执行器约束下基于数据驱动的参数化前馈控制器设计

针对执行器约束下非重复性点到点运动的轨迹跟踪问题, 提出了一种在执行器约束下基于数据驱动的参数化输入整形滤波器和前馈控制器优化设计算法. 首先对输入整形滤波器以及前馈控制器进行参数化, 然后在目标函数中加入控制信号变化量与控制信号能量的约束, 再采用基于数据驱动的迭代寻优算法得到最优参数, 在该参数下可以实现满足执行器约束条件下的运动控制系统轨迹最优跟踪性能. 并且由于采用了前馈参数化设计方法, 在点到点轨迹发生变化时所提出算法依然能够保持良好的轨迹跟踪性能. 仿真与实验结果表明在执行器约束下所提出算法能够实现最优点到点轨迹跟踪性能, 并且对非重复性点到点轨迹跟踪具有一定的鲁棒性.     

Which is the Best PID Variant for Pneumatic Soft Robots?An Experimental Study

This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction.However,due to structural flexibility and compliance,mathematical models for these soft robots are nonlinear with an infinite degree of freedom(DOF).Therefore,accurate position(or orientation)control and optimization of their dynamic response remains a challenging task.Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID.However,to the best of our knowledge,there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response,i.e.,reduce overshoot and settling time.In this paper,we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results.Additionally,most of the existing work on modelfree control in pneumatic soft-robotic literature use manually tuned parameters,which is a time-consuming,labor-intensive task.We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically.We presented results for both manual tuning and automatic tuning using the Ziegler-Nichols method and proposed algorithm,respectively.We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy.The experiment results show that for soft robots,the PID-controller essentially reduces to the PI controller.This behavior was observed in both manual and automatic tuning experiments;we also discussed a rationale for removing the derivative term.     

环卫车辆轨迹跟踪系统的无模型自适应迭代学习控制

针对环卫车辆周期重复性工作特点,考虑模型时变以及未知扰动问题,提出一种基于无模型自适应迭代学习的环卫车辆轨迹跟踪控制方法.首先,针对环卫车辆建立了两轮移动机器人的运动学模型,然后,给出带时变参数和非线性不确定项的迭代域下全格式动态线性化数据模型,引入时间差分估计算法,设计基于最优性能指标的轨迹跟踪无模型自适应迭代学习控...     

结合序列线性规划法的混合遗传算法

通过将遗传算法与改进的序列线性规划法相结合，形成混合遗传算法．当迭代点没有发生交叉和变异时，将目标函数和约束条件在迭代点处线性化，为使迭代点邻域仍然满足约束条件，加入软约束项，用线性规划方法进行寻优．该方法具有全局收敛性，不要求迭代点一定为可行点．仿真结果验证了此法的有效性和合理性．     

New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm

In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder–Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.     

A new method for analysis and design of iterative learning control algorithms in the time-domain

In this paper, a novel analysis method for iterative learning control (ILC) algorithms is presented. Even though expressed in the lifted system representation and hence in the time-domain, the convergence rate as a function of the frequency content of the error signal can be determined. Subsequently, based on the analysis method, a novel ILC algorithm (F-ILC) is proposed. The convergence rate at specific frequencies can be set directly in the design process, which allows simple tuning and a priori known convergence rates. Using the F-ILC design, it is shown how to predict the required number of iterations until convergence is achieved, depending on the reference trajectory and information on the system repeatability. Numerical examples are given and experimental results obtained on an internal combustion engine test bench are shown for validation.     

新型同步发电机励磁控制器的设计

针对传统的基于迭代学习控制算法的同步发电机励磁控制器存在初始控制信号由经验确定的问题,提出了一种基于即时学习型迭代学习控制算法的同步发电机励磁控制器的设计方案。该方案在迭代学习控制算法中引入即时学习算法,利用即时学习算法计算初始控制信号,有效减少了初始控制信号与理想控制信号之间的误差。仿真结果表明,该励磁控制器收敛速度快,具有更强的维持机端电压的能力。     

基于虚拟参考反馈整定的改进无模型自适应控制

针对一类离散时间非线性系统, 提出一种基于虚拟参考反馈整定的改进无模型自适应控制方案. 首先, 利用动态线性化方法给出非线性系统的紧格式动态线性化模型; 然后, 基于优化技术设计控制算法和伪偏导数估计算法; 最后, 设计基于虚拟参考反馈整定的伪偏导数初值和重置值的估计算法. 该控制方案设计仅依赖于被控系统的输入和输出数据, 且能保证闭环系统的稳定性和收敛性. 仿真比较结果验证了所提出方法的有效性.

