不确定电液位置伺服系统的动态面跟踪控制

针对一类含有动态不确定性的双作用液压缸电液伺服系统跟踪控制问题,采用动态面控制方法设计了一个鲁棒自适应跟踪控制器.由于在逆推设计过程中加入了低通滤波器使得该方法不用对模型非线性进行多次微分,因而设计方法简化.所设计的自适应鲁棒控制器不仅能保证闭环系统的半全局渐近稳定,使得输出渐近跟踪期望轨迹;而且,跟踪误差可以通过控制器的设计参数加以调整.数字仿真结果表明,控制系统对给定位置的跟踪具有良好的动态特性,对系统的不确定性,具有较强的鲁棒性.     

Intelligent decision support system for the adaptive control of a flexible manufacturing system with machine and tool flexibility

This paper describes an intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS). The controller is capable of classifying symptoms in developing the control policies on FMSs with flexibility in operation assignment and scheduling of multi-purpose machining centres which have different tools with their own efficiency. The proposed system is implemented by coupling of rule-based IDSS, simulation block and centralised simulation optimiser for elicitation of shop floor control knowledge. This posteriori adaptive controller uses a new bilateral mechanism in simulation optimiser block for offline training of IDSS based on multi-performance criteria simulation optimisation. The proposed intelligent controller receives online information of the FMS current state and trigger appropriate control rule within real-time simulation data exchange. Finally the FMS intelligent controller is validated by a benchmark test problem. Application of this adaptive controller showed that it could be an effective approach for real time control of various flexible manufacturing systems.     

Nonlinear model order reduction based on local reduced‐order bases

A new approach for the dimensional reduction via projection of nonlinear computational models based on the concept of local reduced‐order bases is presented. It is particularly suited for problems characterized by different physical regimes, parameter variations, or moving features such as discontinuities and fronts. Instead of approximating the solution of interest in a fixed lower‐dimensional subspace of global basis vectors, the proposed model order reduction method approximates this solution in a lower‐dimensional subspace generated by most appropriate local basis vectors. To this effect, the solution space is partitioned into subregions, and a local reduced‐order basis is constructed and assigned to each subregion offline. During the incremental solution online of the reduced problem, a local basis is chosen according to the subregion of the solution space where the current high‐dimensional solution lies. This is achievable in real time because the computational complexity of the selection algorithm scales with the dimension of the lower‐dimensional solution space. Because it is also applicable to the process of hyper reduction, the proposed method for nonlinear model order reduction is computationally efficient. Its potential for achieving large speedups while maintaining good accuracy is demonstrated for two nonlinear computational fluid and fluid‐structure‐electric interaction problems. Copyright © 2012 John Wiley & Sons, Ltd.     

基于输出预估自抗扰策略的加筋壁板结构多模态振动主动控制

针对加筋壁板结构中存在的模型难以精确确定和多模态外界干扰等问题,基于加速度传感器,提出了一种不依赖结构精确数学模型的多模态线性自抗扰振动主动控制(Linear Active Disturbance Rejection Control)策略。由于加速度传感器和压电驱动器的异位配置不可避免地使得整个控制系统存在时延。为解决该问题,利用Smith预估器的原理,引入输出预估器来补偿时延,这样设计的自抗扰振动主动控制器能够很好地解决时延对结构振动性能的影响。基于dSPACE实时仿真平台、利用加速度传感器、压电片驱动器,设计并建立四面固支压电加筋壁板结构实验系统,对提出的控制方法进行试验比较研究。最后的试验结果表明,采用提出的具有输出预估功能的自抗扰振动控制器,能够快速有效地抑制结构的多模态振动。     

Learning-based adaptive controller for dynamic manufacturing cells

Due to the complexity, uncertainty and dynamics in the modern manufacturing environment, a flexible and adaptive cell controller is essential to achieve the system production goals. The paper proposes a learning-based approach for the adaptive controller, which will receive feedback on current performance from the cell, and fine-tune its knowledge base by using a cerebellar model articulation controller (CMAC) network. To examine the proposed controller's performance in manufacturing cells, several experiments are conducted based on simulation. The results show that the controller performs well under multiple (conflicting) performance measures. Furthermore, it is shown that the controller with feedback can learn and adapt to changing environments. Most interestingly, the paper also demonstrates that the proposed controller can adapt to changing system objectives (desired performance measures).     

