Farrokh Sassani

Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm

This paper proposes a genetic algorithm (GA) for a redundancy allocation problem for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. Majority of the solution methods for the general redundancy allocation problems assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. Thus, the problem is to select the best redundancy strategy, component, and redundancy level for each subsystem in order to maximize the system reliability under system-level constraints. This belongs to the NP-hard class of problems. Due to its complexity, it is so difficult to optimally solve such a problem by using traditional optimization tools. It is demonstrated in this paper that GA is an efficient method for solving this type of problems. Finally, computational results for a typical scenario are presented and the robustness of the proposed algorithm is discussed.

Fast constrained global minimax optimization of robot parameters

A new global isotropy index (GII) is proposed to quantify the configuration independent isotropy of a robots Jacobian or mass matrix. A new discrete global optimization algorithm is also proposed to optimize either the GII or some local measure without placing any conditions on the objective function. The algorithm is used to establish design guidelines and a globally optimal architecture for a planar haptic interface from both a kinematic and dynamic perspective and to choose the optimum geometry for a 6-DOF Stewart Platform. The algorithm demonstrates consistent effort reductons of up to six orders of magnitude over global searching with low sensitivity to initial conditions.

Optimal kinematic design of a haptic pen

This paper investigates the performance demands of a haptic interface and shows how this information can be used to design a suitable mechanism. A design procedure, previously developed by the authors (1996), consisting of a global isotropy index and a discrete optimization algorithm, allows one to compare a range of geometric variables, actuator scale factors, and even different robot devices for optimum performance. The approach is used to compare the performance of three 6-DOF robots including two well-known parallel platform robots and a novel hybrid robot called the Twin-Pantograph in terms of their semi-dextrous workspaces and static force capabilities. Since the Twin-Pantograph yields the best results, its design is refined to address practical constraints and it is implemented as a haptic pen. The performance of the resulting design is analysed and presented.

A memetic algorithm for the flexible flow line scheduling problem with processor blocking

This paper introduces an efficient memetic algorithm (MA) combined with a novel local search engine, namely, nested variable neighbourhood search (NVNS), to solve the flexible flow line scheduling problem with processor blocking (FFLB) and without intermediate buffers. A flexible flow line consists of several processing stages in series, with or without intermediate buffers, with each stage having one or more identical parallel processors. The line produces a number of different products, and each product must be processed by at most one processor in each stage. To obtain an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches and optimization tools is extremely difficult. Our proposed MA employs a new representation, operators, and local search method to solve the above-mentioned problem. The computational results obtained in experiments demonstrate the efficiency of the proposed MA, which is significantly superior to the classical genetic algorithm (CGA) under the same conditions when the population size is increased in the CGA.

Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints

This paper presents a novel, two-level mixed-integer programming model of scheduling N jobs on M parallel machines that minimizes bi-objectives, namely the number of tardy jobs and the total completion time of all the jobs. The proposed model considers unrelated parallel machines. The jobs have non-identical due dates and ready times, and there are some precedence relations between them. Furthermore, sequence-dependent setup times, which are included in the proposed model, may be different for each machine depending on their characteristics. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches or optimization tools is extremely difficult. This paper proposes an efficient genetic algorithm (GA) to solve the bi-objective parallel machine scheduling problem. The performance of the presented model and the proposed GA is verified by a number of numerical experiments. The related results show the effectiveness of the proposed model and GA for small and large-sized problems.

