STRUCTURAL/CONTROL SYSTEM OPTIMIZATION WITH VARIABLE ACTUATOR MASSES

A method is presented to integrate the design space for structural/control system optimization problems in the case of linear state feedback control. To make the truly simultaneous optimization of structural/control systems tractable, without increasing the number of control design variables, the conventional structural design variable linking idea is extended to the control system feedback gain matrix. In this paper a new control design variable linking scheme based on representing the feedback gain matrix as the linear combination of component matrices corresponding to different modes is introduced. The original nonlinear mathematical programming problem based on a finite element formulation and linear state feedback is replaced by a sequence of explicit approximate problems exploiting various approximation concepts such as structural and control design variable linkings, temporary constraint deletion, and first-order Taylor series expansion of nonlinear behavior constraints in terms of intermediate design variables. Examples that involve a variety of dynamic behavior constraints (including constraints on closed-loop eigenvalues, peak transient displacements, and peak actuator forces) are effectively solved by using the improved control design variable linking scheme presented.

Y-stiffened panel multi-objective optimization using genetic algorithm

Many experimental situations with controllable, independent design variables have an associated null hypothesis 0 of zero dependence of observations on the design variables. The necessity of randomization of the design variables for asymptotic discriminability between 0 and its complement is considered in terms of likelihood ratios. Applications are made to stochastic processes and comparative experiments.

This paper presents an extension of fuzzy-multi-objective genetic algorithm (MOGA) optimization methodology that could effectively be used to find the overall satisfaction of objective functions (selecting the design variables) in the early stages of design process. The coupling of objective functions due to design variables in an engineering design process will result in difficulties in design optimization problems. The primary application of this methodology is the design of a liquid propellant engine with the maximum specific impulse and the minimum weight. The independent design variables in this model are combustion chamber pressure, exit pressure, oxidizer to fuel mass flow rate. To handle the mentioned problems, a fuzzy-multi-objective genetic algorithm optimization methodology is developed based on Pareto optimal set. Liquid propellant engine, F-1 is modeled to illustrate accuracy and efficiency of proposed methodology.

The stabilization problem of linear time-varying uncertain delay systems by means of linear constant memoryless state feedback control is investigated here. In the case when uncertain parameters may vary independently in arbitrarily large bounding sets, the locations of uncertain elements in the system parameters play an important role. So far obtained conditions of this problem have been found to have a particular geometric configuration with respect to the permissible locations of uncertain elements. In this paper, such a previously obtained configuration is developed to a basic form of antisymmetric stepwise configuration which is a necessary and sufficient condition for time-varying uncertain systems without delays to be quadratically stabilizable by means of linear constant state feedback control. It is shown that if a given system satisfies such developed conditions then the system is stabilizable via linear constant state feedback control with regardless to the upper bounds of time-varying uncertain parameters and time-varying delays

Integrated structural and control optimization of intelligent structures

The proposed method of this paper is based on a combination of the mathematical programming method and the optimality criteria method. It attempts to give satisfactory approximate engineering optima of large-scale structures. The concept of reduced design space is adopted to reduce time-consuming computation. The dimensionality of the reduced design space is made equal to the number of behavioral aspects of the structural system. In reduced design space the feasible direction method is employed to optimize structures and only the most critical constraint is considered, therefore, the calculation is much simplified. Basis vectors relate the original design variables to the new independent design variables in reduced design space. The quality of the basis vectors should be as good as possible and their generation should not be time-consuming. The simplified optimality criteria method is used to generate basis vectors. The method is effective for not only truss structures but also wing box structures of fixed geometry under multiple load cases and with stress, displacement and minimum-size constraints.

Biophysical stimulus quantifies the osteoinductivity of the scaffold concerning the mechanoregulatory mathematical models of scaffold-assisted cellular differentiation. Consider a set of independent structural variables ($) that comprises bulk porosity levels ([Formula: see text]) and a set of morphological features of the micro-structure ([Formula: see text]) associated with scaffolds, i.e., [Formula: see text]. The literature suggests that biophysical stimulus ([Formula: see text]) is a function of independent structural variables ($). Limited understanding of the functional correlation between biophysical stimulus and structural features results in the lack of the desired osteoinductivity in a scaffold. Consequently, it limits their broad applicability to assist bone tissue regeneration for treating critical-sized bone fractures. The literature indicates the existence of multi-dimensional independent design variable space as a probable reason for the general lack of osteoinductivity in scaffolds. For instance, known morphological features are the size, shape, orientation, continuity, and connectivity of the porous regions in the scaffold. It implies that the number of independent variables ([Formula: see text]) is more than two, i.e., [Formula: see text], which interact and influence the magnitude of [Formula: see text] in a unified manner. The efficiency of standard engineering design procedures to analyze the correlation between dependent variable ([Formula: see text]) and independent variables ($) in 3D mutually orthogonal Cartesian coordinate system diminishes proportionally with the increase in the number of independent variables ([Formula: see text]) (Deb in Optimization for engineering design-algorithms and examples, PHI Learning Private Limited, New Delhi, 2012). Therefore, there is an immediate need to devise a framework that has the potential to quantify the micro-structural's morphological features in a unified manner to increase the prospects of scaffold-assisted bone tissue regeneration.

