XiaoXiang Liu

Bio: XiaoXiang Liu is an academic researcher. The author has contributed to research in topics: Coupling (piping) & Nonlinear system. The author has an hindex of 1, co-authored 1 publications receiving 10 citations.

On the placement of actuators and sensors for flexible structures with closely spaced modes

Abstract: This paper analyzed the fundamental limitations of previous work and developed a new method to optimally locate actuators and sensors for structures with close modes. Optimization criteria were defined based on the distinguishing modal controllability and observability measures of close modes. An appropriate genetic algorism was adopted as the optimization algorism. Solving the high order Lyapunov functions was avoided by means of the closed-form expressions for controllability and observability Grammians. Since structure with widely separated natural frequencies is approximately balanced, computational efficiency was improved by grouping close modes together and dealing with the resulting subsystems independently. Finally, the effectiveness and optimality of the novel placement scheme were verified on a model structure with close modes.

Combined active learning Kriging with optimal saturation nonlinear vibration control for uncertain systems with both aleatory and epistemic uncertainties

Abstract: This paper addresses the vibration control problems of uncertain systems with both random and multidimensional parallelepiped (MP) convex variables by uniting the optimal saturation nonlinear control (OSNC) and an active learning Kriging (ALK) method. This method can be named ALK-MP-OSNC. The dynamic equations of the controlled systems can be written in ODE forms, and the functions containing saturation nonlinearities on the right side of each of ODE equation can be approximately replaced via using the Kriging model. The efficiency of the Kriging model can be improved through combining the differential evolution (DE) global optimal algorithm with the distance constraint condition. A three-pendulum system, a satellite motion and a moving-mass beam system are employed to demonstrate the performance of the improved ALK-MP-OSNC. Results indicates that the proposed method can efficiently drive the uncertain pendulum system to a chaotic behavior and the other two uncertain systems to a periodic motion. The efficiency and the accuracy of the proposed method can be researched through comparing with the original ALK and the Monte Carlo simulation. In conclusion, the proposed method can be applied to complex engineering fields such as aerospace engineering, civil engineering, ocean engineering, and space deployable engineering.

Real-world engineering problems: two surrogate methods for robust vibration control of moving mass-beam coupling systems with epistemic uncertainty

Abstract: The dynamic responses and the vibration control of the moving mass-beam coupling systems are one of the real-world problems in engineering fields including vehicle-bridge coupling systems and missile-gun systems. There are many uncertainty factors in the real-world engineering applications. Analyzing the effects of uncertainty on the state trajectory of the system is significant to realize the safe operation of the system. This paper develops two surrogate methods to research the evidence-theory-based robust adaptive fuzzy sliding mode control (ET-RAFSMC) of the moving mass-beam coupling systems. For the first approach, the active learning Kriging (ALK) is combined with the ET-RAFSMC to perform the evidence response analysis through an interval Monte Carlo simulation, and then a Karush-Kuhn-Tucker condition is utilized to alleviate the burden of searching the extreme responses. The other surrogate method is the Lobatto polynomial function, which integrates with a spare sampling method for improving the computational efficiency and accuracy when running the extreme value analysis of the ET-RAFSMC. The accuracy and the efficiency of the proposed methods are studied by comparing with Monte Carlo simulations as well as Legendre expansion model. Finally, the performance of the ET-RAFSMC is studied through comparing with the traditional sliding mode control.

An algorithm for LQ optimal actuator location

Abstract: The locations of the control hardware are typically a design variable in controller design for distributed parameter systems. In order to obtain the most efficient control system, the locations of control hardware as well as the feedback gain should be optimized. These optimization problems are generally non-convex. In addition, the models for these systems typically have a large number of degrees of freedom. Consequently, existing optimization schemes for optimal actuator placement may be inaccurate or computationally impractical. In this paper, the feedback control is chosen to be an optimal linear quadratic regulator. The optimal actuator location problem is reformulated as a convex optimization problem. A subgradient-based optimization scheme which leads to the global solution of the problem is used to optimize actuator locations. The optimization algorithm is applied to optimize the placement of piezoelectric actuators in vibration control of flexible structures. This method is compared with a genetic algorithm, and is observed to be faster and more accurate. Experiments are performed to verify the efficacy of optimal actuator placement.

An interval effective independence method for optimal sensor placement based on non-probabilistic approach

Abstract: This paper presents an interval effective independence method for optimal sensor placement, which contains uncertain structural information. To overcome the lack of insufficient statistic description of uncertain parameters, this paper treats uncertainties as non-probability intervals. Based on the iterative process of classical effective independence method, the proposed study considers the eliminating steps with uncertain cases. Therefore, this method with Fisher information matrix is extended to interval numbers, which could conform to actual engineering. As long as we know the bounds of uncertainties, the interval Fisher information matrix could be obtained conveniently by interval analysis technology. Moreover, due to the definition and calculation of the interval relationship, the possibilities of eliminating candidate sensors in each iterative process and the final layout of sensor placement are both presented in this paper. Finally, two numerical examples, including a five-storey shear structure and a truss structure are proposed respectively in this paper. Compared with Monte Carlo simulation, both of them can indicate the veracity of the interval effective independence method.

Damage detection of composite laminate structures using VMD of FRF contaminated by high percentage of noise

Abstract: A novel highly robust-to-noise and closely-situated eigenvalues damage detection method is proposed. The proposed method employs the Variational Mode Decomposition (VMD) algorithm to construct a new set of input signals obtained from the rows of the condensed Frequency Response Function (CFRF) to be used in a sensitivity-based model updating problem. Each row of the FRF matrix is replaced by its Unwrapped Instantaneous Hilbert Phase (UIHP). However, since the signal corresponding to the rows of the CFRF might not exhibit the mono-component property, and thus the UIHP will not be well-defined, VMD is used to obtain a set of constructive mono-component modes for each row, whereby the sum of UIHPs (SUIHP) for that row is obtained. The obtained SUIHPs for all rows of the CFRF are stacked up to obtain a new matrix to be fed into the optimisation problem. The proposed method is tested on a composite laminate plate with different configurations, as an example of structures with closely-situated eigenvalues. The results of the application of highly noisy measurement data for damage detection as well as comparison with two other methods demonstrate the superiority of the proposed method in damage detection of structures with closely-situated eigenvalues using highly noisy input data.

Using incomplete FRF measurements for damage detection of structures with closely-spaced eigenvalues

Abstract: In this paper, a new model updating-based damage detection method based on a modified sensitivity equation is proposed that can tackle the problem of damage detection in structures with closely-spaced eigenvalues. It is known that modal information, such as natural frequencies, in these structures can be of close proximity, making the procedure of damage detection hard if not impossible. The obtained sensitivity equation uses incomplete measurements from frequency response functions (FRFs) to conduct the challenging damage detection of structures with closely-spaced eigenvalues. Although using FRF for damage detection of this kind of structures can offer some advantages over modal data, there are still some challenges that need to be addressed. For instance, it is not possible to have the response of the structure measured in all of its degrees of freedom. As such, the proposed FRF-based model updating method is capable of overcoming these limitations and has the advantage of avoiding modal analysis errors. In order to evaluate the efficiency of the proposed method, two numerical examples of a 144-element three-layered laminated composite plate and a 120-element three-dimensional truss structure, as examples of structures with closely-spaced eigenvalues, are studied. The results demonstrate the capability of the proposed method in damage detection of structures with closely-spaced eigenvalues with incomplete measurement data. Moreover, the results of comparison between the proposed method with some other methods demonstrate the superiority of the proposed method in damage detection of structures with closely-spaced eigenvalues using noisy incomplete FRF data.