Subsystem Global Modal Parameterization for efficient simulation of flexible multibody systems

This paper presents a new model order reduction strategy for flexible multibody simulation, namely the Subsystem Global Modal Parameterization. The proposed method is based on a system‐level reduction technique, named Global Modal Parameterization, but offers significant improvements for systems with many independent DOFs. The approach splits up the motion of a mechanism or part of a mechanism into a relative motion, in which the members move relatively with respect to each other, and a global motion of the system, in which the relative position of the members does not change. The relative motion is described by a local Global Modal Parameterization model expressed in a mechanism‐attached frame, and the global motion is described by the motion of the mechanism‐attached frame. In order to improve simulation efficiency, mass invariants are used, which are also introduced in this paper. Two numerical examples are presented, which show the good accuracy and the major simulation efficiency improvements this new approach offers. Copyright © 2011 John Wiley & Sons, Ltd.     

车辆磁流变座椅悬架的模糊自适应整定PID控制

构建磁流变半主动座椅悬架系统。针对此系统建立了人体-座椅的五自由度动力学模型,其中磁流变阻尼器采用改进的Bouc-Wen模型。设计模糊自适应整定PID控制器,根据实时加速度信号在线整定PID参数,达到最优减振效果。用MATLAB／SIMULINK建立系统的仿真模型。仿真结果表明,该控制器能有效地改善座椅的减振效果。     

Intelligent secondary control in smart microgrids: an on-line approach for islanded operations

Dealing with islanded microgrids (MGs), this paper aims at improving the secondary control process to restrict the fluctuations in both the voltage and frequency signals. With the aim of retrieving these parameters at the nominal values, an intelligent control scheme is devised to adjust the corresponding control parameters. To do so, an on-line self-optimizing control approach is embedded in the MG’s central controller. In the tuning process, evolutionary-based techniques such as genetic algorithms provide proper initial adjustment for the parameters. Subsequently, an artificial neural network (ANN) is triggered to provide accurate online modification of the control parameters. Specifically, the training capability of the ANN mechanism along with its extensibility feature avoids the dependency of the controller on the operating point conditions and accommodates different changes and uncertainty reflections. Detailed simulation studies are conducted to investigate the performance of the proposed approach, and the results are discussed in depth.     

Electromechanical modeling and analytical investigation of nonlinearities in energy harvesting piezoelectric beams

Piezoelectric materials are extensively applied for vibrational energy harvesting especially in micro-scale devices where other energy conversion mechanisms such as electromagnetic and electrostatic methods encounter fabrication limitations. A cantilevered piezoelectric bimorph beam with an attached proof (tip) mass for the sake of resonance frequency reduction is the most common structure in vibrational harvesters. According to the amplitude and frequency of applied excitations and physical parameters of the harvester, the system may be pushed into a nonlinear regime which arises from material or geometric nonlinearities. In this study nonlinear dynamics of a piezoelectric bimorph harvester implementing constitutive relations of nonlinear piezoelectricity together with nonlinear curvature and shortening effect relations, is investigated. To achieve this goal first of all a comprehensive fully-coupled electromechanical nonlinear model is presented through a variational approach. The governing nonlinear partial differential equations of the proposed model are order reduced and solved by means of the perturbation method of multiple scales. Results are presented for a PZT/Silicon/PZT laminated beam as a case study. Findings indicate that material nonlinearities of the PZT layer has the dominant effect leading to softening behavior of the frequency response. At the primary resonance, different frequency responses of the extracted power can be distinguished according to the excitation amplitude, which is due to harmonic generation as a result of piezoelectric nonlinearity. The extracted power is analytically computed and validated with a good agreement by a numerical solution.     

Local/global model order reduction strategy for the simulation of quasi‐brittle fracture

This paper proposes a novel technique to reduce the computational burden associated with the simulation of localized failure. The proposed methodology affords the simulation of damage initiation and propagation while concentrating the computational effort where it is most needed, that is, in the localization zones. To do so, a local/global technique is devised where the global (slave) problem (far from the zones undergoing severe damage and cracking) is solved for in a reduced space computed by the classical proper orthogonal decomposition while the local (master) degrees of freedom (associated with the part of the structure where most of the damage is taking place) are fully resolved. Both domains are coupled through a local/global technique. This method circumvents the difficulties associated with model order reduction for the simulation of highly nonlinear mechanical failure and offers an alternative or complementary approach to the development of multiscale fracture simulators. Copyright © 2011 John Wiley & Sons, Ltd.     

