闭环控制系统反馈增益向量的灵敏度分析

根据振动系统的模态控制方程,利用状态反馈控制和闭环控制系统的极点配置法,文中提出计算模态反馈增益向量的新方法,并在此基础上推导模态反馈增益向量灵敏度分析的方法.该分析方法涉及开环系统的特征值、特征向量以及模态控制矩阵的灵敏度, 由于在方法的推导过程中使用了模态控制方程,因此避免了非常复杂的数学推演.最后,将所提方法应用于一桁架结构的振动控制问题,以说明方法的有效性.     

Direct yaw moment control for distributed drive electric vehicle handling performance improvement

For a distributed drive electric vehicle (DDEV) driven by four in-wheel motors, advanced vehicle dynamic control methods can be realized easily because motors can be controlled independently, quickly and precisely. And direct yaw-moment control (DYC) has been widely studied and applied to vehicle stability control. Good vehicle handling performance: quick yaw rate transient response, small overshoot, high steady yaw rate gain, etc, is required by drivers under normal conditions, which is less concerned, however. Based on the hierarchical control methodology, a novel control system using direct yaw moment control for improving handling performance of a distributed drive electric vehicle especially under normal driving conditions has been proposed. The upper-loop control system consists of two parts: a state feedback controller, which aims to realize the ideal transient response of yaw rate, with a vehicle sideslip angle observer; and a steering wheel angle feedforward controller designed to achieve a desired yaw rate steady gain. Under the restriction of the effect of poles and zeros in the closed-loop transfer function on the system response and the capacity of in-wheel motors, the integrated time and absolute error (ITAE) function is utilized as the cost function in the optimal control to calculate the ideal eigen frequency and damper coefficient of the system and obtain optimal feedback matrix and feedforward matrix. Simulations and experiments with a DDEV under multiple maneuvers are carried out and show the effectiveness of the proposed method: yaw rate rising time is reduced, steady yaw rate gain is increased, vehicle steering characteristic is close to neutral steer and drivers burdens are also reduced. The control system improves vehicle handling performance under normal conditions in both transient and steady response. State feedback control instead of model following control is introduced in the control system so that the sense of control intervention to drivers is relieved.     

Model predictive controller-based multi-model control system for longitudinal stability of distributed drive electric vehicle

Distributed drive electric vehicle(DDEV) has been widely researched recently, its longitudinal stability is a very important research topic. Conventional wheel slip ratio control strategies are usually designed for one special operating mode and the optimal performance cannot be obtained as DDEV works under various operating modes. In this paper, a novel model predictive controller-based multi-model control system (MPC-MMCS) is proposed to solve the longitudinal stability problem of DDEV. Firstly, the operation state of DDEV is summarized as three kinds of typical operating modes. A submodel set is established to accurately represent the state value of the corresponding operating mode. Secondly, the matching degree between the state of actual DDEV and each submodel is analyzed. The matching degree is expressed as the weight coefficient and calculated by a modified recursive Bayes theorem. Thirdly, a nonlinear MPC is designed to achieve the optimal wheel slip ratio for each submodel. The optimal design of MPC is realized by parallel chaos optimization algorithm(PCOA)with computational accuracy and efficiency. Finally, the control output of MPC-MMCS is computed by the weighted output of each MPC to achieve smooth switching between operating modes. The proposed MPC-MMCS is evaluated on eight degrees of freedom(8DOF)DDEV model simulation platform and simulation results of different condition show the benefits of the proposed control system.     

Particle and Kalman filtering for state estimation and control of DC motors

State estimation is a major problem in industrial systems. To this end, Gaussian and nonparametric filters have been developed. In this paper the Kalman Filter, which assumes Gaussian measurement noise, is compared to the Particle Filter, which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a DC motor is used. The reconstructed state vector is used in a feedback control loop to generate the control input of the DC motor. In simulation tests it was observed that for a large number of particles the Particle Filter could succeed in accurately estimating the motor’s state vector, but at the same time it required higher computational effort.     

