含间隙机械系统的混杂模型预测控制器

研究含间隙机械系统的混杂模型预测控制问题.首先,将含间隙机械系统的运行模式分为"间隙模式"和"接触模式".其次,建立了含间隙机械系统的混杂分段仿射 (PWA)模型.然后,利用模型预测控制 (MPC)的方法对约束PWA系统的最优控制进行求解,通过动态规划与多参数二次规划方法,得到了MPC的离线解.最后,通过将分段二次 (PWQ)Lyapunov函数的求解转换成半正定规划,找到了确保闭环控制稳定性的PWQ Lyaplanov函数.跟踪参考速度的实验结果表明,混杂模型预测控制器对含间隙机械系统的跟踪控制具有较好的效果,能够满足小采样时间系统的实时控制要求.     

一种新的基于PSO的MPC及其硬件实现

模型预测控制(MPC)在流程工业中应用已经比较成熟.其核心为在线求解二次规划(QP)问题,运算负荷大时延长,对控制器的运算能力要求高,阻碍了MPC向更深更广的应用领域拓展.为解决上述问题,从算法本身和硬件平台2个方面入手,提出了MPC算法一种新的实现方案.新的以粒子群优化算法(PSO)为核心的MPC算法很好地解决了带约束的二次规划问题,并且以可编程逻辑门阵列(FPGA)为平台用实现了PSO-MPC控制器.这一方案使得MPC可以应用在控制器体积受限,采样频率高的运动控制场合.     

四旋翼无人机安全轨迹跟踪控制

针对存在安全约束的四旋翼无人机,为了保证其能够快速稳定地跟踪给定轨迹,本文提出了一种基于双闭环思想及控制障碍函数求解二次规划问题的控制器设计框架.首先,考虑到无人机的模型不确定性及外界干扰问题,基于快速非奇异终端滑模面设计了双闭环标称控制器,能够实现有限时间快速收敛.进一步地,为了解决无人机遇到的状态、距离约束等安全控制问题,利用控制障碍函数,将带有约束的控制器设计问题转化成二次规划的求解问题.最后,对提出的控制策略进行了仿真,验证了控制器的快速性和鲁棒性,并实现了给定轨迹的安全跟踪.     

压水堆蒸汽发生器水位的前馈模型预测控制

在压水堆核电站中,引起反应堆停堆的主要原因之一是蒸汽发生器(SG)的水位超过了安全界限。为将SG水位控制在一定范围内以保证核电站安全、可靠、经济地运行,将蒸汽流量作为可测扰动加入预测模型中,设计了具有前馈补偿作用的模型预测控制器(MPC)。对给水流量增量施加约束以减小对给水阀的冲击,采用二次规划(QP)最优化算法求解给水流量的最优值,求解时先对水位施加硬约束,若无可行解则加入软约束,以保证系统的稳定性。仿真结果表明,与变增益PID控制相比,MPC的控制效果更好。     

基于线性二次型高斯基准的多变量预测控制技术经济性能评估

由于受控过程参数的漂移及缺乏维护,令采用的控制器性能逐渐降低,需要做经济性能评估,以确保其最佳运行状态.因为目前最小方差评估算法没有考虑控制器的约束条件,对此我们采用线性二次型高斯(linearquadratic Gaussian,LQG)基准的模型预测控制(model predictive control,MPC)双层优化控制结构,将控制和输出的加权值引入上层经济性能指标,通过求解LQG问题获取控制与输出方差关系的离散点集,进一步拟合Pareto最优曲面方程,建立优化命题并求解最优经济指标及设定值.对延迟焦化加热炉的多变量MPC控制进行了性能评估及分析,证明该方法可以改进控制器设计,提高经济效益.     

