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This paper focuses on the fault-tolerant control (FTC) problem for an electric power steering (EPS) system subjected to stochastic sensor failures, and a novel fault-tolerant controller is proposed based on the genetic algorithm (GA). A mathematical model of the EPS system with sensor failures is first established, and the state feedback control law is solved by using linear quadratic regulator techniques to stabilize the closed-loop control system. Then, the dynamic response errors of the EPS system with and without sensor faults are chosen as the optimization objective function. Furthermore, the appropriate weighting matrices are evaluated to obtain the optimal fault control law by using GA. Finally, simulation results are presented to illustrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The traditional suction mechanism with an air pump in robotics is difficult to miniaturize. Integrating a piezoelectric pump into a suction cup is an effective method to achieve miniaturization. In this paper, a novel suction cup with a piezoelectric micropump is designed. The micropump is valveless and the suction cup is designed with a laminated structure in order to facilitate miniaturizing and manufacturing. A systematic optimization design method of the suction cup is introduced which addresses the static and dynamic driving characteristics of the piezoelectric actuator and the rectifying efficiency of diffuser/nozzle’s optimization. The design is verified via simulation using an improved equivalent electric network model. Static lumped parameters in this model are calculated by the finite element method instead of the traditional analytic method, and the diffuser/nozzle’s flow resistance is computed by integrating and introducing rectifying efficiency coefficient. Simulation results indicate that the suction cup can generate a stable negative pressure, and the equivalent electric network model can improve the simulation efficiency and accuracy. The maximum steady-state negative pressure of the suction cup can also be effectively improved after optimization. [ABSTRACT FROM AUTHOR]

Magnetorheological fluid (MRF) damper is one of the most promising semiactive devices for vibration control. In this paper, a shear-valve mode MRF damper for pipeline vibration control is proposed. The dynamic model and the state equation of the pipeline are established and the linear quadratic regulator (LQR) is used to generate the optimal damping force of MRF damper. The design concept considering the structure and the electromagnetic properties simultaneously is discussed in detail. A mathematical model of the relation between shear stress and control current based on interpolation method is established. Finite element analysis (FEA) software COMSOL is selected to simulate the magnetic field and electromagnetism-thermal field of the MRF damper. A computational method based on the simulation model is established to calculate the shear stress. In order to reduce the magnetic leakage, a method of adding magnetism-insulators at both ends of the piston head is presented. The influence of control current, displacement, and velocity on mechanical performance of the proposed MRF damper is experimentally investigated. The test results show that the performance of the MRF damper is basically identical with the theoretical prospective and the simulation conclusions, which proves the correctness and feasibility of this design concept. [ABSTRACT FROM AUTHOR]

A parameter optimal design method for a tank with an inerter system is proposed in this study based on the requirements of tank vibration control to improve the effectiveness and efficiency of vibration control. Moreover, a response indicator and a cost control indicator are selected based on the control targets for liquid storage tanks for simultaneously minimizing the dynamic response and controlling costs. These indicators are reformulated through a random vibration analysis under virtual excitation. The problem is then transformed from a multiobjective optimization problem to a single-objective nonlinear problem using the ε-constraint method, which is consistent with the demand-based method. White noise excitation can be used to design the tank with the inerter system under seismic excitation to simplify the calculation. Subsequently, a MATLAB-based calculation program is compiled, and several optimization cases are examined under different excitation conditions. The effectiveness of the demand-based method is proven through a time history analysis. The results show that specific vibration control requirements can be met at the lowest cost with a simultaneous reduction in base shears and overturning base moments. [ABSTRACT FROM AUTHOR]

