A hybrid formulation for treatment of point-wise state variable constraints in dynamic response optimization

This paper presents methods of design sensitivity analysis and optimization of dynamic response of mechanical and structural systems. The point-wise state variable constraint function is divided into time sub-domains such that each sub-domain contains only one local maximum point. Then, the original constraint is replaced by a number of equivalent functional constraints. Each functional constraint is the integration of the positive value of the original constraint over its own time sub-domain. A direct differentiation method and three adjoint variable methods of design sensitivity analysis are presented. All of these methods are discussed and compared. It turns out that two of the adjoint variable methods are more efficient than others. A hybrid optimization algorithm based on these methods is proposed in detail. Two problems are solved for optimal design. Comparisons of results with those available in the literature are made. Numerical experience with the proposed method is discussed in detail. It is concluded that the new formulation is extremely efficient and converges to either optimal or near optimal solutions without any difficulty.     

Multiple eigenvalues in structural optimization problems

This paper discusses characteristic features and inherent difficulties pertaining to the lack of usual differentiability properties in problems of sensitivity analysis and optimum structural design with respect to multiple eigenvalues. Computational aspects are illustrated via a number of examples.Based on a mathematical perturbation technique, a general multiparameter framework is developed for computation of design sensitivities of simple as well as multiple eigenvalues of complex structures. The method is exemplified by computation of changes of simple and multiple natural transverse vibration frequencies subject to changes of different design parameters of finite element modelled, stiffener reinforced thin elastic plates.Problems of optimization are formulated as the maximization of the smallest (simple or multiple) eigenvalue subject to a global constraint of e.g. given total volume of material of the structure, and necessary optimality conditions are derived for an arbitrary degree of multiplicity of the smallest eigenvalue. The necessary optimality conditions express (i) linear dependence of a set of generalized gradient vectors of the multiple eigenvalue and the gradient vector of the constraint, and (ii) positive semi-definiteness of a matrix of the coefficients of the linear combination.It is shown in the paper that the optimality condition (i) can be directly applied for the development of an efficient, iterative numerical method for the optimization of structural eigenvalues of arbitrary multiplicity, and that the satisfaction of the necessary optimality condition (ii) can be readily checked when the method has converged. Application of the method is illustrated by simple, multiparameter examples of optimizing single and bimodal buckling loads of columns on elastic foundations.Dedicated to the memory of Ernest F. MasurGuest professor during the period 16 November to 11 December, 1992 and 15 November to 12 December, 1993.     

Additional properties of an extended maximum principle†

This paper establishes conditions of optimality for linear systems whoso control functions are quantized either in magnitude or in switching instants, or both. Optimization of such systems hitherto has been limited primarily to dynamic programming techniques and direct enumeration. The results are derived from the total variations of the cost functional. The main theorem provides sufficient conditions of optimality for linear systems with quadratic and positive semi-definite performance index. The results are significant for digital control systems, pulse-code-modulated control systems and some types of pulse-frequency-modulated systems which are subject to quantized variations only. For the special case where the control functions are not quantized, the optimality conditions provided by the extended maximum principle are shown to be necessary and sufficient.     

Sensitivity analysis and optimal design for fiber reinforced composite disks

For a linearly elastic fiber reinforced composite disk, the first variation of an arbitrary stress, strain and displacement functional corresponding to variation of material parameters is derived by using the direct and adjoint approaches to sensitivity analysis. The results are particularized to the case of total potential and complementary energies. The relevant optimality conditions for optimal design and identification problems are then derived.     

A note on truss design for stress and displacement constraints by optimality criteria methods

One of the difficulties with the optimization of large structural systems by optimality criteria (OC) methods, in which stress constraints are stated in terms of relative displacements, is the need to evaluate a large number of Lagrangians. An improved OC method, which does not require Lagrangians for stress constraints, is outlined in this note in the context of problems with stress constraints and a single displacement constraint. Whilst general formulae giving continuum-type optimality criteria for elastic systems are available (e.g. Rozvany 1989), the criteria for trusses are confirmed here by a simple derivation and verified on a ten-bar truss, for which the proposed technique and a dual programming method are shown to give a twelve-digit agreement.     

A sensitivity interpretation of adjoint variables in optimal design

The adjoint method of computing derivatives of cost and constraint functions with respect to design variables requires the calculation of certain adjoint variables. Until now, the adjoint variables have been looked upon only as some intermediate vectors needed to calculate design derivatives. In this paper, they are shown to have an important significance. They represent the sensitivity of the cost and constraint functions with respect to the loading or forcing function in the design problem. A sensitivity theorem for the adjoint variables is presented for structural, mechanical dynamic, and distributed parameter systems. These results offer some immediate practical advantages, such as a method for computing influence coefficients for structural systems, and a method for verifying (debugging) the analytical calculation of adjoint variables in development of a computer code.     

