A novel technique for optimal placement of piezoelectric actuators on smart structures

The problem of positioning of actuators and sensors on smart materials has been a point of interest in recent years. This is due to the fact that in many practical applications there are limitations in space, weight, etc. of the smart structures, which make the problem of positioning more complex. In addition, it is required that the actuators/sensors have the best possible performance. The development of smart structures technology in recent years has provided numerous opportunities for vibration control applications. The use of piezoelectric ceramics or polymers has shown great promise in the development of this technology. The employment of piezoelectric material as actuators in vibration control is beneficial because these actuators only excite the elastic modes of the structures without exciting the rigid-body modes. This is important since very often only elastic motions of the structures are needed to be controlled. The purpose of this paper is to introduce a novel approach developed for optimizing the location of piezoelectric actuators for vibration suppression of flexible structures. A flexible fin with bonded piezoelectric actuators is considered in this study. The frequency response function (FRF) of the system is then recorded and maximization of the FRF peaks is considered as the objective function of the optimization algorithm to find the optimal placement of the piezoelectric actuators on the smart fin. Three multi-layer perceptron neural networks are employed to perform surface fitting to the discrete data generated by the finite element method (FEM). Invasive weed optimization (IWO), a novel numerical stochastic optimization algorithm, is then employed to maximize the weighted summation of FRF peaks. Results indicate an accurate surface fitting for the FRF peak data and an optimal placement of the piezoelectric actuators for vibration suppression is achieved.     

Structural control to minimize the dynamic response of Mindlin—Timoshenko plates

The problem of damping out the vibrations of a thick plate is solved using the optimal control theory of distributed parameter systems. The plate is modelled as a Mindlin- Timoshenko plate to include shear effects and may exhibit viscous damping. The dynamic response of the structure comprises the displacement and velocity components which are combined with the amount of force expended in controlling the motion in a multiobjective cost functional. This functional is minimized with respect to distributed controls. A maximum principle is formulated to relate the control forces to adjoint variables, the use of which leads to the explicit solution of the title problem. The control over the plate is exercised by distributed moment and transverse forces which are in practice applied by means of torque and force actuators.     

Agile attitude maneuver with active vibration-suppression for flexible spacecraft

This paper addresses the agile attitude maneuver of flexible spacecraft using control moment gyros without modal information. Here, piezoelectric actuators are employed to actively suppress the vibration of flexible appendages. Both the dynamics and the proposed controller are globally developed on the Special Orthogonal Group SO(3), avoiding ambiguities and singularities associated with other attitude representations. More specifically, an observer is first designed to estimate the modal information of vibration. A robust control law is developed by synthesizing a proportional-derivative (PD) controller, an adaptive sliding mode controller, and an active vibration-suppression controller, which use the information of the estimated structural modes. The stability of the closed-loop system is proved using Lyapunov stability theory. Finally, numerical examples are performed to show the effectiveness of the proposed method.     

Adaptive neural finite-time control for space circumnavigation mission with uncertain input constraints

This paper explores the design of an anti-saturation adaptive finite-time control strategy with the neural network (NN) technique for the space circumnavigation mission. Before executing the controller design, the analytical solutions of the desired angular velocity and its derivative of the active spacecraft are calculated. Since there are uncertain saturation constraints on control forces and moments in the actual propulsion system, an auxiliary system compensated by an adaptive NN is adopted. The modified auxiliary system no longer needs the precise output values of the actuators. Besides, the hyperbolic tangent function is introduced to design the weight update law for the NN compensator, so that the derivative of the weight estimator will not be amplified by the quadratic of states when the system states are large. It is proved that tracking errors of the system states can converge to a residual set of the origin in finite time. Simulation results show that the maximum amplitudes of the control signals are greatly reduced compared to the classical non-singular terminal sliding-mode control scheme, and that the neural-based compensator can significantly weaken the overshoot and chattering.     