基于Volterra模型的预测控制及应用

由于工业实践的需要,非线性预测控制近年来受到广泛地关注.Volterra模型是一类特殊的非线性模型,非常适合描述工业过程中的无记忆非线性对象.传统的基于Volterra模型的控制器合成法及迭代计算预测控制器法计算量大,且不便于处理控制约束.非线性模型预测控制求解是典型的非线性规划问题,序列二次规划(sequential quadratic program,SQP)算法是求解非线性规划问题常用方法之一.针对Volterra非线性模型预测控制求解问题,本文将滤子法与一种信赖域SQP算法相结合,提出一种改进SQP算法用于基于非线性Volterra模型的带控制约束的多步预测控制求解,并分析了所提方法的收敛性.工业实例仿真结果证实了所提方法的可行性与有效性.     

基于粒子群优化的有约束模型预测控制器

研究了模型预测控制(MPC)中解决带约束的优化问题时所用到的优化算法,针对传统的二次规划(QP)方法的不足,引入了一种带有混沌初始化的粒子群优化算法(CPSO),将其应用到模型预测控制中,用十解决同时带有输入约束和状态约束的控制问题.最后,引入了一个实际的带有约束的线性离散系统的优化控制问题,分别用二次规划和粒子群优化两种算法去解决,通过仿真结果的比较,说明了基于粒子群优化(PSO)的模型预测控制算法的优越性.     

基于高阶观测器和干扰补偿控制的模型预测控制方法

针对状态不可测、外部干扰未知, 并且状态和输入受限的离散时间线性系统, 将高阶观测器、干扰补偿控制与标准模型预测控制(Model predictive control, MPC)相结合, 提出了一种新的MPC方法. 首先利用高阶观测器同步观测未知状态和干扰, 使得观测误差一致有界收敛;然后基于该干扰估计值设计新的干扰补偿控制方法, 并将该方法与基于状态估计的标准MPC相结合, 实现上述系统的优化控制. 所提出的MPC方法克服了利用现有MPC方法求解具有外部干扰和状态约束的优化控制问题时存在无可行解的局限, 能够保证系统状态在每一时刻都满足约束条件, 并且使系统的输出响应接近采用标准MPC方法控制线性标称系统时得到的输出响应. 最后, 将所提控制方法应用到船舶航向控制系统中, 仿真结果表明了所提方法的有效性和优越性.     

基于收缩约束模型预测控制的无人车辆路径跟踪

针对存在有界扰动的非线性无人驾驶车辆避障过程中最优路径规划跟踪问题,提出一种基于预测时域内系统输入输出收缩约束(PIOCC)的模型预测控制(MPC)方法.首先在构建目标函数时,为扩大可行性解的范围引入软约束思想,将最优规划路径的跟随问题转化为对模型预测控制优化问题的求解;其次为避免短预测时域造成闭环系统发散而导致在约束条件限定下出现无可行性解的情况,采用预测时域内系统输入输出收缩约束的方法,设计模型预测控制器;再次基于Lyapunov稳定性理论证明所设计的模型预测闭环控制系统是渐近稳定的;最后通过仿真实例验证了所提出基于PIOCC的控制策略在解决扩大可行解范围和避免闭环系统发散问题时的有效性,实现了无人驾驶车辆在路径跟踪时具有良好的快速性和稳定性.     

六自由度机械臂约束预测控制系统的设计

本文提出了一种基于约束预测控制的机械臂实时运动控制方法.该控制方法分为两层,分别设计了约束预测控制器和跟踪控制器.其中,约束预测控制器在考虑系统物理约束的条件下,在线为跟踪控制器生成参考轨迹;跟踪控制器采用最优反馈控制律,使机械臂沿参考轨迹运动.为了简化控制器的设计和在线求解,本文采用输入输出线性化的方式简化机械臂动力学模型.同时,为了克服扰动,在约束预测控制器中引入前馈策略,提出了带前馈一反馈控制结构的预测控制设计.因此,本文设计的控制器可以使机械臂在满足物理约束的条件下快速稳定地跟踪到目标位置.通过在PUMA560机理模型上进行仿真实验,验证了预测控制算法的可行性和有效性.     

