Finite-time master–slave synchronization and parameter identification for uncertain Lurie systems

This paper investigates the finite-time master–slave synchronization and parameter identification problem for uncertain Lurie systems based on the finite-time stability theory and the adaptive control method. The finite-time master–slave synchronization means that the state of a slave system follows with that of a master system in finite time, which is more reasonable than the asymptotical synchronization in applications. The uncertainties include the unknown parameters and noise disturbances. An adaptive controller and update laws which ensures the synchronization and parameter identification to be realized in finite time are constructed. Finally, two numerical examples are given to show the effectiveness of the proposed method.     

Robust tracking and distributed synchronization control of a multi-motor servomechanism with H-infinity performance

The multi-motor servomechanism (MMS) is a multi-variable, high coupling and nonlinear system, which makes the controller design challenging. In this paper, an adaptive robust H-infinity control scheme is proposed to achieve both the load tracking and multi-motor synchronization of MMS. This control scheme consists of two parts: a robust tracking controller and a distributed synchronization controller. The robust tracking controller is constructed by incorporating a neural network (NN) K-filter observer into the dynamic surface control, while the distributed synchronization controller is designed by combining the mean deviation coupling control strategy with the distributed technique. The proposed control scheme has several merits: 1) by using the mean deviation coupling synchronization control strategy, the tracking controller and the synchronization controller can be designed individually without any coupling problem; 2) the immeasurable states and unknown nonlinearities are handled by a NN K-filter observer, where the number of NN weights is largely reduced by using the minimal learning parameter technique; 3) the H-infinity performances of tracking error and synchronization error are guaranteed by introducing a robust term into the tracking controller and the synchronization controller, respectively. The stabilities of the tracking and synchronization control systems are analyzed by the Lyapunov theory. Simulation and experimental results based on a four-motor servomechanism are conducted to demonstrate the effectiveness of the proposed method.     

Parametric adaptive estimation and backstepping control of electro-hydraulic actuator with decayed memory filter

Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm.     

Design of adaptive backstepping congestion controller for TCP networks with UDP flows based on minimax

The congestion control problem of TCP network systems with user datagram protocol (UDP) flows is investigated in this paper. A nonlinear TCP network model with strict-feedback structure is first established. The unknown UDP flow is regarded as the external disturbance, and the maximum UDP flow is calculated by using the minimax approach. And then, a congestion control algorithm is proposed by using the adaptive backstepping approach. Meanwhile, the adaptive law is employed to estimate the unknown link capacity. The design of the adaptive law is to introduce a parameter mapping mechanism to limit the parameter identification range to a specified interval, thereby improving the estimation efficiency of the parameters. Furthermore, a state-feedback congestion controller is presented to make sure that the output of the system tracks the desired queue. The simulation results show the superiority and feasibility of the proposed method.     

Adaptive output feedback tracking of nonlinear systems with uncertain nonsymmetric dead-zone input

In this paper, the problem of adaptive practical tracking is investigated by output feedback for a class of uncertain nonlinear systems subject to nonsymmetric dead-zone input nonlinearity with parameters of dead-zone being unknown. Instead of constructing the inverse of dead-zone nonlinearity, an adaptive robust control scheme is developed by designing an output compensator including two dynamic gains based respectively on identification and non-identification mechanism. With the aid of dynamic high-gain scaling approach and Backstepping method, stability analysis of the closed-loop system is proceeded using non-separation principle, which shows that the proposed controller guarantees that all closed-loop signal is bounded while the output of system tracks a broad class of bounded reference trajectories by arbitrarily small error prescribed previously. Finally, two examples are given to illustrate our controller effective.     

