Robust Design Scheme Research Articles

Self-Assembled and Nonassembled Alkylated-Fullerene Materials.

Fullerene (C60), a π-conjugated cage molecule consisting of 60 sp2-hybridized carbon atoms that are arranged into perfect icosahedral symmetry, is one of the most extensively studied nanocarbon materials by virtue of its characteristic spherical structure, fascinating optoelectronic properties, and widespread applications in material science. To implement practical applications, C60 is generally used as a building motif to assemble into various ordered superstructures. Unlike the controllable face-to-face π-π interactions of planar π-conjugated molecules, the π-π interactions between the three-dimensional spherical C60 units are random and directionless, which generally lead to complicated aggregated structures and unpredictable properties. The primary target of our research is to produce a robust design strategy for functional C60 materials, by which the single C60 molecules can be engineered into desirable self-organized architectures with optimized functions. To this end, we focused on alkylated fullerene (alk-C60) derivatives, a simple molecular system whose two components, alkyl chains and C60, exhibit both hydrophobicity yet different affinities to organic solvents. As a result, the alk-C60 derivatives present an unusual "hydrophobic amphiphile" system. Through systematic tuning of the substitution pattern of a series of alkyl side chains (number, length, branching, and substitution position) and external experimental conditions, the factors influencing alk-C60 self-assembly behaviors were determined. In addition, the feasibility of forming hybrid coassemblies with alk-C60 and other nanocarbon materials was demonstrated. By taking full advantage of the hydrophobic nature and active optoelectronic properties of these self- or hybrid-assemblies, various superhydrophobic materials and/or optoelectronic devices were developed. However, supported only by weak noncovalent interactions, these ordered superstructures are intrinsically fragile under various external stimuli. To improve the structural stability and achieve consistent optoelectronic performance of these novel materials, we strengthened the ordered structures via metallization and plasticization. Both approaches gave rise to robust and endurable materials with functions inherited from the pristine assemblies but at the cost of their former softness and facile processability. Thereafter, we focused on amorphous materials in view of their consistent and predictable optoelectronic properties that are independent of their geometry and physical environment. Unexpectedly, the amorphous materials obtained were liquids at room temperature, whose excellent deformability might enable applications in flexible/wearable optoelectronic devices. However, the lack of sufficient molecular order impaired their optoelectronic performance. To address this, we devised a straightforward strategy toward the directed ordered self-assembly of the alk-C60 liquids by adding molecular cofactors (n-alkanes or C60) into the liquids. Using this strategy, the balance between intermolecular order and material softness can be readily adjusted to meet different application requirements. Through iterative refinements to our novel alk-C60 system, we have demonstrated its power in generating numerous self-assembled, hybrid-assembled, and nonassembled materials toward versatile applications. We believe such a comprehensive description of these alk-C60-based functional materials provides deep insights into these still-evolving materials, which will underpin more advanced applications in near future.

Mitigation and Recovery From Cascading Failures in Interdependent Networks Under Uncertainty

The interdependence of multiple networks makes today's infrastructures more vulnerable to failures. Prior works mainly focused on robust network design and recovery strategies after failures, given complete knowledge of failure location. Nevertheless, in real-world scenarios, the location of failures might be unknown or only partially known. In this paper, we focus on cascading failures involving the power grid and its communication network with imprecision in failure assessment. We consider a model where functionality of the power grid and its failure assessment rely on the operation of a monitoring system and vice versa. We address ongoing cascading failures with a twofold approach: first, once a cascading failure is detected, we limit further propagation by redispatching generation and shedding loads; and second, we formulate a recovery plan to maximize the total amount of load served during the recovery intervention. We performed extensive simulations on real network topologies showing the effectiveness of the proposed approach in terms of number of disrupted power lines and total served load.