Vibration control of a membrane antenna structure using cable actuators

This paper presents a control strategy of using cable actuators to control the vibration of a membrane antenna structure. The tension cables of the membrane antenna structure are used as actuators, the vibration of the structure can be suppressed by controlling the tension force in the cable actuators. First, the antenna structure with cable actuators is discretized by the finite element method (FEM), and governing equation of the whole structure is established. Then, a controller is designed based on the Lyapunov's stability theory, and the mechanism of this controller is studied through a simple single-degree-of-freedom (SDOF) system. The optimal placement of the cable actuators is also studied numerically in this paper. Simulation results indicate that vibration of the membrane antenna structure can be suppressed effectively by the cable actuators, and optimally placed cable actuators can produce better control effect with smaller control input.     

Time-varying formation control for nonlinear multi-agent systems against actuator attacks

The formation control problem with time-varying characteristics is investigated for the time-delayed nonlinear multi-agent systems against actuator attacks. A neural-network-based adaptive control method is constructed to achieve the desired control objective, which is outputs of the followers can complete the desired transformation of formation configuration. To eliminate the influence of malicious attacks on the actuators, an actuator attacks defense strategy is proposed to resist false data injection attacks occurred in the actuator. The uncertainty of the dynamics caused by nonlinear functions is resolved by the neural-network approximate method. The problem of the time delay is handled by an improved Lyapunov-Krasovskii functional approach, which can also avoid the singularity problem that may occur during the construction of the control method. Based on the Lyapunov stability theory, it is proved that all signals of closed-loop systems are semi-globally stable and the formation error can converge to a small neighborhood of the origin. Finally, the results of simulations are provided to verify the feasibility of the theoretical analysis and the effectiveness of the proposed control method.     

Precise robust motion tracking of a piezoactuated micropuncture mechanism with sliding mode control

This paper proposes a novel model-based control scheme to achieve the precise robust motion control of a piezoactuated micropuncture mechanism for cell injection. Using the Bouc–Wen model, the hysteretic dynamic model of the micropuncture mechanism is constructed, and its local optimization is conducted to facilitate engineering applications. On the basis of this model, a controller that synthesizes a fast nonsingular terminal sliding mode (FNTSM) control and time-delay estimation (TDE) is constructed. The control law for FNTSM has the advantages of continuous output, absence of chatter, and finite-time convergence of tracking error. The unknown quantity for TDE technology can be estimated and compensated online to reduce the FNTSM gain. Experiments on the micropuncture mechanism demonstrate that the developed control scheme provides smaller tracking error than the delay-control strategy based on the linear-error dynamic model or the model-free control scheme (e.g., Jin and Hsia’s controller). Micropuncture experiments on zebrafish embryo are successfully completed. Moreover, from the practical aspects, the control scheme developed herein can be effectively implemented in other types of micro-operation mechanisms driven by piezoelectric actuators.     

Extended dissipative analysis for aircraft flight control systems with random nonlinear actuator fault via non-fragile sampled-data control

This paper addresses the issue of reliable feedback control of an uncertain aircraft flight control systems with disturbances via non-fragile sampled-data control approach. In particular, the parameter uncertainties are assumed to be randomly occurring which is described by the Bernoulli distributed sequences. By constructing a suitable Lyapunov–Krasovskii functional together with Wirtinger-based inequality, a new set of sufficient conditions in terms of linear matrix inequalities is obtained to ensure the asymptotic stability and extended dissipativity of the aircraft flight control systems not only when all actuators are operational, but also in case of some actuator failures. Finally, simulation results are conducted to validate the effectiveness of the proposed control design technique.     

Efficient point-by-point engine calibration using machine learning and sequential design of experiment strategies

Modern engines are controlled by electronic control units, which operate all the engine actuators based on the signals from various sensors in the engine. Traditionally, the control parameters of the actuators are obtained through huge amount of trial-and-error experiments. However, using traditional approach to calibrate these parameters becomes more challenging with the increasing incorporation of new technologies into advanced engines. In order to reduce the number of experiments required in the calibration process of modern engines, a novel point-by-point engine calibration approach based on machine learning methods is proposed in this study. It is an iterative procedure that, for a given operating point, sequential design of experiment (DoE) strategy is utilized to measure the responses of different engine sensors corresponding to different actuator signals, and a machine learning algorithm called initial-training-free online extreme learning machine is utilized to incrementally learn the relationship between the sensors and actuators based on the measurement acquired. In each iterative cycle, meta-heuristic optimization is performed on the machine-learning-based model to search for the best parameters, which are then used as the initial parameters for generating DoE plan of the next cycle. The iteration is repeated until the optimal parameters of that operating point are found. To verify the effectiveness of the proposed approach, experiments on both simulation engine in commercial software and real engine in test bench have been conducted. The results show that the engine calibration can be carried out with significant fewer experiments and time by using the proposed approach.     

