Online Learning ARMA Controllers With Guaranteed Closed-Loop Stability

Pedicle drilling force control of a robotic surgical system via spine-soft tissue coupling model and parameters optimization

Bone drilling is a crucial operation in spinal fusion surgery that requires precise control of the applied force to ensure surgical safety. This manuscript aims to enhance the force servo performance of the orthopedic robot during automatic bone drilling operations. Firstly, an analytical model is introduced to describe the spinal mobility of the spine-soft tissue coupling structure. Then, the model is calibrated using force data obtained from stress relaxation tests. Next, optimal force controller parameters are determined through drilling force control simulations based on the identified model. The dynamic performance and robustness of the closed-loop control system are analyzed to ensure safe drilling procedures. Finally, bone drilling experiments are conducted in a force control mode to verify the effectiveness of the proposed method. The step drilling force response's steady-state error is less than 0.15 N, the relative control error is less than 3 %, and there is no noticeable force overshoot. The amplitude of the sinusoidal force response decays to -3 dB when the target force frequency is up to 3.49 rad/s, indicating a wide control bandwidth. These results demonstrate that the proposed method can rapidly and safely provide an adequate force servo to carry out automatic bone drilling.

High-bandwidth image-based predictive laser stabilization via optimized Fourier filters

Controlling the delivery of kHz-class pulsed lasers is of interest in a variety of industrial and scientific applications, from next-generation laser-plasma acceleration to laser-based x-ray emission and high-precision manufacturing. The transverse position of the laser pulse train on the application target is often subject to fluctuations by external drivers (e.g., room cooling and heating systems, motorized optics stages and mounts, vacuum systems, chillers, and/or ground vibrations). For typical situations where the disturbance spectrum exhibits discrete peaks on top of a broad-bandwidth lower-frequency background, traditional PID (proportional-integral-derivative) controllers may struggle, since as a general rule PID controllers can be used to suppress vibrations up to only about 5%-10% of the sampling frequency. Here, a predictive feed-forward algorithm is presented that significantly enhances the stabilization bandwidth in such laser systems (up to the Nyquist limit at half the sampling frequency) by online identification and filtering of one or a few discrete frequencies using optimized Fourier filters. Furthermore, the system architecture demonstrated here uses off-the-shelf CMOS cameras and piezo-electric actuated mirrors connected to a standard PC to process the alignment images and implement the algorithm. To avoid high-end, high-cost components, a machine-learning-based model of the piezo mirror's dynamics was integrated into the system, which enables high-precision positioning by compensating for hysteresis and other hardware-induced effects. A successful demonstration of the method was performed on a 1 kHz laser pulse train, where externally-induced vibrations of up to 400 Hz were attenuated by a factor of five, far exceeding what could be done with a standard PID scheme.

Active disturbance rejection control design for high-order integral systems

Active disturbance rejection control (ADRC) is designed for regular processes frequently, due to its strong ability to reject disturbances and handle system uncertainties. However, ADRC design for high-order integral systems existing in many natural systems is always ignored. The controller design for high-order integral systems is different from the regular systems due to no pole in the left and right half s-planes. The ADRC design for high-order integral systems is studied to explain this question theoretically in this paper. Based on the equivalent form of ADRC, a theorem about the necessary condition of ADRC is proven which can guarantee the closed-loop system's stability. Additionally, the advantages of ADRC over proportional-integral-derivative (PID) controller in sensor noise rejection and control signal variation are analyzed theoretically. In order to achieve expected control performance for high-order integral systems, a practical design procedure of ADRC is summarized by the single variable method. Several comparative simulations and an experiment based on a ball and beam system are carried out. Running data verify that ADRC can obtain better control performance with strong robustness than PID controller. Eventually, a 100th order ADRC is designed for a 100th order integral system, and simulation results show that ADRC can be designed for high-order integral systems conveniently. Based on theoretical analyses and experimental verifications, ADRC shows some advantages for high-order integral systems.

Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input

In this paper, an optimal control algorithm is designed for uncertain nonlinear systems in discrete-time, which are in nonaffine form and with unknown dead-zone. The main contributions of this paper are that an optimal control algorithm is for the first time framed in this paper for nonlinear systems with nonaffine dead-zone, and the adaptive parameter law for dead-zone is calculated by using the gradient rules. The mean value theory is employed to deal with the nonaffine dead-zone input and the implicit function theory based on reinforcement learning is appropriately introduced to find an unknown ideal controller which is approximated by using the action network. Other neural networks are taken as the critic networks to approximate the strategic utility functions. Based on the Lyapunov stability analysis theory, we can prove the stability of systems, i.e., the optimal control laws can guarantee that all the signals in the closed-loop system are bounded and the tracking errors are converged to a small compact set. Finally, two simulation examples demonstrate the effectiveness of the design algorithm.