Based on fuzzy-approximation adaptive backstepping control method for dual-arm of humanoid robot with trajectory tracking

In this article, a fuzzy-approximation-based adaptive backstepping control method for dual-arm of a humanoid robot was proposed. The purpose of this control system is to provide coordinated movement assistance to enable the humanoid robot’s human-like forearm to grab objects coordinately (or track any continuous desired trajectory), even in the presence of environmental disturbances and parametric uncertainties. We analyze the proposed adaptive backstepping by mathematical modeling and actually measure the robot dual-arm motion information of a number of case when they simulate the trajectory to verify the model. We design the adaptive fuzzy-approximation control strategy and combining the synthesis of the robust design, backstepping control, and Lyapunov function method, the proposed adaptive fuzzy backstepping control does not need to know the humanoid robot’s arms model precisely. In the control system proposed here, once the desired trajectories of the robot’s dual-arm positions are given, the adaptive fuzzy system was closed to any unknown functions and to the derivative of the virtual control law of the humanoid robot system. In this case, a robust design scheme was utilized to compensate for any approximation errors. With the proposed trajectory tracking, not only able to generate the coordinate motions for a humanoid robot’s two arms, but it can also control the arms to move to the desired positions. The proposed closed-loop system under the adaptive fuzzy backstepping control design was effective and that asymptotic stability was successfully achieved. The adaptive fuzzy-approximation backstepping control strategy should be more complete and intelligent and more actual test should be conducted to further evaluate the effect of the proposed trajectory tracking. The instability of dual-arm of humanoid robot system is systematically analyzed and a backstepping control strategy based on the adaptive fuzzy-approximation to improve the continuity of trajectory tracking of the robot’s arms is proposed.

An innovative type of forest insurance in China based on the robust approach

Lack of a differential premium scheme is a major factor restricting the development of China's forest insurance. In this paper, we tackle the forest insurance design problem. By establishing an insurance game model among government, insurance company, and foresters, we first derive an innovative type of insurance based on the value of forest stock volume. This type of insurance has a more flexible pricing space and less dependence on government subsidy. Next, taking the disaster probability and its likely unpredictability into account, we propose a robust premium design strategy based on the min-max regret criterion where the disaster probability can assume any real value within a given interval. Then, we introduce a computational experiment to explain the relationship between the characteristics of forestry (e.g., forest value, government subsidies, understanding of the forest insurance, attention on disaster probability, and variability of the annual disaster probability) and the efficiency of the proposed insurance. The experiment also reveals that the performance of the proposed insurance in an average scenario is much improved.

Distributed low-complexity fault-tolerant consensus tracking of switched uncertain nonlinear pure-feedback multi-agent systems under asynchronous switching

This paper addresses a distributed low-complexity design problem for ensuring preassigned fault-tolerant consensus tracking quality of uncertain switched nonlinear pure-feedback multi-agent systems under arbitrary and asynchronous switching. Switched non-affine nonlinearities and unexpected nonlinear and actuator faults of each system are assumed to be unknown. Compared with existing results in the literature, the main contribution of this paper is to provide a simplified robust control strategy to deal with asynchronously switched nonlinearities and faults among systems in the consensus tracking field. Using nonlinearly transformed error surfaces and the common Lyapunov function method, a common robust consensus tracking design strategy is established to ensure the preassigned fault-tolerant consensus tracking performance and the system reliability on the switched faults, without using any adaptive fault compensation techniques based on neural networks or fuzzy logic systems. It is shown that the distributed consensus tracking errors remain within preselected bounds even at switching and fault occurrence instants and finally converge to a preselected neighborhood of the origin.

Robust Sampled-Data Bilateral Teleoperation: Single-Rate and Multirate Stabilization

This paper investigates robust sampled-data control design strategies for nonlinear bilateral teleoperators exposed to communication constraints, using discrete-time approximate models of the master and slave robots and assuming both single-rate and multirate sampling. The single-rate design guarantees input-to-state stability of the (unknown) exact discrete-time model in a semiglobal practical sense, in spite of norm-bounded channel uncertainties. Then imposing different sampling rates on positions/velocities or torque inputs in each side of the system, a multirate scheme is proposed and shown to maintain similar stability properties under a well-known Nyquist frequency assumption on control torques. Simulation results verify the advantages of our direct sampled-data design. The example also shows that the multirate controller successfully stabilizes the teleoperation system even when the single-rate bilateral setup is ineffective in the presence of different sampling rates.