A noninvasive system-level model order reduction scheme for flexible multibody simulation

This paper presents a novel system-level model order reduction scheme for flexible multibody simulation, namely the system-level affine projection (SLAP). Contrary to existing system-level model order reduction approaches for multibody systems simulation, this methodology allows to obtain a constant reduced order basis which can be obtained in a noninvasive fashion with respect to the original flexible multibody model. It is shown that this scheme enables an automatic joint constraint elimination which can be obtained at low computational cost through exploitation of the component level modes typically employed in flexible multibody simulation. The equations of motion are derived such that the computational cost of the resulting SLAP model is independent of the original model size. This approach results in a set of ordinary differential equations with a constant mass matrix and nonlinear internal forces. This structure makes the resulting model suitable for a range of estimation, control, and design applications. The proposed approach is validated numerically on a flexible four-bar mechanism and shows good accuracy for a very low-order SLAP model.     

Position Control of Flexible Joint Carts Using Adaptive Generalized Dynamics Inversion

This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion (AGDI) to track the position of a Linear Flexible Joint Cart (LFJC) system along with vibration suppression of the flexible joint. The proposed AGDI control law will be comprised of two control elements. The baseline (continuous) control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control objectives. The control law is realized by inverting the prescribed dynamics using dynamically scaled Moore-Penrose generalized inversion. To boost the robust attributes against system nonlinearities, parametric uncertainties and external perturbations, a discontinuous control law will be augmented which is based on the concept of sliding mode principle. In discontinuous control law, the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering reduction. The closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is guaranteed. Rigorous computer simulations followed by experimental investigation will be performed on Quanser's LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.

     

Design of multi-objective damping controller for gate-controlled series capacitor

This paper proposes an optimization procedure based on eigenvalues to carry out the stabilization function of the Gate-Controlled Series Capacitor (GCSC) in a power system. It is aimed to provide a reliable damping framework by means of a GCSC based multi-objective damping controller. The proposed method employs Particle Swarm Optimization (PSO) to search for optimal parameter settings of a widely used multi-objective lead-lag damping controller. The eigenvalue analysis is considered as the cornerstone of the performed studies in order to investigate the multi-objective methodology in which the unstable or lightly damped modes are scheduled to effectively shift to some prescribed stability zones in the s-plane. The effectiveness of the suggested approach in damping local and interarea oscillations modes in a multi-machine power system, over a wide range of loading conditions, is confirmed through eigenvalue analysis and time simulation.     

Control logic verification for an automotive body assembly line using simulation

In this paper, an approach is proposed that verifies the controller logic processes for the automobile industry via simulation. For this purpose, a state-based object model that creates a virtual car body assembly line is proposed and a verification methodology using observed signal sequences during the simulation is proposed. This approach was applied to an assembly line controlled by a PLC and the effectiveness of the proposed system was explained in a case study.     

H∞控制理论在管道降噪中的仿真实验研究

本文运用H∞控制理论建立管道内一维声场的前馈式噪声主动控制模型,将前馈控制器设计问题转化为标准的H∞控制问题,为有效地消减次级声场对参考传声器的声反馈影响,声反馈通道被融合于控制器的设计过程中,以保证ANC系统的稳定性和降效果,计算机仿真结果表明,该系统能够稳定运行,并且可有效地降低截止频率以下频段的噪声,在设定频带内达到了较好的降噪效果。∞     

Non-Negative Adaptive Mechanism-Based Sliding Mode Control for Parallel Manipulators with Uncertainties

In this paper, a non-negative adaptive mechanism based on an adaptive nonsingular fast terminal sliding mode control strategy is proposed to have finite time and high-speed trajectory tracking for parallel manipulators with the existence of unknown bounded complex uncertainties and external disturbances. The proposed approach is a hybrid scheme of the online non-negative adaptive mechanism, tracking differentiator, and nonsingular fast terminal sliding mode control (NFTSMC). Based on the online non-negative adaptive mechanism, the proposed control can remove the assumption that the uncertainties and disturbances must be bounded for the NFTSMC controllers. The proposed controller has several advantages such as simple structure, easy implementation, rapid response, chattering-free, high precision, robustness, singularity avoidance, and finite-time convergence. Since all control parameters are online updated via tracking differentiator and non-negative adaptive law, the tracking control performance at high-speed motions can be better in real-time requirement and disturbance rejection ability. Finally, simulation results validate the effectiveness of the proposed method.     