Substructural control of fuzzy nonlinear flexible structures

It is advantageous to use the substructural and/or decentralized techniques in structural control to save on computations and time. In this paper, a generalized substructural approach is presented in the control of fuzzy nonlinear flexible structures with discrete sensors/actuators. The substructural control scheme is developed using the static condensation technique together with the LQG control method. The subcontrollers and subobservers designed at substructure levels are used to assemble the global controller and observer for the whole structure. Nonlinear effects are included in the structural formulations and a fuzzy methodology is adopted for handling the imprecision present in the structure modeling. The nonlinear and fuzzy schemes are applied to one structural control problem to illustrate the accuracy and capability of the substructural control technique.     

Active vibration control of an elastically connected double-string continuous system

The paper deals with the optimal control of a distributed host structure consisting of two elastically connected complex continuous double-string system and subjected to certain excitation load. Investigation of the behavior of such system is of great theoretical and practical importance. A technique is proposed to actively damp out the undesired vibrations in the structures by a combination of applied actuators and displacement feedback gains. Two performance measures, involving energies at the terminal time as well as applied and feedback control efforts, are introduced. The optimality conditions of the applied actuators are derived by using the method of eigenfunction expansion and calculus of variations. The feedback parameters are numerically determined from the solution of a minimization problem. The proposed approach is illustrated by a numerical example involving a system which consists of two strings subjected to a continuous load.     

Boundary optimal control of structural vibrations in an annular plate

A maximum principle is formulated and validated for the vibration control of an annulus plate with the control forces acting on the boundary. In addition, the maximum principle can be applied to plates with multiply connected domains. The performance index is specified as a quadratic functional of displacement and velocity along with a suitable penalty term involving the control forces. Using this index an explicit control law is derived with the help of an adjoint variable satisfying the adjoint differential equation and certain terminal conditions together with the proposed maximum principle. The implementation of the theory is presented and the effectiveness of the boundary control is investigated by a numerical example.     

极大望远镜基于FPGA的大批量位移促动器实时高精度控制的研究

运用现场可编程门阵列(FPGA)并发执行的特点,提出一种并行实时控制方法,其核心在于构建产生位移促动器控制信号的模块.重点介绍了如何使用底层硬件语言构建控制模块,并对它进行功能仿真.对控制系统硬件平台和软件平台的实现也做了一定介绍.     

Piezoelectric-driven droplet impact printing with an interchangeable microfluidic cartridge

Microfluidic impact printing has been recently introduced, utilizing its nature of simple device architecture, low cost, non-contamination, and scalable multiplexability and high throughput. In this paper, we have introduced an impact-based droplet printing platform utilizing a simple plug-and-play microfluidic cartridge driven by piezoelectric actuators. Such a customizable printing system allows for ultrafine control of droplet volume from picoliters (∼23 pl) to nanoliters (∼10 nl), a 500 fold variation. The high flexibility of droplet generation can be simply achieved by controlling the magnitude of actuation (e.g., driving voltage) and the waveform shape of actuation pulses, in addition to nozzle size restrictions. Detailed printing characterizations on these parameters have been conducted consecutively. A multiplexed impact printing system has been prototyped and demonstrated to provide the functions of single-droplet jetting and droplet multiplexing as well as concentration gradient generation. Moreover, a generic biological assay has also been tested and validated on this printing platform. Therefore, the microfluidic droplet printing system could be of potential value to establish multiplexed micro reactors for high-throughput life science applications.     

