Carlo simulations. The result shows that the RBDO of SCR is a more rational optimization approach compared with traditional deterministic optimization, and using metamodel technique during the dynamic optimization process can significantly decrease the computational expense without sacrificing accuracy.


2017 ◽  
Vol 52 (14) ◽  
pp. 1971-1986 ◽  
Author(s):  
T Vo-Duy ◽  
T Truong-Thi ◽  
V Ho-Huu ◽  
T Nguyen-Thoi

The paper presents an efficient numerical optimization approach to deal with the optimization problem for maximizing the fundamental frequency of laminated functionally graded carbon nanotube-reinforced composite quadrilateral plates. The proposed approach is a combination of the cell-based smoothed discrete shear gap method (CS-DSG3) for analyzing the first natural frequency of the functionally graded carbon nanotube reinforced composite plates and a global optimization algorithm, namely adaptive elitist differential evolution algorithm (aeDE), for solving the optimization problem. The design variables are the carbon nanotube orientation in the layers and constrained in the range of integer numbers belonging to [−900 900]. Several numerical examples are presented to investigate optimum design of quadrilateral laminated functionally graded carbon nanotube reinforced composite plates with various parameters such as carbon nanotube distribution, carbon nanotube volume fraction, boundary condition and number of layers.


Author(s):  
Carlos A. Duchanoy ◽  
Marco A. Moreno-Armendáriz ◽  
Carlos A. Cruz-Villar

In this paper a dynamic optimization methodology for designing a passive automotive damper is proposed. The methodology proposes to state the design problem as a dynamic optimization one by considering the nonlinear dynamic interactions between the damper and the other elements of the suspension system, emphasizing geometry, dimensional and movement constraints. In order to obtain realistic simulations of the suspension, a link between a Computer-Aided Engineering Model (CAEM) and a multi-objective dynamic optimization algorithm is developed. As design objectives we consider the vehicle safety and the passenger comfort which are represented by the contact area of the tire and the vibrations of the cockpit respectively. The damper is optimized by stating a set of physical variables that determine the stiffness and damping coefficients as independent variables for the dynamic optimization problem, they include the spring helix diameter, the spring wire diameter, the oil physical characteristics and the bleed orifice diameters among others. The optimization algorithm that we use to solve the problem at hand is a multi-objective evolutive optimization algorithm. For this purpose we developed a parameterized model of the damper which is used to link the CAE tools and the optimization software, thus enabling fitness evaluations during the dynamic optimization process. By selecting the physical characteristics of the damper as design variables instead of the typical stiffness and damping coefficients, it is possible to consider important design constrains as the damper size, movement limitations and anchor points. As result of the proposed methodology a set of blueprints of non dominated Pareto configurations of the damper are provided to the decision maker.


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