Chanying Li
This paper studies the identification of nonlinearly parameterized control systems in given experiments. Several identifiability criteria are established and an implementable algorithm is proposed for practicality with the convergence rate explicitly computed.
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While many techniques have been developed for chance constrained stochastic optimal control with Gaussian disturbance processes, far less is known about computationally efficient methods to handle non-Gaussian processes. In this paper, we develop a method for solving chance constrained stochastic optimal control problems for linear time-invariant systems with general additive disturbances with finite moments and unimodal chance constraints. We propose an open-loop control scheme for multi-vehicle planning, with both target sets and collision avoidance constraints. Our method relies on the one-…
Robust Control Lyapunov-Value Functions for Nonlinear Disturbed Systems
本文提出鲁棒控制Lyapunov值函数用于非线性扰动系统，通过哈密尔顿-雅可比可达性分析构建非光滑CLF，并解决维度灾难问题，通过预热启动和系统分解提升计算效率。
This paper deals with the problem of predicting the future state of discrete-time input-delayed systems in the presence of unknown disturbances that can affect both the state and the output equations of the plant. Since the disturbance is unknown, computing an exact prediction of the future plant states is not possible. To circumvent this problem, we propose using a high-order extended Luenberger-type observer for the plant states, disturbances, and their finite difference variables, combined with a new equation for computing the prediction based on Newton's series from the calculus of finite …
We consider the Chance Constrained Model Predictive Control problem for polynomial systems subject to disturbances. In this problem, we aim at finding optimal control input for given disturbed dynamical system to minimize a given cost function subject to probabilistic constraints, over a finite horizon. The control laws provided have a predefined (low) risk of not reaching the desired target set. Building on the theory of measures and moments, a sequence of finite semidefinite programmings are provided, whose solution is shown to converge to the optimal solution of the original problem. Numeri…
This paper develops a new active fault tolerant control system based on the concept of analytical redundancy. The novel design consists of an observation filter based fault detection and identification system integrated with a nonlinear model predictive controller. A number of observation filters were designed, integrated with the nonlinear controller and tested before reaching the final design which comprises an unscented Kalman filter for fault detection and identification together with a nonlinear model predictive controller to form an active fault tolerant control system design.
We present an adaptive event-triggered boundary control scheme for a parabolic PDE-ODE system, where the reaction coefficient of the parabolic PDE, and the system parameter of a scalar ODE, are unknown. In the proposed controller, the parameter estimates, which are built by batch least-square identification, are recomputed and the plant states are resampled simultaneously. As a result, both the parameter estimates and the control input employ piecewise-constant values. In the closed-loop system, the following results are proved: 1) the absence of a Zeno phenomenon; 2) finite-time exact identif…
This article presents an identification methodology to capture general relationships, with application to piecewise nonlinear approximations of model predictive control for constrained (non)linear systems. The mathematical formulation takes, at each iteration, the form of a constrained linear (or quadratic) optimization problem that is mathematically feasible as well as numerically tractable. The efficiency of the devised methodology is demonstrated via two industrial applications. Results suggest the possibility to achieve high approximate precision with limited number of regions, leading to …
This paper provides a new method to solve analytic interpolation problems with rationality and derivative constraints, occurring in many applications to system and control. It is based on the covariance extension equation previously proposed by Byrnes and Lindquist in a different context. A complete solution for the scalar problem is provided, and a homotopy continuation method is presented and applied to some problems in modeling and robust control. Some numerical examples illustrate robustness and efficiency of the proposed procedure.