Ride safety of a tracked vehicle is the key focus of this research. The factors that affect the ride safety of a vehicle are analyzed and evaluation parameters with their criteria are proposed. A multibody cosimulation approach is used to investigate the effects of hydropneumatic parameters on the ride safety and aid with design optimization and tuning of the suspension system. Based on the cosimulation environment, the vehicle multibody dynamics (MBD) model and the road model are developed using RecurDyn, which is linked to the hydropneumatic suspension model developed in Lab AMESim. Test verification of a single suspension unit is accomplished and the suspension parameters are implemented within the hydropneumatic model. Virtual tests on a G class road at different speeds are conducted. Effects of the accumulator charge pressure, damping diameter, and the track tensioning pressure on the ride safety are analyzed and quantified. This research shows that low accumulator charge pressure, improper damping diameter, and insufficient track tensioning pressure will deteriorate the ride safety. The results provide useful references for the optimal design and control of the parameters of a hydropneumatic suspension. [ABSTRACT FROM AUTHOR]

A hybrid uncertain model is introduced to deal with the uncertainties existing in a disc brake system in this paper. By the hybrid uncertain model, the uncertain parameters of the brake with enough sampling data are treated as probabilistic variables, while the uncertain parameters with limited data are treated as interval probabilistic variables whose distribution parameters are expressed as interval variables. Based on the hybrid uncertain model, the reliability-based design optimization (RBDO) of a disc brake with hybrid uncertainties is proposed to explore the optimal design for squeal reduction. In the optimization, the surrogate model of the real part of domain unstable eigenvalue of the brake system is established, and the upper bound of its expectation is adopted as the optimization objective. The lower bounds of the functions related to system stability, the mass, and the stiffness of design component are adopted as the optimization constraints. The combinational algorithm of Genetic Algorithm and Monte-Carlo method is employed to perform the optimization. The results of a numerical example demonstrate the effectiveness of the proposed optimization on improving system stability and reducing squeal propensity of a disc brake under hybrid uncertainties. [ABSTRACT FROM AUTHOR]

This paper develops a technique for identifying dynamic loads acting on a structure based on impulse response of the structure, also referred to as the system Markov parameters, and structure response measured at optimally placed sensors on the structure. Inverse Markov parameters are computed from the forward Markov parameters using a linear prediction algorithm and have the roles of input and output reversed. The applied loads are then reconstructed by convolving the inverse Markov parameters with the system response to the loads measured at optimal locations on the structure. The structure essentially acts as its own load transducer. It has been noted that the computation of inverse Markov parameters, like most other inverse problems, is ill-conditioned which causes their convolution with the measured response to become quite sensitive to errors in system modeling and response measurements. The computation of inverse Markov parameters (and thereby the quality of load estimates) depends on the locations of sensors on the structure. To ensure that the computation of inverse Markov parameters is well-conditioned, a solution approach, based on the construction of D-optimal designs, is presented to determine the optimal sensor locations such that precise load estimates are obtained. [ABSTRACT FROM AUTHOR]

This paper developed an effective optimization method, i.e., gradient-Hessian matrix-based method or second order method, of frame structures subjected to the transient loads. An algorithm of first and second derivatives of dynamic displacement and stress with respect to design variables is formulated based on the Newmark method. The inequality time-dependent constraint problem is converted into a sequence of appropriately formed time-independent unconstrained problems using the integral interior point penalty function method. The gradient and Hessian matrixes of the integral interior point penalty functions are also computed. Then the Marquardt's method is employed to solve unconstrained problems. The numerical results show that the optimal design method proposed in this paper can obtain the local optimum design of frame structures and sometimes is more efficient than the augmented Lagrange multiplier method. [ABSTRACT FROM AUTHOR]

This paper presents an integrated approach to optimize for design and control of mechanical systems with random input parameters. Random parameters are represented by probability density functions. A numerical technique defining directly the representative values and the associated probabilities is implemented by modeling the stochastic parameters as generalized Wiener processes. The Monte Carlo¡s method is also implemented to deal with correlated parameters. A design and control sensitivity analysis optimization formulation is derived. A conceptual separation between time variant and time invariant design parameters is presented, this way including the design space into the control space and considering the design variables as control variables not depending on time. By using time integrals through all the derivations, the design and control problems are unified. In the optimization process we can use both types of variables simultaneously or by interdependent levels. The dynamic response is modeled via space and time finite elements, and is integrated either by at-once integration or step-by-step. The formulation is applied to a numerical example. [ABSTRACT FROM AUTHOR]