Robust-optimal active vibration controllers design for the uncertain flexible mechanical systems possessing integrity via genetic algorithm

In this paper, a robust-optimal control approach is proposed to treat the active vibration control (or active vibration suppression) problem of flexible mechanical systems under mode truncation, linear time-varying parameter uncertainties in both the controlled and residual parts, feedback gain perturbations, estimator gain perturbations and partial actuator failures. A sufficient condition is proposed to ensure that the flexible mechanical systems with time-varying structured parameter uncertainties are asymptotically stable against partial actuator failures. Systems which have such a property of keeping stable under partial actuator failures are said to possess integrity, and this is an inherent property of MIMO systems. Based on the robust stability constraint and the minimization of a defined H₂ performance, a hybrid Taguchi-genetic algorithm (HTGA) is applied to solve the optimal state feedback controller and observer design problem of uncertain flexible mechanical systems. A design example of a flexible rotor system is given to demonstrate the applicability of the proposed approach. It is shown that the proposed approach can obtain satisfactory results.     

基于最优控制的汽车操纵逆动力学的研究

提出了一种基于最优控制理论的汽车操纵逆动力学研究方法，用于识别不同汽车跟踪同一期望路径的方向盘转角输入。以驾驶员对汽车施加的方向盘转角输入为控制变量，以精确跟踪期望路径为控制目标，将汽车操纵逆动力学问题转化为最优控制问题。利用直接配置方法将最优控制问题转化为非线性规划问题，运用序列二次规划方法求解。仿真结果表明：该方法能够使汽车很好地跟踪所期望的路径，且可以比较跟踪同一路径的不同汽车的操纵性能。     

时滞对振动主动控制系统控制效果的影响分析

为解决振动主动控制技术在实际工程应用中反馈环节的时滞会导致受控系统失稳的问题,以获得最优的控制效果,提出了采用逐步输入荷载项的方法,对精细积分方法进行修正,用以求解含双时滞受控系统的动力学方程。通过数值仿真,计算不同时滞情况下的系统响应,在得到不同反馈增益下含双时滞的动力系统响应峰值分布基础上,分析了时滞变化和反馈增益不同的取值对系统响应的影响。研究结果表明,时滞对受控系统控制效果的影响程度随反馈增益的增大而增大,当时滞量和反馈控制增益匹配调节适当时,可以使系统保持稳定状态,该结果可为考虑时滞影响的结构振动主动控制算法的合理设计提供依据。
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A fuzzy–logic controller for an electrically driven steering system for a motorcar

This paper presents an application where a Fuzzy-Logic Controller (FLC) is used at a supervisory level to implement mutual coordination of the steering of the two front wheels of a motorcar. The two front wheels are steered by two independent discrete time state feedback controllers with a view to optimize the steering slip angles. The functions of the two controllers are tied together by way of a FLC. Because of the presence of unmodelled dynamics and disturbances acting on the two sides, it is difficult to achieve the desired performance using conventional control systems. This is the primary reason that FLC is emploged to solve the problem. The results show that the implemented system achieved desired coupling between the two independent systems and thereby reduces the difference between the two steered angles.     

线控转向系统的前轮转角跟踪策略研究

研究通过对线控转向系统进行主动控制,可靠并准确地得到期望的前轮转角。基于建立的线控转向系统数学模型,使用非线性自回归模型确定其系统参数,设计内模控制器跟踪车辆的期望运动状态。通过开环和闭环试验,对控制器在典型的驾驶工况下的有效性进行了验证。通过与PID控制器的结果对比,证明所设计的内模控制器能提供更好的控制性能。为减少驾驶员的操纵负担并确保车辆在不同行驶条件下的稳定性,根据不同工况下的测试结果提出基于增益不变的变角传动比控制策略,并设计了滑模控制器跟踪期望横摆角以实现主动转向。通过对内模和滑模控制器的联合仿真结果表明,所设计的控制器可实现期望横摆角度的精确跟踪,显著提高车辆的操纵灵活性和稳定性。     