Control–structural design optimization for vibration of piezoelectric intelligent truss structures

Based on the design sensitivity analysis for structural dynamics in time domain, an integrated control–structural design optimization method is proposed to the vibration control of piezoelectric intelligent truss structure. In this investigation, the objective function and constraint functions include not only the conventional design indexes of structure but also the vibration control indexes and the feedback control variables. The structural design variables are optimized simultaneously with the vibration control system. The sensitivity relations for the control–structure optimization model are derived by using a new method, and the sequential linear programming algorithm is used to solve this kind of optimization problem. The numerical examples given in the paper demonstrate the effectiveness of methods and the program.     

Design sensitivity analysis and optimization of dynamic response

The paper presents methods of design sensitivity analysis and optimization of dynamic response of mechanical and structural systems. A key feature of the paper is the development of procedures to handle point-wise state variable constraints. Difficulties with a previous treatment where such constraints were transformed to equivalent integral constraints are noted and explained from theoretical as well as engineering standpoints. An alternate treatment of such constraints is proposed, developed and evaluated. In this treatment each point-wise state variable constraint is replaced by several constraints that are imposed at all the local max-points for the original constraint function. The differential equations of motion are formulated in the first-order form so as to handle more general problems. The direct differentiation and adjoint variable methods of design sensitivity analysis to deal with the point-wise constraints are presented. With the adjoint variable methods, there are two ways of calculating design sensitivity coefficients. The first approach uses an impulse load and the second approach uses a step load for the corresponding adjoint equation. Since the adjoint variable methods are better for a large class of problems, an efficient computational algorithm with these methods is presented in detail. Optimum results for several problems are obtained and compared with those available in the literature. The new formulation works extremely well as precise optimum designs are obtained.     

GLOBAL ASYMPTOTIC STABILIZATION OF THE PROTOTYPICAL AEROELASTIC WING SECTION VIA TP MODEL TRANSFORMATION

A comprehensive analysis of aeroelastic systems has shown that these systems exhibit a broad class of pathological response regimes when certain types of non‐linearities are included. In this paper, we propose a design method of a state‐dependent non‐linear controller for aeroelastic systems that includes polynomial structural non‐linearities. The proposed method is based on recent numerical techniques such as the Tensor Product (TP) model transformation and the Linear Matrix Inequality (LMI) control design methods within the Parallel Distributed Compensation (PDC) frameworks. In order to link the TP model transformation and the LMI's in the proposed design method, we extend the TP model transformation with a further transformation. As an example, a controller is derived that ensures the global asymptotic stability of the prototypical aeroelastic wing section via one control surface, in contrast with previous approaches which have achieved local stability or applied additional control actuator on the purpose of achieving global stability. Numerical simulations are used to provide empirical validation of the control results. The effectiveness of the controller design is compared with a former approach.     

Discussion on the optimization problem formulation of flexible components in multibody systems

This paper is dedicated to the structural optimization of flexible components in mechanical systems modeled as multibody systems. While most of the structural optimization developments have been conducted under (quasi-)static loadings or vibration design criteria, the proposed approach aims at considering as precisely as possible the effects of dynamic loading under service conditions. Solving this problem is quite challenging and naive implementations may lead to inaccurate and unstable results. To elaborate a robust and reliable approach, the optimization problem formulation is investigated because it turns out that it is a critical point. Different optimization algorithms are also tested. To explain the efficiency of the various solution approaches, the complex nature of the design space is analyzed. Numerical applications considering the optimization of a two-arm robot subject to a trajectory tracking constraint and the optimization of a slider-crank mechanism with a cyclic dynamic loading are presented to illustrate the different concepts.     

Low-sensitivity optimal feedback control for linear discrete-time systems

The problem of reducing sensitivity of discrete-time systems to parameter variations is considered. State and output feedback gains are obtained to minimize a quadratic criterion which includes sensitivity functions. Necessary conditions for optimality are derived and numerical examples are discussed.     

Optimal design of fibre reinforced membrane structures

A design problem of finding an optimally stiff membrane structure by selecting one–dimensional fiber reinforcements is formulated and solved. The membrane model is derived in a novel manner from a particular three-dimensional linear elastic orthotropic model by appropriate assumptions. The design problem is given in the form of two minimization statements. After finite element discretization, the separate treatment of each of the two statements follows from classical results and methods of structural optimization: the stiffest orientation of reinforcing fibers coincides with principal stresses and the separate selection of density of fibers is a convex problem that can be solved by optimality criteria iterations. Numerical solutions are shown for two particular configurations. The first for a statically determined structure and the second for a statically undetermined one. The latter shows related but non-unique solutions.     

Stochastic model predictive control without terminal constraints

Sufficient conditions for the stability of stochastic model predictive control without terminal cost and terminal constraints are derived. Analogous to stability proofs in the nominal setup, we first provide results for the case of optimization over general feedback laws and exact propagation of the probability density functions of the predicted states. We highlight why these results, being based on the principle of optimality, do not directly extend to currently used computationally tractable approximations such as optimization over parameterized feedback laws and relaxation of the chance constraints. Based thereon, for both cases, stability results are derived under stronger assumptions. A third approach is presented for linear systems where propagation of the mean value and the covariance matrix of the states instead of the complete distribution is sufficient, and hence, the principle of optimality can be used again. The main results are presented for nonlinear systems along with examples and computational simplifications for linear systems.     