Output feedback control of electro-hydraulic servo actuators with matched and mismatched disturbances rejection

In this paper, two output feedback controllers are proposed for motion control of double-rod electro-hydraulic servo actuators with matched and mismatched disturbances rejection. All of them employ an linear extended state observer (LESO) to achieve real-time estimates of the unmeasured system states and matched disturbance, and a nonlinear disturbance observer (NDO) to estimate the largely unknown mismatched disturbance at the same time. Thus, the disturbances are compensated via their online estimates in a feedforward way when implementing the resulting control algorithms, respectively. Furthermore, a continuously differentiable friction model is employed to compensate the majority of nonlinear friction existing in the system and reduce the burden of the NDO. Specially, one of the proposed control schemes utilizes model-based compensation terms depending on the desired trajectory to be tracked instead of the estimated system states. By doing this, online computation burden can be reduced. The stability of the whole closed-loop system under each control scheme is guaranteed by theoretical analysis. Moreover, the applicability of each control scheme are validated by experiments in different working conditions.     

Decentralized adaptive neural control for interconnected stochastic nonlinear delay-time systems with asymmetric saturation actuators and output constraints

This paper investigates the problem of decentralized adaptive backstepping control for a class of large-scale stochastic nonlinear time-delay systems with asymmetric saturation actuators and output constraints. Firstly, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation nonlinearity, and barrier Lyapunov functions are designed to ensure that the output parameters are restricted. Secondly, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. At last, based on Lyapunov stability theory, a decentralized adaptive neural control method is proposed, and the designed controller decreases the number of learning parameters. It is shown that the designed controller can ensure that all the closed-loop signals are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two examples are provided to show the effectiveness of the proposed method.     

一种新型精密节流阀的设计与建模

本文针对现有的节流阀调节精度不高,不能做调节使用等问题,通过利用超磁致伸缩材料(GMM)制作的精密节流阀来解决,并提出了一种基于超磁致伸缩材料(GMM)的节流阀的创新设计。该设计利用超磁致伸缩驱动器(GMA)作为动力部分,阀芯作为执行元件,通过控制出油口与阀芯之间的间隙来控制流量,间隙则是通过控制时间来精密的控制间隙的大小,这样就实现了精密节流阀的控制。     

Iterative learning control-based tracking synchronization for linearly coupled reaction-diffusion neural networks with time delay and iteration-varying switching topology

In this paper, the D-type iterative learning control (ILC) protocol based on the local neighbor information is designed to achieve tracking synchronization for linearly coupled reaction-diffusion neural networks in presence of time delay and iteration-varying switching topology under a repetitive environment. Firstly, based on non-collocated sensors and actuators network, the proposed D-type ILC update law can realize tracking synchronization by utilizing output tracking errors. Then, by virtue of the contraction mapping principle, the sufficient convergence conditions of tracking synchronization errors are presented under the fixed commutation topology. Subsequently, the synchronization conclusions are extended to the iteration-varying commutation topology scenario. Finally, two numerical examples are provided to verify the efficacy of the obtained results.     

Synchronous control of multiple electrohydraulic actuators under distributed switching topologies with lumped uncertainty

A leader-following synchronous control is proposed in multiple electrohydraulic actuators (MEHAs) under distributed switching topologies to guarantee the follower electrohydraulic actuators (EHAs) tracking the leader motion. Each EHA has a 3-orders nonlinear dynamics with lumped uncertainties involving uncertain hydraulic parameters and unknown external load. Then a quasi-synchronization controller together with a high-gain disturbance observer is designed by Lyapunov techniques to guarantee the synchronous errors asymptotically convergence to a zero neighborhood. Finally, the effectiveness of the proposed quasi-synchronous controller is verified by both simulation and experimental bench such that the finite EHA nodes achieve leader-following synchronous motion under distributed switching topologies.     

功能陶瓷研究进展与发展趋势

简要评述了陶瓷基板,微波介质陶瓷,铁电压电陶瓷,半导体陶瓷,铁电薄膜和智能材料等功能陶瓷的研究进展和发展趋势,对我院功能陶瓷的发展提出了一些建议。