In robot manipulators, the joint flexibility may be or not introduced intentionally. Thus, flexible joint robots (FJRs) are useful in aerospace applications and human rehabilitation, for example. Besides, FJRs appear in industrial manipulators. The feedback linearization control has been applied to many mechatronics systems, including FJRs. However, if spring damping between the links and rotors is present, the state feedback linearization design is no longer feasible. In order to overcome this situation, in this paper, an input-output feedback linearization approach is developed to achieve trajectory tracking control of FJRs. The study is complemented with simulation results, which validates the proposed theory. By assuming the presence of spring damping, a comparison between the known state feedback linearization technique and the proposed input-output feedback linearization is given, showing better results for the introduced approach.

A novel procedure is presented to design a linear constant state feedback controller with a prespecified performance measure. The method is an iterative technique based on the sensitivity of a quadratic cost functional with respect to the feedback gains. At each iteration, a Riccati-type algebraic matrix equation is solved to compute the sensitivity expressions needed to update the feedback gains.

<div class="section abstract"><div class="htmlview paragraph">The design of drive units in electric vehicles (EVs) presents challenges due to the need to pass multiple linear and non-linear load cases. This can result in inefficient design. Therefore, optimization plays a critical role in improving the design efficiency. However, setting up the optimization process itself can be challenging, especially when dealing with complex design variables and different load cases that require the use of various computer-aided engineering (CAE) solvers. The drive unit, being a casting component, presents additional challenges in setting up Multidisciplinary Design Optimization (MDO) process. This paper introduces an efficient process for addressing these challenges by presenting a sample Multidisciplinary Design Optimization (MDO) problem. The problem involves the manipulation of discrete design variables, such as the number of ribs, and incorporates five different load cases that require the utilization of different CAE solvers. The proposed process demonstrates a streamlined approach that can be completed in less than one week. It showcases the steps involved in setting up the optimization process, including defining the design variables, selecting appropriate load cases, and integrating different CAE solvers. By following this process, engineers can effectively optimize the design of drive units in EVs, leading to improved mass efficiency. Overall, this paper highlights the importance of optimization in addressing the challenges associated with the design of drive units in EVs. It presents a practical and efficient process that can be implemented within a short timeframe, allowing engineers to achieve optimal design solutions. By leveraging this approach, manufacturers can enhance the performance and efficiency of EV drive units, contributing to the advancement of electric mobility.</div></div>

This article describes the design of a linear observer–linear controller-based robust output feedback scheme for output reference trajectory tracking tasks in the case of nonlinear, multivariable, nonholonomic underactuated mobile manipulators. The proposed linear feedback scheme is based on the use of a classical linear feedback controller and suitably extended, high-gain, linear Generalized Proportional Integral (GPI) observers, thus aiding the linear feedback controllers to provide an accurate simultaneous estimation of each flat output associated phase variables and of the exogenous and perturbation inputs. This information is used in the proposed feedback controller in (a) approximate, yet close, cancelations, as lumped unstructured time-varying terms, of the influence of the highly coupled nonlinearities, and (b) the devising of proper linear output feedback control laws based on the approximate estimates of the string of phase variables associated with the flat outputs simultaneously provided by the disturbance observers. Simulations reveal the effectiveness of the proposed approach.

The (multi-player) linear quadratic state feedback control problem for index one descriptor systems

This paper concerns the problems of robust stability analysis and stabilization for uncertain linear system with time-varying delay. In order to obtain a robust stability criterion with less conservative for uncertain linear delay system, a quadratic integral inequality is improved by matrix decomposition. Further, two different kinds of controller, i.e., linear memoryless state feedback controller and linear state feedback controller with state delay, are derived in terms of the robust stability criteria. Two numeral examples are provided to demonstrate the validity of the proposed methods.

Semiglobal asymptotic stabilization of nonlinear systems with triangular zero dynamics by linear feedback