Sensor/actuator selection for tensegrity structures

Li, Faming; Skelton, Robert E.

doi:10.1109/cdc.2006.377413

Cited by 8 publications

(3 citation statements)

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“…112,[148][149][150][151] Sensor and actuator placement combined with control design is another extensively studied area of dynamic system design optimization. 135,[152][153][154][155][156] Other important applications include automotive suspension and powertrain design, 24, 96, 99, 157-165 robotic system design, 5,7,8,20,21,91,100,101,105,140,[166][167][168][169] and energy systems. 114,[170][171][172][173] While substantial depth exists in select application areas for dynamic system design optimization, fundamental MDO formulations specifically designed for dynamic systems are largely not available.…”

Section: Existing Uses Of Mdo For Dynamic System Designmentioning

confidence: 99%

Multidisciplinary Design Optimization of Dynamic Engineering Systems

Allison

Herber

2013

54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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Dynamic engineering systems are playing an increasingly important role in society, especially as active and autonomous dynamic systems become more mature and prevalent across a variety of domains. Successful design of complex dynamic systems requires multidisciplinary analysis and design techniques. While multidisciplinary design optimization (MDO) has been used successfully for the development of many dynamic systems, the established MDO formulations were developed around fundamentally static system models. We still lack general MDO approaches that address the specific needs of dynamic system design. In this article we review the use of MDO for dynamic system design, identify associated challenges, discuss related efforts such as optimal control, and present a vision for fully integrated design approaches. Finally, we lay out a set of exciting new directions that provide an opportunity for fundamental work in MDO. Nomenclature a(·)= analysis function a, b = example problem parameters α, β = energy domain designations A = state matrix for a linear and time invariant system B = input matrix for a linear and time invariant system c = suspension damping coefficient ε = convergence tolerance f (·)= design objective function f (·)= derivative function f a (·) = algebraic constraint g(·)= design constraint functions g p (·) = physical system constraints γ(t) = algebraic variable vector h i = time step i = time step index j = Gauss-Seidel block index, multiple-shooting time segment index k = iteration counter k s = suspension spring stiffness K = gain matrix K * = optimal gain matrix L(·) = Lagrange or running cost term m = number of Gauss-Seidel coordinate blocks n s = number of states n t = number of time steps n T = number of time segments φ(·) = cost function φ * (·) = optimal-value function (inner loop solution) φ(·) = alternative plant design objective function ψ(·) = Mayer or terminal cost term π(·) = augmented Lagrangian penalty function t = time t F = length of the time horizon t i = time at step i T j = time at the end of time segment j u(t) = control input trajectories u * (t) = optimal control trajectories u i = control input at time step i U = matrix discretization of u(t) x = optimization variable vector x * = optimal solution x k = solution estimate at iteration k x c = control system design variable vector x p = physical system design variable vector x p * = optimal plant design X = Cartesian product of closed convex sets ξ(t) = state variable trajectories ξ * (t) = optimal state trajectories ξ i = state at time step î ξ(t) = subset of state trajectorieṡ ξ(·) = time derivative of ξ(t) Ξ = discretization of ξ(t) Ξ = subset of discretized state trajectories y = coupling variable Y = matrix of initial state values for multiple shooting time segments ζ(·) = defect constraint functions (residuals) ζ i (·) = defect constraint between time segments

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Section: Existing Uses Of Mdo For Dynamic System Designmentioning

confidence: 99%

Multidisciplinary Design Optimization of Dynamic Engineering Systems

Allison

Herber

2013

54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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“…For example Skelton and Li [78] and Li and Skelton [43] started from a configuration with sensors and actuators in all the edges of the structure, and then successively eliminated those whose contribution to the desired closed loop performance is smaller.…”

Section: Design For Tensegrity Structuresmentioning

confidence: 99%

“…In this case, the actuator and measurement noises would deteriorate the output performance no matter how large the control effort was (Skelton and Li [78]), so it is necessary to use high precision components which implies a higher overall cost. Skelton and Li [78] and Li and Skelton [43] reformulated the problem, calling it economic design, to find the minimum number of sensors and actuators with the minimum necessary precision to achieve the desired closed loop performance. Finally, the criterion used by de Jager [14] was to achieve a given closed loop shape change performance in order to go from one initial configuration to another while keeping the stability of the structure.…”

Section: Design For Tensegrity Structuresmentioning

confidence: 99%

Tensegrity frameworks: Dynamic analysis review and open problems

Tur

Juan

2009

Mechanism and Machine Theory

113

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The main objective of this paper is twofold. First, to conclude the overview about tensegrity frameworks, started by the same authors in a previous work, covering the most important dynamic aspects of such structures. Here, the most common approaches to tensegrity dynamic modeling used so far are presented, giving the most important results about their dynamic behavior under external action.Also, the main underlying problems are identified which allow the authors to give a clear picture of the main research lines currently open, as well as the most relevant contributions in each of them, which is in fact the second main objective of this paper. From the extensive literature available on the subject, four main areas have been identified: design and form-finding methods which deal with the problem of finding stable configurations, shape changing algorithms which deal with the problem of finding stable trajectories between them and, also control algorithms which take into account the dynamic model of the tensegrity structure and possible external perturbations to achieve the desired goal and performance.Finally, some applications of such structures are presented emphasizing the increasing interest of the scientific community on tensegrity structures.

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