A Continuum Model for Fiber-Reinforced Soft Robot Actuators

Fiber-reinforced elastomeric enclosures (FREEs) generate sophisticated motions, when pressurized, including axial rotation, extension, and compression, and serve as fundamental building blocks for soft robots in a variety of applications. However, most modeling techniques employed by researchers do not capture the key characteristics of FREEs to enable development of robust design and control schemes. Accurate and computationally efficient models that capture the nonlinearity of fibers and elastomeric components are needed. This paper presents a continuum model that captures the nonlinearities of the fiber and elastomer components as well as nonlinear relationship between applied pressure, deformation, and output forces and torque. One of the key attributes of this model is that it captures the behavior of FREEs in a computationally tractable manner with a minimum burden on experimental parameter determination. Without losing generality of the model, we validate it for a FREE with one fiber family, which is the simplest system exhibiting a combination of elongation and twist when pressurized. Experimental data in multiple kinematic configurations show agreement between our model prediction and the moments that the actuators generate. The model can be used to not only determine operational parameters but also to solve inverse problems, i.e., in design synthesis.

Probabilistic Constrained Approach for Distributed Robust Beamforming Design in Cognitive Two-way Relay Networks

In this paper, we propose the distributed robust beamforming design scheme in cognitive two-way amplify-and-forward (AF) relay networks with imperfect channel state information (CSI). Assuming the CSI errors follow a complex Gaussian distribution, the objective of this paper is to design the robust beamformer which minimizes the total transmit power of the collaborative relays. This design will guarantee the outage probability of signal-to- interference-plus-noise ratio (SINR) beyond a target level at each secondary user (SU), and satisfies the outage probability of interference generated on the primary user (PU) above the predetermined maximum tolerable interference power. Due to the multiple CSI uncertainties in the two-way transmission, the probabilistic constrained optimization problem is intractable and difficult to obtain a closed-form solution. To deal with this, we reformulate the problem to the standard form through a series of matrix transformations. We then accomplish the problem by using the probabilistic approach based on two sorts of Bernstein-type inequalities and the worst-case approach based on S-Procedure. The simulation results indicate that the robust beamforming designs based on the probabilistic method and the worst-case method are both robust to the CSI errors. Meanwhile, the probabilistic method can provide higher feasibility rate and consumes less power.

Decentralized Robust Transceiver Designs for MISO SWIPT Interference Channel

This paper considers a $K$ -user multiple-input single-output interference channel for simultaneous wireless information and power transfer, where each multi-antenna transmitter serves a single-antenna receiver per user pair. All receivers perform simultaneously information processing and energy harvesting (EH) based on the receive power-splitting (PS) architectures. Assuming imperfect channel state information (CSI) at the transmitters, we develop a near-optimal robust transceiver design scheme that minimizes the total transmission power under the worst-case signal-to-interference-plus-noise ratio (SINR) and EH constraints at the receivers, by jointly optimizing transmit beamforming and receive PS ratio per receiver. When the CSI uncertainties are bounded by ellipsoidal regions, it is shown that the worst-case SINR and EH constraints per receiver can be recast into quadratic matrix inequality forms. Leveraging semidefinite relaxation technique, the intended robust beamforming and PS (BFPS) problem can be relaxed as a tractable (centralized) semidefinite program. More importantly, relying on the state-of-the-art alternating direction method of multipliers in convex optimization, we propose a decentralized algorithm capable of computing the robust BFPS scheme with local CSI and limited information exchange among the transmitters. It is shown that the proposed decentralized algorithm is guaranteed to converge to the centralized solution. Numerical results are provided to demonstrate the merits of the proposed approaches.

A Taylor Expansion‐Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

AbstractGlobal sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap‐to‐evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we develop a Taylor expansion‐based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD‐based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.

Geometry of thresholdless active flow in nematic microfluidics

"Active nematics" are orientationally ordered but apolar fluids composed of interacting constituents individually powered by an internal source of energy. When activity exceeds a system-size dependent threshold, spatially uniform active apolar fluids undergo a hydrodynamic instability leading to spontaneous macroscopic fluid flow. Here, we show that a special class of spatially non-uniform configurations of such active apolar fluids display laminar (i.e., time-independent) flow even for arbitrarily small activity. We also show that two-dimensional active nematics confined on a surface of non-vanishing Gaussian curvature must necessarily experience a non-vanishing active force. This general conclusion follows from a key result of differential geometry: geodesics must converge or diverge on surfaces with non-zero Gaussian curvature. We derive the conditions under which such curvature-induced active forces generate "thresholdless flow" for two-dimensional curved shells. We then extend our analysis to bulk systems and show how to induce thresholdless active flow by controlling the curvature of confining surfaces, external fields, or both. The resulting laminar flow fields are determined analytically in three experimentally realizable configurations that exemplify this general phenomenon: i) toroidal shells with planar alignment, ii) a cylinder with non-planar boundary conditions, and iii) a "Frederiks cell" that functions like a pump without moving parts. Our work suggests a robust design strategy for active microfluidic chips and could be tested with the recently discovered "living liquid crystals".