High-precision tracking differentiator via generalized discrete-time optimal control

An enhanced discrete-time tracking differentiator (TD) with high precision based on discrete-time optimal control (DTOC) law is proposed. This law takes the form of state feedback for a double-integral system that adopts the Isochronic Region approach. There, the control signal sequence is determined by a linearized criterion based on the position of the initial state point on the phase plane. The proposed control law can be easily extended to the TD design problem by combining the first-state variable of the double-integral system with the desired trajectory. To improve the precision of the discretization model, we introduced a zero-order hold on the control signal. We also discuss the general form of DTOC law by analysing the relationship between boundary transformations and boundary characteristic points. After comparing the simulation results from three different TDs, we determined that this new TD achieves better performance and higher precision in signal-tracking filtering and differentiation acquisition than do existing TDs. Also the comparisons of the computational complexities between the proposed DTOC law and normal one are demonstrated. For confirmation of its utility, we processed raw phasor measurement units data via the proposed TD. In the absence of complex power system modelling and historical data, it was verified that the proposed TD is suitable for applications of real-time synchrophasor estimations, especially when the states are corrupted by noise.     

Modal space vibration control of a beam by using the feedforward and feedback control loops

This paper introduces a vibration control method for a flexible beam subjected to arbitrary, unmeasurable disturbance forces. The concept of independent modal space control is adopted. Here, we choose the modal filters as the state estimator to obtain the modal coordinates and modal velocities for the modal space control. Because of the existence of the disturbance forces, applying only the state feedback to suppress the vibration usually cannot achieve the desired control performance. The modal space feedforward control is then introduced to cancel out the disturbance forces. In this paper, the feedforward and feedback control method is implemented to reduce the beam vibration. The disturbance force observer is established to observe the disturbance modal forces for the feedforward control. The control gains are derived from the extended optimal control algorithm, where the disturbance modal forces are treated as exogenous state variables. By combining the feedback, feedforward control laws and the disturbance force observer together, the vibration control performances are discussed.     

摇摆工况下电动汽车高速斜齿轮传动振动控制方法

在摇摆工况下,当无级变速转换为传动变速时,导致扭矩方向及大小的偏移、主转速和从转速不一致,从而产生斜齿轮传动振动问题,为此,提出了基于线性反馈的电动汽车高速斜齿轮传动振动控制方法。通过分析斜齿轮传动时内部的电流走向,明确传动装置的速度比与金属带速度比的反比关系。结合动力学和傅里叶变换算法,推导出速度导纳、位移导纳以及加速度导纳间响应关系,利用响应系数识别 3 种导纳情况下,传动原点处的共振与反共振参数,以此为基础,采用线性反馈混沌控制法建立反馈数据增益矩阵,得出具有稳定周期的调节系数,利用调节系数控制传动点的运行轨迹,完成传动振动的高效控制。仿真测试结果表明,该方法提高了汽车内部共振区域频率波动的吻合程度,有效降低了外界噪声及其他干扰因素导致的传动振动影响,控制性能较好。     

悬臂梁智能结构主共振响应的最优化控制

应用最优化控制方法研究悬臂梁主共振响应的减振控制。由主共振振动定常解的稳定条件得到反馈控制增益的参数范围。以主共振有控制振动峰值与无控制振动峰值的比值为减振的衰减率,以该衰减率为目标函数,反馈控制增益参数范围为约束条件,利用最优化方法计算得到衰减率最小的速度反馈控制参数。计算发现速度反馈控制参数数值越大,衰减率越小,控制效果越好。以非线性振动系统的能量函数为目标函数,计算得到能量函数值最小的位移反馈控制参数,实现智能结构体主共振响应的控制器设计。由悬臂梁主共振振动实验实测数据计算出最优化控制参数,设计非线性主共振振动实验控制系统,进行减振控制实验研究。     