A sequential approximate programming strategy for reliability-based structural optimization

Although reliability-based structural optimization (RBSO) is recognized as a rational structural design philosophy that is more advantageous to deterministic optimization, most common RBSO is based on straightforward two-level approach connecting algorithms of reliability calculation and that of design optimization. This is achieved usually with an outer loop for optimization of design variables and an inner loop for reliability analysis. A number of algorithms have been proposed to reduce the computational cost of such optimizations, such as performance measure approach, semi-infinite programming, and mono-level approach. Herein the sequential approximate programming approach, which is well known in structural optimization, is extended as an efficient methodology to solve RBSO problems. In this approach, the optimum design is obtained by solving a sequence of sub-programming problems that usually consist of an approximate objective function subjected to a set of approximate constraint functions. In each sub-programming, rather than direct Taylor expansion of reliability constraints, a new formulation is introduced for approximate reliability constraints at the current design point and its linearization. The approximate reliability index and its sensitivity are obtained from a recurrence formula based on the optimality conditions for the most probable failure point (MPP). It is shown that the approximate MPP, a key component of RBSO problems, is concurrently improved during each sub-programming solution step. Through analytical models and comparative studies over complex examples, it is illustrated that our approach is efficient and that a linearized reliability index is a good approximation of the accurate reliability index. These unique features and the concurrent convergence of design optimization and reliability calculation are demonstrated with several numerical examples.     

Feasibility and optimality in structural design

Some relationships between optimal and feasible structural designs are presented. In many structural design problems, where the objective function is not sensitive to changes in the design variables, the object is to find an improved feasible design rather than the theoretical optimum. Interior optimization methods that converge to the optimum from the interior side of the acceptable domain are most suitable for this purpose. Several methods, intended to introduce improved feasible designs, are discussed in this paper. These methods are particularly useful for problems with a narrow feasible region or in cases where it is necessary to determine whether a feasible region exists. Necessary conditions for feasible solutions are derived and a procedure to convert a nonfeasible design into a feasible one by modifying the preassigned parameters is introduced. It is shown that the optimal value of the modified parameters can often be determined directly. Several examples illustrate the relationships between feasibility and optimality. It is emphasized that optimization methods are most useful in the search for improved feasible solutions for various practical design problems.     

Determination of optimal actuator forces and positions in smart structures using adjoint method

The problem of optimal design of structures with active support is analyzed in the paper. The sensitivity expressions with respect to the generalized force and the position of actuator are derived by the adjoint structure approach. Next, the optimality conditions are formulated by means of an introduced Lagrangian function. The problem of introduction of a new actuator is also considered and the condition of modification is expressed by means of the topological derivative. The obtained sensitivity formula, optimality conditions and modification conditions are applied in the optimization algorithm with respect to the number, positions and generalized forces of the actuators. Numerical examples of optimal control of beams illustrate the procedure proposed in the paper.     

Sulla ottimizzazione dei sistemi discreti

As an extension of the results of the first part of this work, necessary conditions are derived for the optimality of discrete nonlinear systems with constraint sets on the state and control variables which are «locally approximable by convex cones». The conditions are deduced geometrically from general properties of such cones and their dual cones in relation to non linear mappings an intersections of sets.     

Optimal input for discrimination of autoregressive models under output amplitude constraints

Optimal input design is considered for discriminating effectively between two rival autoregressive models when the amplitude of the system output has to be regulated within a certain tolerance limit with high certainty. This constraint is more appropriate than a power constraint when an extremely large system output may cause hazardous conditions in the system. First, conditions for the optimal input are derived based on the Ds-criterion, which corresponds to the power of the likelihood ratio test. Then two approaches are presented to construct the optimal input satisfying the conditions: one is based on the idea of a Chebyshev system, and the other is an autoregressive recursion approach. Numerical simulations illustrate the applicability of the proposed optimal input for autoregressive model discrimination.     

New approaches to relaxed stabilization conditions and H-infinity control designs for T-S fuzzy systems

This paper focuses on the problem of fuzzy control for a class of continuous-time T-S fuzzy systems.New methods of stabilization design and H infinity control are derived based on a relaxed approach in...     

An evolutionary method for optimal design of plates with discrete variable thicknesses subject to constant weight

This paper presents a simple evolutionary method for optimization of plates subject to constant weight, where design variable thicknesses are discrete. Sensitivity numbers for sizing elements are derived using optimality criteria methods. An optimal design with minimum displacement or minimum strain energy is obtained by gradually shifting material from elements to the others according to their sensitivity numbers. A simple smoothing technique is additionally employed to suppress formation of checkerboard patterns. It is shown that the proposed method can directly deal with discrete design variables. Examples are provided to show the capacity of the proposed evolutionary method for structural optimization with discrete design variables.