Tracking control of small-scale helicopters using explicit nonlinear MPC augmented with disturbance observers

Small-scale helicopters are very attractive for a wide range of civilian and military applications due to their unique features. However, the autonomous flight for small helicopters is quite challenging because they are naturally unstable, have strong nonlinearities and couplings, and are very susceptible to wind and small structural variations.A nonlinear optimal control scheme is proposed to address these issues. It consists of a nonlinear model predictive controller (MPC) and a nonlinear disturbance observer. First, an analytical solution of the MPC is developed based on the nominal model under the assumption that all disturbances are measurable. Then, a nonlinear disturbance observer is designed to estimate the influence of the external force/torque introduced by wind turbulences, unmodelled dynamics and variations of the helicopter dynamics. The global asymptotic stability of the composite controller has been established through stability analysis. Flight tests including hovering under wind gust and performing very challenging pirouette have been carried out to demonstrate the performance of the proposed control scheme.     

Advances in Modeling and Control of Tethered Unmanned Helicopters to Enhance Hovering Performance

The hovering capabilities of unmanned helicopters can be seriously affected by wind. One possible solution for improving hovering performance under such circumstances is the use of a tethered setup that takes advantage of the tension exerted on the cable that links the helicopter to the ground. This paper presents a more elaborate strategy for helicopter control in this augmented setup, that extends previous work on the subject by the authors. Particularly, a combination of classical PID control laws, together with model inversion blocks, constitutes the basis of the new controller. Additionally, feed-forward action for counteracting rotational couplings is also taken into account. The resulting nonlinear control structure considers also a viscoelastic model of the tether which accurately reproduce the behavior of real ropes. Several simulations under artificially generated wind influences are presented to endorse the validity of the new proposed controller.     

基于ESO的无人直升机高精度姿态控制

针对小型无人直升机存在模型参数不确定性、电磁干扰影响的问题,设计了一种基于ESO（扩张状态观测器）的高精度姿态控制方法．直升机姿态通道中的不确定部分及外界复合扰动被视为总扰动,通过ESO进行实时估计,结合状态反馈控制器实现扰动消除．试验结果表明,在0.1 s内姿态角可从0°快速跟踪到5°且无超调．最后将设计的控制器应用于研制的高精度无人驾驶系统,实现系统参数变动等条件下直升机的全自主定点悬停和航迹飞行．     

Robust constrained predictive control using comparison model

This paper proposes a quadratic programming (QP) approach to robust model predictive control (MPC) for constrained linear systems having both model uncertainties and bounded disturbances. To this end, we construct an additional comparison model for worst-case analysis based on a robust control Lyapunov function (RCLF) for the unconstrained system (not necessarily an RCLF in the presence of constraints). This comparison model enables us to transform the given robust MPC problem into a nominal one without uncertain terms. Based on a terminal constraint obtained from the comparison model, we derive a condition for initial states under which the ultimate boundedness of the closed loop is guaranteed without violating state and control constraints. Since this terminal condition is described by linear constraints, the control optimization can be reduced to a QP problem.     

小型无人直升机姿态非线性鲁棒控制设计

本文针对小型无人直升机的姿态控制问题,通过系统参数辨识,获得了较为准确的无人直升机姿态动力学模型.并根据无人直升机的动态特性,设计了基于神经网络前馈与滑模控制的非线性鲁棒姿态控制律,该控制律对直升机模型的先验知识要求较低.利用基于Lyapunov的分析方法证明,设计的控制律能够实现对无人直升机姿态角的半全局指数收敛镇定控制,并能确保闭环系统的稳定性.基于姿态飞行控制实验平台的实时飞行控制实验结果表明,提出的控制设计取得了很好的姿态控制效果,并对系统不确定性和外界风扰动具有较好的鲁棒性.     

On the stability of constrained MPC without terminal constraint

The usual way to guarantee stability of model predictive control (MPC) strategies is based on a terminal cost function and a terminal constraint region. This note analyzes the stability of MPC when the terminal constraint is removed. This is particularly interesting when the system is unconstrained on the state. In this case, the computational burden of the optimization problem does not have to be increased by introducing terminal state constraints due to stabilizing reasons. A region in which the terminal constraint can be removed from the optimization problem is characterized depending on some of the design parameters of MPC. This region is a domain of attraction of the MPC without terminal constraint. Based on this result, it is proved that weighting the terminal cost, this domain of attraction of the MPC controller without terminal constraint is enlarged reaching (practically) the same domain of attraction of the MPC with terminal constraint; moreover, a practical procedure to calculate the stabilizing weighting factor for a given initial state is shown. Finally, these results are extended to the case of suboptimal solutions and an asymptotically stabilizing suboptimal controller without terminal constraint is presented.     

Formation control of multiple mecanum-wheeled mobile robots with physical constraints and uncertainties

Aiming at the formation control of multiple Mecanum-wheeled mobile robots (MWMRs) with physical constraints and model uncertainties, a novel robust control scheme that combines model predictive control (MPC) and extended state observer-based adaptive sliding mode control (ESO-ASMC) is proposed in this paper. First, a linear MPC strategy is proposed to address the motion constraints of MWMRs, which can transform the robot formation model based on leader-follower into a constrained quadratic programming (QP) problem. The QP problem can be solved iteratively online by a delay neural network (DNN) to obtain the optimal control velocity of the follower robot. Then, to address the input saturation constraints, model uncertainties and unknown disturbances in the dynamic model, an improved ESO-ASMC is proposed and compared with the robust adaptive terminal sliding mode control (RATSMC) and the conventional sliding mode control (SMC) to prove the effectiveness. The proposed scheme, considering the optimal control velocity obtained by the kinematics controller as the given desired velocity of the dynamics controller, can implement precise formation control, while solving various physical constraints of the robot, and eliminating the effects of model uncertainties and disturbances. Finally, through a comparative simulation case, the effectiveness and robustness of the proposed method are verified.

     

Robust nonlinear MPC without terminal constraint for tracking changing setpoints in the presence of non-additive unknown disturbance

This paper develops a novel robust tracking model predictive control (MPC) without terminal constraint for discrete-time nonlinear systems capable to deal with changing setpoints and unknown non-additive bounded disturbances. The MPC scheme without terminal constraint avoids difficult computations for the terminal region and is thus simpler to design and implement. However, the existence of disturbances and/or sudden changes in a setpoint may lead to feasibility and stability issues in this method. In contrast to previous works that considered changing setpoints and/or additive slowly varying disturbance, the proposed method is able to deal with changing setpoints and non-additive non-slowly varying disturbance. The key idea is the addition of tightened input and state (tracking error) constraints as new constraints to the tracking MPC scheme without terminal constraints based on artificial references. In the proposed method, the optimal tracking error converges asymptotically to the invariant set for tracking, and the perturbed system tracking error remains in a variable size tube around the optimal tracking error. Closed-loop input-to-state stability and recursive feasibility of the optimization problem for any piece-wise constant setpoint and non-additive disturbance are guaranteed by tightening input and state constraints as well as weighting the terminal cost function by an appropriate stabilizing weighting factor. The simulation results of the satellite attitude control system are provided to demonstrate the efficiency of the proposed predictive controller.     

小型无人直升机悬停状态自适应模糊PID控制器设计

小型遥控直升机在实际应用中主要是完成航拍,为了实现小型直升机水平姿态的自动平衡,将其升级为无人直升机.针对小型直升机的降阶模型,提出了一种便于单片机实现的、针对悬停作业时小型无人直升机自适应模糊PID控制器的设计方法,能对PID参数进行动态整定,实现无人直升机悬停状态时水平姿态的平衡,仿真实验表明,自适应模糊PID控制器的动态性能好、稳态精度高、鲁棒性较强,宜适用于小型无人直升机的增稳控制.