Parameters identification and trajectory control for a hydraulic system

In order to improve the tracking accuracy of a hydraulic system, an improved ant colony optimization algorithm (IACO) is proposed to optimize the values of proportional–integral–derivative (PID) controller. In addition, this paper presents an experimental study on the parameters identification to deduce accurate numerical values of the hydraulic system, which also determines the relationship between control signal and output displacement. Firstly, the basic principle of the hydraulic system and the mathematical model of the electro-hydraulic proportional control system are analyzed. Based on the theoretical models, the transfer function of the control system is obtained by recursive least square identification method (RLS). Then, the key parameters of the control system model are obtained. Some improvements are proposed to avoid premature convergence and slow convergence rate of ACO: the transition probability is revised based adjacent search mechanism, dynamic pheromone evaporation coefficient adjustment strategy is adopted, pheromone update rule and parameters optimization range are also improved. Then the proposed IACO tuning based PID controller and the identification parameters are modeled and simulated using MATLAB/Simulink and AMESim co-simulation platform. Comparisons of IACO, standard ACO and Ziegler–Nichols (Z–N)PID controllers are carried out with different references as step signal and sinusoidal wave using the co-simulation platform. The simulation results of the bucket system using the proposed controller demonstrates improved settling time, rise time and the convergence speed with a new objective function J. Finally, experiments with leveling operations are performed on a 23 ton robotic excavator. The experimental results show that the proposed controller improves the trajectory accuracy of the leveling operation by 28% in comparison to the standard ACO-PID controller.     

An on-line modified least-mean-square algorithm for training neurofuzzy controllers

The problem hindering the use of data-driven modelling methods for training controllers on-line is the lack of control over the amount by which the plant is excited. As the operating schedule determines the information available on-line, the knowledge of the process may degrade if the setpoint remains constant for an extended period. This paper proposes an identification algorithm that alleviates "learning interference" by incorporating fuzzy theory into the normalized least-mean-square update rule. The ability of the proposed methodology to achieve faster learning is examined by employing the algorithm to train a neurofuzzy feedforward controller for controlling a liquid level process. Since the proposed identification strategy has similarities with the normalized least-mean-square update rule and the recursive least-square estimator, the on-line learning rates of these algorithms are also compared.     

Model Based PI Tuning Rule with Ultimate Gain and Phase Margin Criteria

Abstract

The control of various processes using a proportional-integral (PI) controller is investigated in this work. A new tuning rule based on the ultimate gain and phase margin specification is proposed. The tuning rule is given by analytical approximations. To obtain the tuning rule, a control signature is formulated using the ultimate properties of the system. The PI controller parameters are related with process model parameter to yield a model based tuning rule. Simulation examples are provided to demonstrate the performance and robustness of the present tuning rule. The new tuning rule requires a relatively small computational effort and is particularly useful for online applications.     

基于功率谱估计的双液压马达同步系统实验研究

针对双液压马达同步驱动系统中由于两液压马达的制造误差、安装误差以及元件和密封圈磨损老化等因素引起的不同步问题,提出一种以功率谱估计为基础的同步控制器定量反馈设计方法。该方法首先对两马达工作范围内各工作点频率特性进行非参数辨识,形成定量反馈设计所需系统模板;然后再使用定量反馈设计单输入单输出方法和多输入多输出稳定性条件设计双液压马达同步控制器。同步实验结果表明用该方法设计出的控制器可以保证在整个工作范围内满足同步性能指标和鲁棒性要求。     

热轧立辊电液伺服系统的自适应模糊控制

以热轧立辊为研究背景,针对其液压伺服系统存在的非线性、参数不确定性以及负载干扰等特点,基于模糊基函数网络提出一种自适应控制方法.首先将非线性系统线性化并将其作为已知系统,利用这部分已知的动态特性设计反馈控制使标称系统稳定.然后利用模糊基函数网络仅学习非线性系统不确定性的上界,将输出作为补偿控制器的参数,并在Lyapunov稳定意义下构造自适应控制器,该自适应控制器不仅确保了闭环系统的鲁棒性而且加快了跟踪误差的收敛速度.将该控制器应用于某热轧立辊电液位置伺服系统中进行仿真研究,结果表明,该控制器优于传统的PID控制器,可以取得较好的控制效果.     