A fuzzy programming approach for a cell formation problem with dynamic and uncertain conditions

This paper presents an integration of explicit uncertainty for a cell formation problem (CFP) with a dynamic condition in cellular manufacturing systems (CMS). The dynamic condition indicates a multi-period planning horizon, in which product mix and demand in each period are different. As a result, the best cells designed for one period may not be the most efficient for subsequent periods and thus require reconfigurations. Moreover, in real manufacturing systems, some input parameters are fuzzy in nature. In such cases, the fluctuation in part demand and the availability of manufacturing facilities in each period can also be regarded as fuzzy. In this paper, a fuzzy programming-based approach is developed to solve an extended mixed-integer programming model of the dynamic CFP, in which there are piecewise fuzzy numbers as coefficients in the objective function and the technological matrix. The main purpose of this paper is to determine the optimal cell configuration in each period with the maximum degree of satisfying the fuzzy objective under the given constraints. To illustrate the behavior of the proposed model and verify the performance of the developed fuzzy programming-based approach, we introduce a number of numerical examples to illustrate the use of the foregoing approach. Finally, the related computational results are reported and discussed.

Nonlinear modeling and validation of solenoid-controlled pilot-operated servovalves

Solenoid-controlled pilot-operated servovalves are flow control devices widely used to drive high-flow hydraulic actuators in heavy-duty off-highway machinery. In these applications, a spool-operated pilot stage is required to account for large flow force in the main hydraulic valve. Design optimization, performance improvement, and fault diagnosis of such complex two-stage servovalves often require sophisticated analytical models with accurate physical parameters. The paper presents a step-by-step methodology for nonlinear modeling, parameter determination, model validation, and performance evaluation of solenoid-controlled pilot-operated spool valves. The proposed model takes into account such nonlinearities as pilot spool dead band, spool friction, flow coefficient variability, and leakage. This new model is complete in the sense that all nonlinear interactions between various electromechanical elements have been incorporated in a compact yet comprehensive model. The simulation model accepts a command voltage to the solenoids as input and gives the second stage spool displacement as output. To obtain the physical parameters used in the valve model, a systematic approach is proposed based on state measurement and curve-fitting techniques. The identified parameters of an example valve were used in simulations both to validate the nonlinear valve model and also to assess the valves performance when certain valve parameters change.

Copper foil-type vibration-based electromagnetic energy harvester

This paper presents the modeling, simulation, fabrication and experimental results of a vibration-based electromagnetic power generator (EMPG). A novel, low-cost, one-mask technique is used to fabricate the planar coils and the planar spring. This fabrication technique can provide an alternative for processes such as lithographie galvanoformung abformung (LIGA) or SU-8 molding and MEMS electroplating. Commercially available copper foils of 20 µm and 350 µm thicknesses are used for the planar coils and planar spring, respectively. The design with planar coils on either side of the magnets provides enhanced power generation for the same footprint of the device. The harvesters overall volume is 1 cm3. Excitation of the EMPG, at the fundamental frequency of 371 Hz, base acceleration of 13.5 g and base amplitude of 24.4 µm, yields an open circuit voltage of 60.1 mV, as well as 46.3 mV load voltage and 10.7 µW power for a 100 Ω load resistance. At a matching impedance of 7.5 Ω the device produced a maximum power of 23.56 µW and a power density of 23.56 µW cm−3. The simulations based on the analytical model of the device show good agreement with the experimental results.

Cascade control of hydraulically actuated manipulators

A fundamental study on the control of hydraulically actuated robots is presented. Dynamic modelling is performed in both time-domain and frequency-domain. It is shown that the inclusion of hydraulic elements increases the order of the system. Hydraulic compliance is the most effective factor in this regard. Three distinct control strategies are applied. Their performances are evaluated and compared. All three methods are exemplified with a two link hydraulic robot in a computer simulation. The robot has the same hydraulic configuration as many existing industrial manipulators. The simulation program is written in ACSL ( A dvanced C ontinuous S imulation L anguage) running on a VAX 11/750.

Resolved-Mode Teleoperated Control of Heavy-Duty Hydraulic Machines

This paper addresses the control problem for a class of heavy-duty hydraulic machines in order to achieve coordinated motion control of the implement. A model and sensor-based control algorithm is proposed which is applied in conjunction with closed-loop components. The algorithm is basically a feedforward load compensating scheme which uses the measured hydraulic line pressures along with the appropriate portion of the hydraulic model to control the joint velocities