     

一种离-在线混合数据驱动控制方法及其在直流电动机中的应用

针对离线和在线数据驱动控制方法各自存在的不足,提出一种离-在线混合数据驱动控制方法。首先给出一种基于最小二乘支持向量机和虚拟目标值反馈整定的离线数据驱动控制方法;其次在二自由度控制系统框架下,结合无模型自适应控制,给出一种离-在线混合数据驱动控制方法的结构和设计步骤。该方法跳过被控对象建模过程,大大降低了控制器设计成本,且可避免引入模型误差。将该方法应用于直流电动机离-在线数据驱动控制中,并进行了仿真实验,结果表明,离-在线混合数据驱动控制方法可以有效地实现目标信号跟踪和电动机系统的不确定(外部扰动)抑制。     

Intelligent tracking control for robot manipulator including actuator dynamics via TSK-type fuzzy neural network

In this paper, a Takagi-Sugeno-Kang-type fuzzy-neural-network control (T-FNNC) scheme is constructed for an n-link robot manipulator to achieve high-precision position tracking. According to the concepts of mechanical geometry and motion dynamics, the dynamic model of an n-link robot manipulator including actuator dynamics is introduced initially. However, it is difficult to design a suitable model-based control scheme due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, a T-FNNC system without the requirement of prior system information and auxiliary control design is investigated to the joint position control of an n-link robot manipulator for periodic motion. In this model-free control scheme, a five-layer fuzzy-neural-network is utilized for the major control role, and the adaptive tuning laws of network parameters are established in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. In addition, experimental results of a two-link robot manipulator actuated by dc servomotors are provided to verify the effectiveness and robustness of the proposed T-FNNC methodology.     

基于风驱动优化算法WDO的PID参数优化

鉴于PID控制器的优越性,其在工业控制领域中的引用越来越广泛。PID控制器的性能主要在于其参数优化设计,PID参数优化问题一直是研究热点。为了解决PID参数优化问题,提出了一种基于自然启发的风驱动优化算法（WDO）的PID优化控制方法,该算法以PID三个参量为控制对象,以误差绝对值和控制输入平方项的时间积分作为优化目标,经过迭代寻优计算得到系统最优控制量。通过计算机仿真,并与遗传算法和粒子群算法PID参数优化相比,结果表明：该算法提高了系统的控制精度、响应速度和鲁棒性,为控制系统PID参数整定提供了参考。     

基于二进制GA的B样条重构曲线节点优化

带法向约束的自由曲线曲面重构在光学反射面设计中起至关重要的作用.本文为解决法向约束下的曲线重构问题提出了一种优化方案,使得重构出的曲线在逼近数据点的同时,亦能满足相应法向约束.首先,利用惩罚函数的方法将带法向约束的优化问题转化为无约束的优化问题.然后,引入二进制编码的遗传算法（GA）,建立合适的适应度函数,自适应产生优化节点向量,如此迭代进化,直到产生令人满意的重构曲线为止.考虑到节点向量非递减的特性,而遗传算法在寻找最优节点向量的过程中有可能打乱节点向量的顺序,所以在建立适应度函数的时候将变量调整为无序有界变量.通过与传统最小二乘方法和粒子群智能优化方法的比较,本文方案在解决带法向条件约束的曲线重构问题上优势明显,且对于任意形状的曲线重构都行之有效.     

基于贝叶斯压缩感知的信号重构

本文提出了基于贝叶斯压缩感知的信号重构算法,将压缩感知理论应用于信号的压缩传输以及重构,该算法将压缩感知问题转化为线性回归问题,逐步推演出结果向量之间的迭代关系,最后通过迭代以得到原始信号的精确重构. 仿真说明了贝叶斯压缩感知在信号处理中的应用,结果表明该算法对一维和二维信号的压缩重构有很好的效果.     

Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

This paper proposes the combination of two model-free controller tuning techniques, namely linear virtual reference feedback tuning (VRFT) and nonlinear state-feedback Q-learning, referred to as a new mixed VRFT-Q learning approach. VRFT is first used to find stabilising feedback controller using input-output experimental data from the process in a model reference tracking setting. Reinforcement Q-learning is next applied in the same setting using input-state experimental data collected under perturbed VRFT to ensure good exploration. The Q-learning controller learned with a batch fitted Q iteration algorithm uses two neural networks, one for the Q-function estimator and one for the controller, respectively. The VRFT-Q learning approach is validated on position control of a two-degrees-of-motion open-loop stable multi input-multi output (MIMO) aerodynamic system (AS). Extensive simulations for the two independent control channels of the MIMO AS show that the Q-learning controllers clearly improve performance over the VRFT controllers.     