Rotation matrix based finite-time attitude synchronization control for spacecraft with external disturbances

This paper investigates the anti-unwinding finite-time attitude synchronization control problem for Spacecraft formation flying with external disturbances. Two finite-time controllers are designed based on rotation matrix and terminal sliding mode method. By designing a novel sliding mode surface, the first controller is developed when the upper bound of the external disturbances can be exactly known. However, this value is not always available in reality. In addition, the direct use of the upper bound of the external disturbances can result in the chattering problem. For the purpose of overcoming the disadvantage of the first controller, a modified control law is proposed, in which the adaptive law is applied to estimate the unknown value online. Theoretical analysis and numerical simulations are presented to demonstrate the validity of the proposed controllers.     

两非对称液压缸同步举升系统的自适应跟踪控制

针对由两个非对称液压缸组成的电液伺服同步举升系统,首先建立了该系统的非线性耦合模型,并在该模型的基础上设计出一种鲁棒自适应跟踪控制器。该控制器利用多变量后推设计方法实现了液压缸对目标轨迹的跟踪控制以及同步控制,并结合参数自适应律解决系统中某些参数的不确定性问题。整个控制律的设计过程通过Lyapunov函数方法保证系统的稳定性。为验证该控制律,使用AMESim软件构建两非对称液压缸同步举升系统仿真模型,仿真结果验证了提出方法的有效性。     

A novel body frame based approach to aerospacecraft attitude tracking

In the common practice of designing an attitude tracker for an aerospacecraft, one transforms the Newton-Euler rotation equations to obtain the dynamic equations of some chosen inertial frame based attitude metrics, such as Euler angles and unit quaternions. A Lyapunov approach is then used to design a controller which ensures asymptotic convergence of the attitude to the desired orientation. Although this design methodology is pretty standard, it usually involves singularity-prone coordinate transformations which complicates the analysis process and controller design. A new, singularity free error feedback method is proposed in the paper to provide simple and intuitive stability analysis and controller synthesis. This new body frame based method utilizes the concept of Euleraxis and angles to generate the smallest error angles from a body frame perspective, without coordinate transformations. Global tracking convergence is illustrated with the use of a feedback linearizing PD tracker, a sliding mode controller, and a model reference adaptive controller. Experimental results are also obtained on a quadrotor platform with unknown system parameters and disturbances, using a boundary layer approximated sliding mode controller, a PIDD controller, and a unit sliding mode controller. Significant tracking quality is attained.     

Improved finite-control-set model predictive control for active front-end rectifiers with simplified computational approach and on-line parameter identification

In this paper, an improved finite-control-set model predictive control method is proposed for active front-end rectifiers where the computational effort and parameter mismatch problems are taken into account simultaneously. Specifically, a desired voltage vector which only requires one exploration is directly selected by using a single cost function, and the process of selection of the desired voltage vector is optimized by using a sector distribution method. Meanwhile, a model reference adaptive system-based online parameter identification approach is presented to alleviate the parameter mismatch problem. The advantages of the proposed method summarized as follows: First, the proposed algorithm reduces the eight possible voltage vectors to one. The exhaustive exploration can be avoided while the control performance is not deteriorated. Second, the proposed controller can mitigate performance degradation caused by the model parameter mismatch. Simulation results under various parameters operating conditions are presented to demonstrate the efficacy of the proposed method.     

Analytical design of an industrial two-term controller for optimal regulatory control of open-loop unstable processes under operational constraints

This paper proposes a novel optimization-based approach for the design of an industrial two-term proportional-integral (PI) controller for the optimal regulatory control of unstable processes subjected to three common operational constraints related to the process variable, manipulated variable and its rate of change. To derive analytical design relations, the constrained optimal control problem in the time domain was transformed into an unconstrained optimization problem in a new parameter space via an effective parameterization. The resulting optimal PI controller has been verified to yield optimal performance and stability of an open-loop unstable first-order process under operational constraints. The proposed analytical design method explicitly takes into account the operational constraints in the controller design stage and also provides useful insights into the optimal controller design. Practical procedures for designing optimal PI parameters and a feasible constraint set exclusive of complex optimization steps are also proposed. The proposed controller was compared with several other PI controllers to illustrate its performance. The robustness of the proposed controller against plant-model mismatch has also been investigated.     