The attitude control of a quadrotor is a fundamental problem, which has a pivotal role in a quadrotor stabilization and control. What makes this problem more challenging is the presence of uncertainty such as unmodelled dynamics and unknown parameters. In this paper, to cope with uncertainty, an $H_\infty$ control approach is adopted for a real quadrotor. To achieve $H_\infty$ controller, first a continuous-time system identification is performed on the experimental data to encapsulate a nominal model of the system as well as a multiplicative uncertainty. By this means, $H_\infty$ controllers …
MPC and System Identification with Differentiable Physics: Fluid System and Particle Beam Control
本文提出基于可微物理的模型预测控制框架，用于同时进行控制与参数估计，通过自动微分实现流体系统和粒子束的实时优化控制。
Numerical homotopy continuation for control and online identification of nonlinear systems: the survey of selected results
本文综述了参数数值延拓方法在非线性系统设定点控制与参数辨识中的应用，涵盖仿射系统与非仿射系统，并展示了线性与非线性非凸参数化下的在线辨识仿真结果。
Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
综述面向控制的系统辨识方法，涵盖经典、机器学习及物理信息方法。通过将系统辨识与控制相关或物理信息特性结合，以优化问题形式强化特性，综述相关方法并给出示例，还指出未来研究挑战方向。
We introduce the problem of parameter identification for a coupled nonlocal Cahn-Hilliard-reaction-diffusion PDE system stemming from a recently introduced tumor growth model. The inverse problem of identifying relevant parameters is studied here by relying on techniques from optimal control theory of PDE systems. The parameters to be identified play the role of controls, and a suitable cost functional of tracking-type is introduced to account for the discrepancy between some a priori knowledge of the parameters and the controls themselves. The analysis is carried out for several classes of mo…
By transforming identification and control for nonlinear system into optimization problems, a novel optimization method named state transition algorithm (STA) is introduced to solve the problems. In the proposed STA, a solution to a optimization problem is considered as a state, and the updating of a solution equates to a state transition, which makes it easy to understand and convenient to implement. First, the STA is applied to identify the optimal parameters of the estimated system with previously known structure. With the accurate estimated model, an off-line PID controller is then designe…
Focusing on identification, this paper develops techniques to reconstruct zero and nonzero elements of a sparse parameter vector of a stochastic dynamic system under feedback control, for which the current input may depend on the past inputs and outputs, system noises as well as exogenous dithers. First, a sparse parameter identification algorithm is introduced based on L2 norm with L1 regularization, where the adaptive weights are adopted in the optimization variables of L1 term. Second, estimates generated by the algorithm are shown to have both set and parameter convergence. That is, sets o…
Online identification and control of PDEs via Reinforcement Learning methods
本文针对未知偏微分方程控制问题，提出结合状态依赖黎卡提控制与贝叶斯线性回归在线辨识的强化学习算法，并通过数值实验验证其在无限时域控制问题中的收敛性。
On Data-Driven Model Identification for Nonlinear Optimal Control
本文针对非线性最优控制中的数据驱动模型辨识，揭示高阶大域近似存在方差与病态性诅咒，并提出迭代辨识局部LTV模型结合ILQR的方法，可有效缓解问题并局部精确求解。
We consider the problem of safe control design for a class of nonlinear, control-affine systems subject to an unknown, additive, nonlinear disturbance. Leveraging recent advancements in the application of Koopman operator theory to the field of system identification and control, we introduce a novel fixed-time identification scheme for the infinitesimal generator of the infinite-dimensional, but notably linear, Koopman dynamical system analogous to the nonlinear system of interest. That is, we derive a parameter adaptation law that allows us to recover the unknown, residual nonlinear dynamics …
An adjoint control method for initial condition identification of the Abstract Cauchy problem
针对抽象柯西问题的初始条件识别，提出并分析了一种基于伴随方程控制的通用方法，通过分析和数值实验验证了其有效性。
Active Fault Identification and Robust Control for Unknown Bounded Faults via Volume-Based Costs
本文提出一种基于集员辨识和椭球体积代价的主动故障诊断框架，结合管道模型预测控制，在不依赖预定义故障模型的情况下实现更快、更鲁棒的故障辨识与闭环控制。
Robust Topology Identification and Control of LTI Networks
本文提出一种鲁棒方案，用于识别和控制线性动态网络的拓扑结构，通过自适应反馈输入和滑模控制结合，实现网络动态渐近跟踪参考动态，并通过仿真验证理论成果。
In control design most control strategies are model-based and require accurate models to be applied successfully. Due to simplifications and the model-reality-gap physics-derived models frequently exhibit deviations from real-world-systems. Likewise, purely data-driven methods often do not generalise well enough and may violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired loss functions separately have shown promising results to conquer these drawbacks. In this contribution we extend existing methods towards the identification of non-autonomous systems and…
System Identification and Control of Valkyrie through SVA--Based Regressor Computation
本文利用空间向量代数(SVA)实现瓦尔基里人形机器人的同时辨识与控制，通过空间惯性张量计算动力学参数，提出高效算法实现回归器评估，用于在线系统辨识与基于模型的控制。
Data-Driven Identification of Attack-free Sensors in Networked Control Systems
本文提出了一种数据驱动方法，用于在线识别网络化控制系统中未受攻击的传感器，并通过数值实验验证了其有效性。
End-to-end guarantees for indirect data-driven control of bilinear systems with finite stochastic data
本文提出具有稳定性保证的双线性系统间接数据驱动控制端到端算法，通过统计学习理论推导有限样本识别误差界，并结合鲁棒控制器设计实现指数稳定的闭环控制。
Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds
针对未知时变Markov跳变线性系统，论文提出基于单轨迹辨识与确定性等价控制的回合式自适应方案，证明其样本复杂度及 O(√T) 遗憾，并在部分已知条件下改进至 O(polylog(T))。
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