改进差分进化算法在可靠性冗余分配问题中的应用

提出一种改进差分进化算法(IDE),以解决系统可靠性冗余分配问题.在罚函数法的基础上,对约束处理方法进行改进. 新约束处理方法在搜索过程中不需要在每一步都计算惩罚函数值,加快了寻优速度.具有良好的通用性,可以引入到其他智能优化算法中.将改进的算法用于求解4类典型的系统可靠性冗余分配问题,实验结果表明了所提出的改进算法具有很好的寻优精度和收敛速度.     

基于信息融合最优估计的非线性离散系统预测控制

针对非线性离散系统的二次型最优预测控制问题, 提出了一种基于信息融合最优估计的迭代预测控制算法. 通过融合二次型性能指标函数中包含的未来参考轨迹和控制能量的软约束信息, 以及系统状态方程和输出方程的硬约束信息, 获得协状态序列和控制序列的最优估计. 通过二自由度机器人操作手的转移控制仿真, 表明了该控制算法具有良好的稳定性和鲁棒性.     

基于分布估计算法的单路口交通信号控制

针对遗传算法求解城市道路交叉口信号控制存在的主要问题,以四相位交叉路口为研究对象,建立了以信号周期内车辆延误总时间最短为目标函数,以各相位有效绿灯时间为控制变量的单路口交通信号优化模型.并分别以整数编码的PBIL算法和实数编码的EMNA算法两种典型分布估计算法求解单路口交通信号优化问题.仿真结果表明,与传统遗传算法相比,两种分布估计算法均可用更小的种群规模快速高效地求得最优解.     

Optimal behaviour prediction using a primitive-based data-driven model-free iterative learning control approach

This paper suggests an optimal behaviour prediction mechanism for Multi Input-Multi Output control systems in a hierarchical control system structure, using previously learned solutions to simple tasks called primitives. The optimality of the behaviour is formulated as a reference trajectory tracking problem. The primitives are stored in a library of pairs of reference input/controlled output signals. The reference input primitives are optimized at the higher hierarchical level in a model-free iterative learning control (MFILC) framework without using knowledge of the controlled process. Learning of the reference input primitives is performed in a reduced subspace using radial basis functions for approximations. The convergence of the MFILC learning scheme is achieved via a Virtual Reference Feedback Tuning design of the feedback controllers in the lower level feedback control loops. The new complex trajectories to be tracked are decomposed into the output primitives regarded as basis functions. Next, the optimal reference input fed to the control system in order to track the desired new trajectory is then recomposed from the reference input primitives. The efficiency of this approach is demonstrated on a case study concerning the control of a two-axis positioning mechanism, and the experimental validation is offered.     

Multi-agent graphical games with input constraints: an online learning solution

This paper studies an online iterative algorithm for solving discrete-time multi-agent dynamic graphical games with input constraints. In order to obtain the optimal strategy of each agent, it is necessary to solve a set of coupled Hamilton-Jacobi-Bellman (HJB) equations. It is very difficult to solve HJB equations by the traditional method. The relevant game problem will become more complex if the control input of each agent in the dynamic graphical game is constrained. In this paper, an online iterative algorithm is proposed to find the online solution to dynamic graphical game without the need for drift dynamics of agents. Actually, this algorithm is to find the optimal solution of Bellman equations online. This solution employs a distributed policy iteration process, using only the local information available to each agent. It can be proved that under certain conditions, when each agent updates its own strategy simultaneously, the whole multi-agent system will reach Nash equilibrium. In the process of algorithm implementation, for each agent, two layers of neural networks are used to fit the value function and control strategy, respectively. Finally, a simulation example is given to show the effectiveness of our method.     

Predictive iterative learning control with experimental validation

This paper develops an iterative learning control law that exploits recent results in the area of predictive repetitive control where a priori information about the characteristics of the reference signal is embedded in the control law using the internal model principle. The control law is based on receding horizon control and Laguerre functions can be used to parameterize the future control trajectory if required. Error convergence of the resulting controlled system is analyzed. To evaluate the performance of the design, including comparative aspects, simulation results from a chemical process control problem and supporting experimental results from application to a robot with two inputs and two outputs are given.