涡轮叶片精铸模具CAD系统参数规则库设计

在涡轮叶片精铸模具CAD系统开发中,参数规则库设计用以解决模具零件参数化造型中专家经验知识的表达问题,并实现系统智能化。针对参数规则的特点,提出一种参数规则库的设计方案,很好地解决了参数规则的知识表达、运算和推理,为涡轮叶片精铸模具CAD系统的开发奠定了基础。     

Calculation of PID controller parameters by using a fuzzy neural network

In this paper, we use the fuzzy neural network (FNN) to develop a formula for designing the proportional-integral-derivative (PID) controller. This PID controller satisfies the criteria of minimum integrated absolute error (IAE) and maximum of sensitivity (Ms). The FNN system is used to identify the relationship between plant model and controller parameters based on IAE and Ms. To derive the tuning rule, the dominant pole assignment method is applied to simplify our optimization processes. Therefore, the FNN system is used to automatically tune the PID controller for different system parameters so that neither theoretical methods nor numerical methods need be used. Moreover, the FNN-based formula can modify the controller to meet our specification when the system model changes. A simulation result for applying to the motor position control problem is given to demonstrate the effectiveness of our approach.     

Mean deviation coupling synchronous control for multiple motors via second-order adaptive sliding mode control

A new mean deviation coupling synchronization control strategy is developed for multiple motor control systems, which can guarantee the synchronization performance of multiple motor control systems and reduce complexity of the control structure with the increasing number of motors. The mean deviation coupling synchronization control architecture combining second-order adaptive sliding mode control (SOASMC) approach is proposed, which can improve synchronization control precision of multiple motor control systems and make speed tracking errors, mean speed errors of each motor and speed synchronization errors converge to zero rapidly. The proposed control scheme is robustness to parameter variations and random external disturbances and can alleviate the chattering phenomena. Moreover, an adaptive law is employed to estimate the unknown bound of uncertainty, which is obtained in the sense of Lyapunov stability theorem to minimize the control effort. Performance comparisons with master–slave control, relative coupling control, ring coupling control, conventional PI control and SMC are investigated on a four-motor synchronization control system. Extensive comparative results are given to shown the good performance of the proposed control scheme.     

New approach of friction identification for electro-hydraulic servo system based on evolutionary algorithm and statistical logics with experiments

High-precision control of an Electro-hydraulic servo system (EHSS) should fully consider practical non-linearities, such as dynamic friction, in controller design. The LuGre model is widely used to describe non-linear friction, but parameter identification for this model remains a challenging task. This study proposes a novel identification approach for the LuGre model based on the Evolutionary algorithm (EA) and statistical logics. Identification experiments are also conducted for a practical EHSS. Static parameters are identified with constant velocity experiments. Moreover, the non-symmetry of friction in positive and negative rotary directions is fully considered. Dynamic parameters are identified with the pre-sliding process in the breakaway experiment. The EA is utilized to enhance the optimal estimation of friction parameters. Statistical logics are used to predict the deviations in the estimated results with high confidence interval. Furthermore, extensive simulation and experiment results validate the effectiveness of the proposed approach.

     

Adaptive tracking control of two-wheeled welding mobile robot with smooth curved welding path

This paper proposes an adaptive controller for partially known system and applies to a two-wheeled Welding Mobile Robot (WMR) to track a reference welding path at a constant velocity of the welding point. To design the tracking controller, the errors from WMR to steel wall is defined, and the controller is designed to drive the errors to zero as fast as desired. Additionally, a scheme of error measurement is implemented on the WMR to meet the need of the controller. In this paper, the system moments of inertia are considered to be partially unknown parameters which are estimated using update laws in adaptive control scheme. The simulations and experiments on a welding mobile robot show the effectiveness of the proposed controller.