Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.     

一种机器人轨迹的鲁棒跟踪控制

把基于拉格朗日方程的n关节机器人动力学模型,转化成了一线性状态方程.基于这种线性状态方程,利用李雅普诺夫函数方法分别针对机器人标称模型和有外界不确定性干扰时,设计前馈控制器和反馈控制器,使得机器人的实际运动轨迹在标称模型下,指数收敛于所给定的期望运动轨迹;在有外界不确定性干扰时,它与期望轨迹的误差是终值有界的.并且,针对后者所提出的控制律进行仿真.仿真结果表明,这种连续鲁棒控制律对于机器人系统存在外界不确定性干扰时是十分有效的.     

Pressure control of an electro-hydraulic actuated clutch via novel hysteresis model

This paper mainly focuses on the development of pressure tracking control logic of electro-hydraulic actuators for vehicle application. This is done to improve and ensure the performance of a precise lower-level controller for evolving modern shift control logic. The required performance is obtained by hysteresis model-based feed-forward control and additional feedback control. The hysteresis and the time delay, which adversely affect pressure control, are well known nonlinear behaviors in electro-hydraulic actuators. In order to cope with the hysteresis, a novel hysteresis model is proposed based on a physical phenomenon. A mathematical model based on a characteristic curve obtained in preliminary experiments is presented using only one tuning parameter, and this model can be inverted easily to construct a feed-forward controller. In addition, a feedback controller is designed considering the stability margin of a time delay system. The feedback control inputs ensure compensation of the feed-forward errors caused by model error and uncertainty. The proposed controller is designed to lower computational cost considering applicability for production vehicles. As a result, the developed pressure controller is applied to a transmission control unit of a production vehicle and verified experimentally for various driving scenarios.     

Suppression of bounded exogenous disturbances: A linear dynamic output controller

In the paper, we study a problem of constructing a linear dynamic output controller for suppressing bounded exogenous perturbations in a linear control system. We propose an approach based on a method of invariant ellipsoids and technique of linear matrix inequalities. A control of gyroplatform and two-mass system is given as an example.     

高超声速飞行器非线性鲁棒控制律设计

高超声速飞行器具有模型非线性程度高、耦合程度强、参数不确定性大、抗干扰能力弱等特点,其自主控制具有较大的挑战.论文提出了一种基于鲁棒补偿技术和反馈线性化方法的非线性鲁棒控制方法.文中首先采用反馈线性化的方法对纵向模型进行输入输出线性化,实现速度和高度通道的解耦和非线性模型的线性化.针对得到的线性模型,设计包括标称控制器和鲁棒补偿器的线性控制器.基于极点配置原理,设计标称控制器使标称线性系统具有期望的输入输出特性,利用鲁棒补偿器来抑制参数不确定性和外界扰动对于闭环控制系统的影响.基于小增益定理,证明了闭环控制系统的鲁棒稳定性和鲁棒跟踪性能.相比于非线性回路成形控制方法,仿真结果表明了所设计非线性鲁棒控制算法的有效性和优越性.     

A dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlinear processes

 Conventional industrial control systems are in majority based on the single-input-single-output design principle with linearized models of the processes. However, most industrial processes are nonlinear and multivariable with strong mutual interactions between process variables that often results in large robustness margins, and in some cases, extremely poor performance of the controller. To improve control accuracy and robustness to disturbances and noise, new design strategies are necessary to overcome problems caused by nonlinearity and mutual interactions. We propose to use a dynamically-constructed, feedback fuzzy neural controller (DCF-FNC) from the input–output data of the process and a reference model, for direct model reference adaptive control (MRAC) to deal with such problems. The effectiveness of our approach is demonstrated by simulation results on a real-world example of cold mill thickness control and is compared with the performances of the conventional PID controller and the cascade correlation neural network (CCN). Exploiting the advantage of intelligent adaptive control, both the CCN and our DCF-FNC significantly increases the control precision and robustness, compared to the linear PID controller, with our DCF-FNC giving the best results in terms of both accuracy and compactness of the controller, as well as being less computationally intensive than the CCN. We argue that our DCF-FNC feedback controller with both structure and parameter learning can provide a computationally efficient solution to control of many real-world multivariable nonlinear processes in presence of disturbances and noise.     

