Concurrent Design Optimization of Mechanical Structure and Control for High Speed Robots

Park, Jahng Hyon; Asada, H. Harry

doi:10.23919/acc.1993.4793381

Cited by 21 publications

(22 citation statements)

References 7 publications

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“…Using gravity balance as the plant design objectiveφ(·) simplifies the problem, but is inaccurate for high-speed motions. 101 Allison presented a more complete formulation that produces system-optimal results for high-speed counterbalanced manipulators. 21 Trivedi et al also used a Case 3 approach for soft robotic manipulator design where a static model was used for physical system optimization.…”

Section: Optimal Dynamic System Designmentioning

confidence: 99%

“…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%

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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: Optimal Dynamic System Designmentioning

confidence: 99%

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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“…In this formulation, the structure-control design variable vector p * = [p * s , p * c ] T is found by minimizing Ψ ∈ R nJ * n ξ (called in this paper robust performance index) given in (12), i.e., the square of the elements of the Jacobian of the original performance index vector with respect to ξ (the terms in ( ∇Φ ξ ) 2 ), subject to the dynamic model of the feedback system (2), the sensitivity of the state vector (11), static and dynamic inequality constraints (3), static and dynamic equality constraints (4), lower and upper limits in both, the structural design vector p s (5), and the input vector u (6). The multiobjective dynamic optimization problem for the robust structure-control design of a mechatronic system is formulated as follows:…”

Section: Formulation Of the Robust Structure-control Designmentioning

confidence: 99%

“…In [10], a method to increase the dynamic performance and to reduce the control effort of an actively controlled space structure is presented. The authors of [5] present a concurrent design method of the mechanical structure and control of a robot manipulator based on an iterative algorithm. In [6], the design for control (DFC) approach is formally presented; the idea of DFC is to design the mechanical structure as simple as possible such that the dynamic model of the system be simplified; therefore, a better control performance is achieved for regulation.…”

Section: Introductionmentioning

confidence: 99%

Robust Structure-Control Design Approach for Mechatronic Systems

Villarreal-Cervantes¹,

Cruz-Villar

Álvarez-Gallegos³

et al. 2013

IEEE/ASME Trans. Mechatron.

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In this paper, a robust formulation for the structurecontrol design of mechatronic systems is developed. The proposed robust approach aims at minimization of the sensitivity of the nominal design objectives with respect to uncertain parameters. The robust integrated design problem is established as a nonlinear multiobjective dynamic optimization one, which in order to consider synergetic interactions uses mechanical and control nominal design objectives. A planar parallel robot and its controller are simultaneously designed with the proposed approach when the nominal design objectives are the tracking error and the manipulability measure. The payload at the end-effector is considered as the uncertain parameter. Experimental results show that a robustly designed parallel robot presents lower sensitivity of the nominal design objectives under the effects of changes at the payload than a nonrobustly designed one.Index Terms-Differential evolution algorithm, mechatronic design, robust structure-control design.

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“…Consequently, in order to help mechanical structure and controller of mechatronic system modeling simultaneously, the mechatronic system design methods must be integration. Accordingly, some of numerical based integrated design strategies for mechatronic system were proposed to some fields such as: aerospace [1][2][3], robotics [4][5][6] and manufacturing systems [7][8] in the early years. However, the dynamic models derived with the above integrated methods typically have a high order.…”

Section: Integrated Modeling and Analysis Of Dynamic Mechatronic Systemsmentioning

confidence: 99%