基于自适应模糊神经网络辨识的电液伺服系统L2增益设计

为克服电液伺服系统不确定性、非线性、估计误差和干扰等因素对系统稳定性和精度的影响，提出了基于自适应模糊神经网络辨识的电液伺服系统L2增益设计方法。用自适应模糊神经网络在线估计包括系统不确定性和非线性在内的未知动态特性，同时用增益自适应变结构补偿自适应模糊神经网络的估计误差，用系统L2增益设计方法抑制干扰对系统的影响，以期使系统对不确定性和非线性具有鲁棒性，而且从干扰到描述系统跟踪误差的评价函数的L2增益小于指定值。     

粒子群及局部搜索算法在串并联系统结构优化中的应用

将粒子群算法和局部搜索算法相结合,用于解决串并联系统的冗余分配问题。介绍串并联系统结构,确定该冗余分配问题以系统最小费用为优化目标,以系统可用度不能低于某一确定值为约束条件。应用概率生成函数(UGF)方法计算系统可用度,将粒子群算法和局部搜索算法相结合进行优化求解,并给出迭代过程。通过实例对优化迭代过程进行具体说明。     

MIMO variable structure controller design for a bioreactor benchmark process

In this paper, variable structure control of a bioreactor is studied. The process has two state variables named cell mass and nutrient amount, and two control inputs to maintain the state variables at their desired levels. Although the state space representation of the system seems simple, the system displays several challenges that make it necessary to develop a good flowrate (control) management strategy. Due to the plant-model mismatch, variable structure control technique is applied and it is seen that the sliding subspace is reached in finite time and the behavior thereafter is insensitive to considerable degrees of variation in the parameters and disturbances. The design is based on the nominal model and a comparison with a feedback linearizing controller is presented. The objective of the paper is to illustrate the efficacy of MIMO sliding mode control on a benchmark problem. Overall, the results with the proposed controller demonstrate the following desirable characteristics: (i) very good tracking precision (ii) small percent overshoot values and (iii) good decoupling of the process states.     

基于PMP的HEV全局最优能量管理策略研究

动态规划常用于求取已知工况的全局最优解,但该方法计算量大、求解时间长。为了解决这个问题,把发电机组和电池组模型适当化简,在保持电池组电量平衡的条件下,以电池组荷电状态为状态变量,以电池组输出功率为控制量,采用庞特里亚金极小值原理算法求解了混合动力汽车的能量管理问题,得到了全局最优解,并给出了详细的求解过程。与动态规划相比,该方法求解需要的时间很短。     

Robust observer-based absolute stabilization for Lur’e singularly perturbed systems with state delay

This paper is concerned with the problem of robust observer-based absolute stabilization for Lur’e singularly perturbed time-delay systems. The aim is to design a suitable observer-based feedback control law such that the resulting closed-loop system is absolutely stable. First, a full-order state observer is constructed. Based on the linear matrix inequality (LMI) technique, a delay-dependent sufficient condition is presented such that the observer error system is absolutely stable. Then, for observer-based feedback control, by introducing some slack matrices, a sufficient condition for input-to-state stability (ISS) of the closed-loop system with regard to the observer error is presented. Thus, the absolute stabilization of the closed-loop system can be guaranteed based on the ISS property. In addition, the criteria presented are both independent of the small parameter and the upper bound for the absolute stability can be obtained in a workable algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the developed methods.     

Novel fuzzy feedback linearization strategy for control via differential geometry approach

The study investigates a novel fuzzy feedback linearization strategy for control. The main contributions of this study are to construct a control strategy such that the resulting closed-loop system is valid for any initial condition with almost disturbance decoupling performance, and develop the feedback linearization design for some class of nonlinear control systems. The feedback linearization control guarantees the almost disturbance decoupling performance and the uniform ultimate bounded stability of the tracking error system. Once the tracking errors are driven to touch the global final attractor with the desired radius, the fuzzy logic control is immediately applied via a human expert’s knowledge to improve the convergence rate. One example, which cannot be solved by the first paper on the almost disturbance decoupling problem, is proposed in this paper to exploit the fact that the almost disturbance decoupling and the convergence rate performances are easily achieved by the proposed approach.