A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction

This paper aims to investigate suitable time series models for repairable system failure analysis. A comparative study of the Box-Jenkins autoregressive integrated moving average (ARIMA) models and the artificial neural network models in predicting failures are carried out. The neural network architectures evaluated are the multi-layer feed-forward network and the recurrent network. Simulation results on a set of compressor failures showed that in modeling the stochastic nature of reliability data, both the ARIMA and the recurrent neural network (RNN) models outperform the feed-forward model; in terms of lower predictive errors and higher percentage of correct reversal detection. However, both models perform better with short term forecasting. The effect of varying the damped feedback weights in the recurrent net is also investigated and it was found that RNN at the optimal weighting factor gives satisfactory performances compared to the ARIMA model.     

基于自适应线性自抗扰控制的飞机防滑刹车系统重构控制

飞机防滑刹车系统是确保飞机安全起飞、着陆和滑跑的重要航空机电系统. 除了其动力学中的强非线 性、强耦合以及参数时变外, 潜在的执行器等组件故障也会严重降低防滑刹车系统的安全性与可靠性. 为满足故障 及扰动状态下系统的性能需求, 本文提出了一种基于自适应线性自抗扰控制的飞机防滑刹车系统重构控制方法. 根据飞机防滑刹车系统的组成结构及工作原理对其进行数学建模, 并对执行器注入故障因子. 设计了自适应线性 自抗扰重构控制器, 同时分析了整个闭环系统的稳定性. 该控制器将组件故障、外部干扰以及测量噪声等视为总扰 动, 根据状态误差反馈和系统输出信息, 利用BP神经网络在线优化更新扩张状态观测器和状态误差反馈律参数, 从 而更精确地观测与补偿总扰动带来的不利影响. 最后, 在不同跑道环境下的仿真结果验证了所提出重构控制器的适 应性和鲁棒性.     

A Muscle-based Feed-forward Controller of the Human Body

There is an increasing demand for human body motion data. Motion capture and physical animation have been used to generate such data. It is, however, apparent that such methods cannot automatically generate arbitrary human body motions. A human body is a redundant multi-linked body controlled by a number of muscles. For this reason, the muscles must work appropriately and cooperatively for controlling the whole body. It is well-known that the human body control system is composed of two parts: The open-loop feed-forward control system and the closed-loop feedback control system. Many researchers have investigated the characteristics of the latter by analyzing the response of a human body to various external perturbations. However, for the former, very few studies have been done. This paper proposes an open-loop feed-forward model of the lower extremities which includes postural control for accurate animation of a human body. Assumptions are made here that the feed-forward controller minimizes a certain objective value while keeping the balance of the body stable. The actual human motion data obtained using a motion capturing technique is compared with the trajectory calculated using our method for verification. The best criteria which is based on muscle dynamics is proposed. Using our method, dynamically correct human animation can be created by merely specifying a few key postures.     

Fuzzy Control of a Helio-Crane

In this paper we present a comparison of two fuzzy-control approaches that were developed for use on a non-linear single-input single-output (SISO) system. The first method is Fuzzy Model Reference Learning Control (FMRLC) with a modified adaptation mechanism that tunes the fuzzy inverse model. The basic idea of this method is based on shifting the output membership functions in the fuzzy controller and in the fuzzy inverse model. The second approach is a 2 degrees-of-freedom (2 DOF) control that is based on the Takagi-Sugeno fuzzy model. The T-S fuzzy model is obtained by identification of evolving fuzzy model and then used in the feed-forward and feedback parts of the controller. An error-model predictive-control approach is used for the design of the feedback loop. The controllers were compared on a non-linear second-order SISO system named the helio-crane. We compared the performance of the reference tracking in a simulation environment and on a real system. Both methods provided acceptable tracking performance during the simulation, but on the real system the 2 DOF FMPC gave better results than the FMRLC.     

基于阻尼最小二乘法的神经网络预测偏差补偿自校正控制器

本文提出一种神经网络预测偏差补偿自校正控 制器,用线性模型的预测控制去控制非线性系统,其预测偏差用神经网络进行补偿．线性模 型的辨识和神经网络的学习均采用阻尼最小二乘法．仿真结果表明,用这种控制器能有效地 控制非线性系统,并具有超调小,鲁棒性好的特点．     

High-speed and precision control of a piezoelectric positioner with hysteresis,resonance and disturbance compensation