弹性飞机的混合 最优PID控制器参数优化

基于弹性飞机的降阶模型设计控制器时需要考虑模型降阶带来的降阶误差,这就需要设计的控制器具有较强的鲁棒性。为此,以某弹性飞机12阶模型为例,研究了基于弹性飞机降阶模型的混合 最优PID控制器的参数优化。首先基于平衡截断法得到了6阶降阶模型。然后,根据全阶模型和降阶模型的频域降阶误差选取了合适的鲁棒加权函数。之后,给出了闭环系统跟踪误差 范数的一种简化计算方法用于 优化指标的计算。最后基于差分进化算法进行了混合 最优PID控制器的参数寻化。仿真结果表明,与 混合灵敏度控制器相比,优化得到的混合 最优PID控制器具有更强的鲁棒性,能同时镇定参数和非参数两种不确定性。对弹性形变模态也有较好的抑制作用,刚性模态也取得了很好的响应效果。     

大型氦低温系统多变量控制

大型氦低温系统广泛应用于各类大科学装置中,运行中往往会产生热脉冲,通过负载端传导给制冷系统,对制冷系统产生热冲击。为了研究和应对热冲击,建立了一种多变量控制策略并得到了相关仿真和实验结果。首先以真实系统为基础建立了氦低温系统的动态仿真模型,同时建立了一个基于模糊神经网络的多变量协同控制策略,并将其应用在仿真液化器模型和一个真实的氦透平制冷系统上,得到了低温系统降温过程和控制过程的仿真和实验数据。仿真和实验结果显示本策略的偏差积分量为0.016 5,下降时间为102 s,上升时间为112 s。普通PID的的偏差积分量为0.026 9,下降时间为154 s,上升时间为170 s。通过仿真和实验过程的比较,验证了本文建立的动态仿真模型具有可用的精度,证明了本策略具有较好的控制效果。     

On the use of modal derivatives for nonlinear model order reduction

Modal derivative is an approach to compute a reduced basis for model order reduction of large‐scale nonlinear systems that typically stem from the discretization of partial differential equations. In this way, a complex nonlinear simulation model can be integrated into an optimization problem or the design of a controller, based on the resulting small‐scale state‐space model. We investigate the approximation properties of modal derivatives analytically and thus lay a theoretical foundation of their use in model order reduction, which has been missing so far. Concentrating on the application field of structural mechanics and structural dynamics, we show that the concept of modal derivatives can also be applied as nonlinear extension of the Craig–Bampton family of methods for substructuring. We furthermore generalize the approach from a pure projection scheme to a novel reduced‐order modeling method that replaces all nonlinear terms by quadratic expressions in the reduced state variables. This complexity reduction leads to a frequency‐preserving nonlinear quadratic state‐space model. Numerical examples with carefully chosen nonlinear model problems and three‐dimensional nonlinear elasticity confirm the analytical properties of the modal derivative reduction and show the potential of the proposed novel complexity reduction methods, along with the current limitations. Copyright © 2016 John Wiley & Sons, Ltd.     

Reduced order modelling and predictive control of multivariable nonlinear process

In this paper, an efficient model-predictive control strategy that can be applied to complex multivariable process is presented. A reduced order generalized predictive algorithm is proposed for online applications with reduction in complexity and time elapsed. The complex multivariable process considered in this work is a binary distillation column. The reduced order model is developed with a recently proposed hybrid algorithm known as Clustering Dominant Pole Algorithm and is able to compute the full set of dominant poles and their cluster centre efficiently. The controller calculates the optimal control action based on the future reference signals, current state and constraints on manipulated and controlled variables for a high-order dynamic simulated model of nonlinear multivariable binary distillation column process. The predictive control algorithm uses controlled auto-regressive integrated moving average model. The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE.