Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework 一句话 MPC:在每个采用点处,根据被控对象的状态和预测模型,预测系统在未来一段时间内的状态,依据某一性能指标(成本函数)来求解最优的一组控制序列,并将这组控制序列的第一个控制作用作为输出给执行机构,在下一个采样点继续...
Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework 一、问题提出 Dynamic function 系统描述 二、LMPC 构建 Sampled Safe Set 采样安全集的构建 Iteration cost 迭代成本的构建 LMPC 公式的构建 三、证明 迭代可行性 渐近稳定性 成本非增性 最优性 四、实例 一句话 MPC: 在...
This study presents a framework for model predictive control (MPC)-based surface condensation prevention that can avoid the surface condensation during the cooling periods when the TABS is in operation. Because MPC determines the input signal for the system not only on the basis of the current ...
本文记录HPIPM: a high-performance quadratic programming framework for model predictive control,Gianluca Frison Moritz Diehl 。作者自称是他此前工作的升级版:High-Performance Small-Scale Solvers for …
In recent years, computer scientists have developed increasingly advanced algorithms for controlling the movements of robotic agents. These include model predictive control (MPC) techniques, which use a model of the agent's ...
Informer-based model predictive control framework considering group controlled hydraulic balance model to improve the precision of client heat load control... Informer-based model predictive control framework considering group controlled hydraulic balance model to improve the precision of client heat load ...
First, in the fractional-order model predictive control framework, we will focus on the design of a model predictive controller for a (possibly time-varying) discrete-time fractional-order dynamical system model (68)Δαx[k+1]=Akx[k]+Bku[k]+Bkww[k], where w[k] denotes a sequence of...
This thesis deals with a model predictive control framework for control design of Advanced Driver Assistance Systems, where car-following tasks are under control. The framework is applied to design several autonomous and cooperative controllers and to examine the controller properties at the microscopic ...
控制模型 (1)xt+1=f(xt,ut) (2)xt∈χ,ut∈υ 其中,(1)表示模型的动力学方程,(2)表示模型的状态约束、输入约束。不过我觉得这篇论文的约束还少了一个输入和状态的联合约束(比如功率约束)。 因为这个论文讨论的是迭代方法,所以将第j次迭代的输入和状态轨迹分别表示为: ...
However, we can trivially extend the framework developed here to time-varying parameters modeled as the Gauss–Markov process θ(t + 1) = Θθ(t) + w(t) where Θ is a known, constant matrix and w(t) is a sequence of independent and identically distributed Gaussian randomvectorswith zero...