A novel composite control strategy is developed in this paper to compensate hysteresis, resonance and disturbances in a piezo-actuated nanopositioner. The control objective of the piezoelectric positioner is to achieve high tracking performance in terms of accuracy and speed. For this purpose, a Bouc–Wen model based hysteresis compensator is first applied to mitigate the hysteresis nonlinearity without the complex inverse hysteresis calculation. And then, the linear dynamic of the hysteresis compensated system is identified and inverted to account for the resonance. A model-based inversion feed-forward controller is designed to achieve high speed tracking. Afterwards, a high-gain feedback controller is designed based on a notch filter to handle the modeling inaccuracy and all kinds of disturbances. So, the feed-forward controller can be augmented to the feedback controller to realize high speed and precision tracking. The enhancement of tracking performance is demonstrated through several comparative experiments. The performance of 70 Hz bandwidth and 0.281 μm precision can be achieved, which validated the effectiveness of the proposed composite control scheme.     

Feedback control of chaotic systems

A suboptimal feedback controller implemented by a multilayer feed-forward neural network is presented to control the unpredictable behavior of chaotic systems. The controller has been tested on the Lorenz and the Rössler systems using numerical simulation. Results show that chaotic systems, subject to feedback control, can be tamed to behave like a system having point attractors with associated basins of attraction.     

基于CMAC与PID复合控制的汽轮机功率控制系统

针对汽轮机功率调节过程的非线性特征,提出了将CMAC神经网络与常规PID控制相结合的方法,并将其应用于汽轮机功率控制中。该复合控制方法可以实现前馈与反馈的联合控制,其中前馈控制由CMAC神经网络实现,反馈控制由常规PID控制器实现。通过对比分析CMAC/PID复合控制与常规PID控制的仿真结果,可以看到在不同的扰动因素存在时,CMAC/PID复合控制均能取得较好的控制效果。     

Modeling and control of a PEM fuel cell system: A practical study based on experimental defined component behavior

In this contribution, the dynamical behavior of a polymer electrolyte membrane (PEM) fuel cell system is modeled; related control approaches are developed. The system model used for experimental and modeling purposes describes a 1.2 kW PEM fuel cell stack and an air blower. Due to the dynamical fuel cell–blower interaction the fuel cell stack and the blower model are validated to real systems respectively. Additionally, a feedback based on PI-control is used for hydrogen pressure control with an anode inlet valve. This controller is able to eliminate a stationary error between the anode and cathode pressures. For principal investigations three control approaches, a classical static feed-forward control approach, a state-space feedback control, and a novel gain-scheduling approach are developed, applied, and compared. As result, it can be shown that the feed-forward approach lacks in performance recovering the excess oxygen ratio to the desired level. The state-space feedback control shows stationary error. The introduced gain-scheduling control approach leads to a fast excess oxygen ratio recovery without stationary deviations.     

Modeling, identification, and control of a class of nonlinearsystems

In this paper, we propose a new fuzzy hyperbolic model for a class of complex systems, which is difficult to model. The fuzzy hyperbolic model is a nonlinear model in nature and can be easily derived from a set of fuzzy rules. It can also be seen as a feedforward neural network model and so we can identify the model parameters by BP-algorithm. We prove that the stable controller can be designed based on linear system theory. Two methods of designing the controller for the fuzzy hyperbolic model are proposed. The results of simulation support the effectiveness of the model and the control scheme     

Neural networks for advanced control of robot manipulators

Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in the bank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we prove that the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given.     

Feedback gains for correcting small perturbations to standingposture

A dynamical model of the neuro-musculo-skeletal mechanics of a cat hindlimb is developed to investigate the feedback regulation of standing posture under small perturbations. The model is a three-joint limb, moving only in the sagittal plane, driven by 10 musculotendon actuators, each with response dynamics dependent on activation kinetics and muscle kinematics. Under small perturbations, the nonlinear postural regulation mechanism is approximately linear. Sensors exist which could provide state feedback. Thus, the linear quadratic regulator is proposed as a model for the structure of the feedback controller for regulation of small perturbations. System states are chosen to correspond to the known outputs of physiological sensors: muscle forces (sensed by tendon organs), a combination of muscle lengths and velocities (sensed by spindle organs), joint angles and velocities (sensed by joint receptors), and motoneuron activities (sensed by Renshaw cells). Thus, the feedback gain matrices computed can be related to the spinal neural circuits. Several proposals for control strategy have been tested under this formulation. It is shown that a strategy of regulating all the states leads to controllers that best mimic the externally measured behavior of real cats