An optimal vibration control strategy for robust active suspension systems design with actuator delays and uncertainties

An optimal vibration control strategy for robust active suspension systems design with actuator delays and uncertainties

Robust Stabilization of Inverted Pendulum Using ALQR Augmented by Second-Order Sliding Mode Control

In this paper, a robust nonlinear control design strategy to solve the stabilization problem of an inverted pendulum system subject to parametric uncertainties and unmodeled dynamics is proposed. The control strategy is based on the combination of Amplified Linear Quadratic Regulator (ALQR) control with a high-order sliding mode algorithm. Differently from the standard ALQR controller, parametric uncertainties are considered in the design process. Linear matrix inequality conditions are provided to deal with the computational issues arising with the inclusion of this feature. A sliding mode term is added to the ALQR control law to mitigate the effect of unmodeled dynamics, such as dry friction, neglected in the system model. In order to prevent the occurrence of chattering, a high-order sliding mode approach was used, namely the second-order super-twisting algorithm. The effectiveness of the proposed strategy is evaluated through a real experiment performed using the Quanser inverted pendulum plant.

Robust input design for nonlinear dynamic modeling of AUV

Input design has a dominant role in developing the dynamic model of autonomous underwater vehicles (AUVs) through system identification. Optimal input design is the process of generating informative inputs that can be used to generate the good quality dynamic model of AUVs. In a problem with optimal input design, the desired input signal depends on the unknown system which is intended to be identified. In this paper, the input design approach which is robust to uncertainties in model parameters is used. The Bayesian robust design strategy is applied to design input signals for dynamic modeling of AUVs. The employed approach can design multiple inputs and apply constraints on an AUV system’s inputs and outputs. Particle swarm optimization (PSO) is employed to solve the constraint robust optimization problem. The presented algorithm is used for designing the input signals for an AUV, and the estimate obtained by robust input design is compared with that of the optimal input design. According to the results, proposed input design can satisfy both robustness of constraints and optimality.

Bayesian D‐optimal designs for error‐in‐variables models

Bayesian optimality criteria provide a robust design strategy to parameter misspecification. We develop an approximate design theory for Bayesian D‐optimality for nonlinear regression models with covariates subject to measurement errors. Both maximum likelihood and least squares estimation are studied, and explicit characterisations of the Bayesian D‐optimal saturated designs for the Michaelis–Menten, Emax and exponential regression models are provided. Several data examples are considered for the case of no preference for specific parameter values, where Bayesian D‐optimal saturated designs are calculated using the uniform prior and compared with several other designs, including the corresponding locally D‐optimal designs, which are often used in practice. Copyright © 2017 John Wiley & Sons, Ltd.

Robust Design Strategy for Multiple Tuned Mass Dampers with Consideration of Frequency Bandwidth

The tuned mass damper (TMD) has been widely used in vibration control of civil engineering structures after numerous analytical studies and experimental verifications. A large number of TMDs have been implemented in real structures against natural and man-made excitations since the early 1970. From field observations, it was found that the control efficacy of a TMD may decay significantly when its frequency is not well tuned to the desired value. A multiple TMD (MTMD) system was proposed to tune a band of frequencies, thereby enhancing the vibration control efficiency. Frequency detuning usually results from structural system variations due to errors in modeling, construction, and system parameter identification. In this study, the frequency detuning effect due to uncertainties of the controlled system parameters on the performance of the MTMDs is investigated. A sensitivity analysis was first carried out to evaluate the control effectiveness of a MTMD system designed by a conventional method. A new robust design method with account taken of frequency bandwidth is proposed to alleviate the detuning effect. Numerical analysis is performed to verify the applicability and efficacy of the proposed robust design method.

Rational design of reconfigurable prismatic architected materials.

Advances in fabrication technologies are enabling the production of architected materials with unprecedented properties. Most such materials are characterized by a fixed geometry, but in the design of some materials it is possible to incorporate internal mechanisms capable of reconfiguring their spatial architecture, and in this way to enable tunable functionality. Inspired by the structural diversity and foldability of the prismatic geometries that can be constructed using the snapology origami technique, here we introduce a robust design strategy based on space-filling tessellations of polyhedra to create three-dimensional reconfigurable materials comprising a periodic assembly of rigid plates and elastic hinges. Guided by numerical analysis and physical prototypes, we systematically explore the mobility of the designed structures and identify a wide range of qualitatively different deformations and internal rearrangements. Given that the underlying principles are scale-independent, our strategy can be applied to the design of the next generation of reconfigurable structures and materials, ranging from metre-scale transformable architectures to nanometre-scale tunable photonic systems.

Evolutionary Reliable Regional Kriging Surrogate and Soft Outer Array for Robust Engineering Optimization

Robust optimization seeks designs with optimized performance and low sensitivity to possible variations in a product’s life-cycle. As a popular robust design scheme in industry, Taguchi method uses the signal-to-noise ratio (SNR) as a metric of robustness. However, the Taguchi experimental design includes an inner orthogonal arrays (OA) for control factors and an outer OA for noise factors in estimating SNR-based robustness, raising a serious cost concern, especially if expensive samples are involved. Furthermore, rigorous control of noise factors to prespecified levels in the outer OA is impractical in engineering applications. This paper presents a novel approach, robust optimization using evolving reliable Kriging surrogate (ROERKS) that uses an evolving surrogate model to approximate the actual system, and uses a soft outer array to estimate the robustness. Both control variables and noise factors are merged into a combined experimental design served as the training samples to construct a Kriging-based surrogate model. An evolutionary optimizer is applied to search of the subspace of the design variables for a robust optimal solution, and a soft outer array is introduced to estimate the fitness function consisted of the mean and the variance response of evolving individual. To accommodate reduced accuracy of the surrogate model owing to an inadequate sample size, an issue that commonly arises in expensive optimization, the proposed algorithm uses a reliable region to constrain the genetic algorithm search. The verification result of the quasi-optimum is then added to the training samples to refine the evolving surrogate and to adjust the reliable region. A hybrid infilling strategy is then introduced to prevent the early convergence of the quasi-optimum. If the predicted optimum is in close proximity to current samples, the infilling strategy switches to an alternative sample with maximum expected improvement to improve sample efficiency. The iteration process continues until it converges to a robust optimum. The robust optimization of a numerical example for minimization and the robust optimization of a micro accelerometer design with a nominal-the-best sensitivity are presented to demonstrate the effectiveness of the proposed method. ROERKS outperforms Taguchi method and the RSM-based robust optimization, and derives a superior robust optimum using many fewer experiments.

A novel robust design method for the sense mode of a MEMS vibratory gyroscope based on fuzzy reliability and Taguchi design

This paper proposes a novel robust design method for the sense mode of a MEMS vibratory gyroscope based on fuzzy reliability and Taguchi design. The principles of fuzzy reliability and Taguchi design are both introduced and described in detail. Experimental results demonstrate that the signal to noise ratio of the robust design scheme is better than those of the other experimental schemes. Over the full temperature range from −40 to 80°C, the temperature sensitivities of phase margin, gain margin, sensitivity margin, the maximum amplitude of open loop system, bandwidth of closed loop system, and the performance function of the robust design system are all smaller than those of the original design system. Meanwhile, the temperature sensitivity of the bandwidth of the robust design system is improved to 126 from 1075 ppm/°C. Moreover, the bias drift over the full temperature range of the robust design system is improved to 61°/h from 179°/h.

Adjoint operator‐based isomorphism realisation and control design for non‐linear systems

In this study, a realisation to robust right coprime factorisation and robust stability of non-linear systems with unknown bounded perturbations are investigated based on inner product of Hilbert spaces. In detail, first, a feasible framework based on inner product is proposed to study robust right factorisation of the perturbed non-linear systems, which provides fundamental for factorising the systems and guaranteeing robust stability. Second, a condition based on adjoint operators of Hilbert spaces is given for the non-linear systems with unknown bounded perturbations, according to which a compensator is designed and meanwhile eliminates difficulties in obtaining internal signal of the perturbed non-linear systems. After that, a realisable design scheme on robust stability is given based on the designed controller and the unimordular property. According to the proposed robust design scheme, the non-linear systems with unknown bounded perturbations can be handled precisely and effectively. Finally, a simulation example is given to confirm the effectiveness of the proposed methods.