Liting Sun

Optimization-based constrained iterative learning control with application to building temperature control systems

In this paper, an optimization-based constrained iterative learning control (ILC) with an iteratively tunable feedback controller is proposed for building temperature control systems. To guarantee good control performance in the presence of both repetitive and non-repetitive disturbances, the ILC input and the feedback controller are optimized simultaneously in each iteration. Considering constraints from the input saturation, the ILC convergence requirement and the closed-loop stability, the controller design is formulated as a convex optimization problem. The influence of disturbance uncertainties is also incorporated into the optimization problem in the form of chance constraints. To reduce the complexity of the problem, special techniques such as relaxation and projection on convex sets are introduced to make the algorithm more efficient. The effectiveness of the proposed algorithm is verified by simulations conducted on a four-room testbed system.

Arbitrary-Order Iterative Learning Control Considering H∞ Synthesis

Iterative learning control (ILC) is an effective technique to improve the tracking performance of systems through adjusting the feedforward control signal based on the memory data. It is critically important to design the learning filters in the ILC algorithm that assures the robust stability of the convergence of tracking errors from one iteration to next. The design procedure usually involves lots of tuning work especially in high-order ILC. To facilitate this procedure, this paper proposes an approach to design learning filters for an arbitrary-order ILC with guaranteed convergence and ease of tuning. The filter design problem is formulated into an H∞ optimal control problem. This approach is based on an infinite impulse response (IIR) system and conducted directly in iteration-frequency domain. Important characteristics of the proposed approach are explored and demonstrated on a simulated wafer scanning system.Copyright

Enhanced wide-spectrum vibration suppression based on adaptive loop shaping

In linear feedback control, the attenuation of disturbances at the designer-selected frequencies is subjected to the fundamental limitation of undesired error amplifications at other frequencies, due to the “waterbed” effect that is induced from Bodes Integral Theorem. In the presence of unknown disturbances with high-frequency wide-spectrum peaks, such undesired error amplifications severely degrades the closed-loop servo performance, and are extremely difficult to control using traditional loop shaping techniques. In this paper, a direct adaptive control approach is proposed based on adaptive loop shaping and disturbance observer (DOB). The proposed algorithm offers more flexibilities in controlling the “waterbed” effect, to achieve enhanced attenuation of the unknown wide-spectrum disturbances. Verification of the proposed algorithm is provided by simulations of hard disk drives (HDDs) for audio vibration suppression.

Matrix factorization for design of Q-filter in iterative learning control

Iterative learning control (ILC) has been extensively used in systems that repeatedly follow the same desired trajectory. The key idea is to incorporate the tracking errors from previous iterations to generate a better feedforward signal for the next iteration. A drawback of ILC is that all disturbances are assumed to be repetitive, while in practice non-repetitive disturbances may also affect the system behaviors. To address this problem, many efforts have been made on designing Q-filters to filter out the non-repetitive effects from the error signal. This paper presents a nonparametric Q-filter design procedure which does not require any explicit specification of the properties of non-repetitive disturbances. Namely, we perform matrix factorization on a set of error signals in the time-frequency domain to construct a non-repetitive error dictionary. The learned dictionary is then used to encode the error signal in each ILC iteration. This in turn results in a low-rank matrix and a sparse matrix that, respectively, describe the undesired non-repetitive effects and the desired repetitive effects. The effectiveness of the proposed method is demonstrated on a laboratory testbed wafer scanner.

Distributed and cooperative optimization-based iterative learning control for large-scale building temperature regulation

In this paper, a distributed and cooperative optimization-based iterative learning control (ILC) algorithm is proposed for large-scale building temperature control problems. With the algorithm, large-scale building temperature control problems are solvable with reasonable computational load and guaranteed control performance under nearly repetitive disturbances. The large-scale centralized system is separated into several distributed and cooperative small-scale subsystems that communicate among each other. For each subsystem, a convex optimization problem is solved. The cooperative learning policy allows all subsystems to contribute together to improve the overall performance. The convergence property of the algorithm is proved and simulation results are provided to demonstrate its effectiveness.

Robust dexterous manipulation under object dynamics uncertainties

Dexterous manipulation has broad applications in assembly lines, warehouses and agriculture. To perform broad-scale manipulation tasks, it is desired that a multi-fingered robotic hand can robustly manipulate objects without knowing the exact objects dynamics (i.e. mass and inertia) in advance. However, realizing robust manipulation is challenging due to the complex contact dynamics, the nonlinearities of the system, and the potential sliding during manipulation. In this paper, a dual-stage grasp controller is proposed to handle these challenges. In the first stage, feedback linearization is utilized to linearize the nonlinear uncertain system. Considering the structures of uncertainties, a robust controller is designed for such a linearized system to obtain the desired Cartesian force on the object. In the second stage, a manipulation controller regulates the contact force based on the Cartesian force from the first stage. The dual-stage grasp controller is able to realize robust manipulation without contact modeling, prevent the slippage, and withstand 40% mass and 50% inertia uncertainties. Moreover, it does not require velocity measurement or 3D/6D tactile sensor. Simulation results on Mujoco verify the efficacy of the proposed method. The simulation video is available at [1].

Multirate iterative learning control for enhanced motion performance with application to wafer scanner systems

Iterative learning control (ILC) is an effective control technique for servo improvement in systems that repetitively execute the same tasks. In the learning process, the measured tracking error from the current iteration is incorporated to generate a new feedforward compensation signal to improve the system performance in the next iteration. Due to its discrete-time implementation, conventional ILC only considers errors at the sampled output points without inter-sample learning ability. Therefore, its achievable performance is limited by the output sampling rate. In this paper, a multirate ILC (MRILC) approach is proposed. Based on multirate Kalman Smoother and multirate feedforward control, the ILC update law in a multirate two-degree-of-freedom (2-DOF) control system with a fast feedforward ILC input but a slow output sampling rate is derived. The bandwidth of the learning loop may then be extended beyond that of the feedback loop for enhanced inter-sample learning. The effectiveness of the proposed MRILC is verified by experiments on a wafer scanner system.