A study on high accuracy discrete-time sliding mode control

In this paper a Discrete-Time Sliding-Mode based controller design for high accuracy motion control systems is presented. The controller is designed for a general SISO system with nonlinearity and external disturbance. Closed-Loop behavior of the general system with the proposed control and Lyapunov stability is shown and the error of the closed loop system is proven to be within an o(T2). The proposed controller is applied to a stage driven by a piezo drive that is known to suffer from hysteresis nonlinearity in the control gain. Proposed SMC controller is proven to offer chattering-free motion and rejection of the disturbances represented by hysteresis and the time variation of the piezo drive parameters. As a separate idea to enhance the accuracy of the closed loop system a combination of disturbance rejection method and the SMC controller is explored and its effectiveness is experimentally demonstrated. Closed-loop experiments are presented using PID controller with and without disturbance compensation and Sliding-Mode Controller with and without disturbance compensation for the purpose of comparison

Norm Optimal Iterative Learning Control with Application to Problems in Accelerator based Free Electron Lasers and Rehabilitation Robotics

This paper gives an overview of the theoretical basis of the norm optimal approach to iterative learning control followed by results that describe more recent work which has experimentally benchmarking the performance that can be achieved. The remainder of then paper then describes its actual application to a physical process and a very novel application in stroke rehabilitation

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

Current-cycle iterative learning control for high-precision position tracking of piezoelectric actuator system via active disturbance rejection control for hysteresis compensation

As a typical smart structure, piezoelectric actuator (PEA) is an essential constituent component in piezoelectric-driven positioning stages. Nevertheless, the positioning precision is severely degraded by its innate rate-dependent hysteretic nonlinearity. In this paper, an innovative control method which combines active disturbance rejection control (ADRC) and current-cycle iterative learning control (CILC) is proposed by constructing PEA as a second-order disturbance-based (SODB) structure to handle both hysteretic nonlinearities and dynamic uncertainties of PEA. The proposed method differs from the prevalent model-inverse solution in hysteresis compensation, where the control performance of the latter extremely relies on the accurateness of the hysteretic model while the former does not require a mathematical model of hysteresis since it is considered as a general disturbance and eliminated. Compared with the existing hysteresis compensation via pure ADRC method, the proposed method has improved robustness by incorporating an additional ILC loop to ADRC. Comparative experimentations are executed on a PEA system and results imply that the proposed approach has better control performance than pure proportionalintegral (PI) control and ADRC

Batch-to-batch iterative learning control of a fed-batch fermentation process

PhD ThesisRecently, iterative learning control (ILC) has been used in the run-to-run control of batch processes to directly update the control trajectory. The basic idea of ILC is to update the control trajectory for a new batch run using the information from previous batch runs so that the output trajectory converges asymptotically to the desired reference trajectory. The control policy updating is calculated using linearised models around the nominal reference process input and output trajectories. The linearised models are typically identified using multiple linear regression (MLR), partial least squares (PLS) regression, or principal component regression (PCR). ILC has been shown to be a promising method to address model-plant mismatches and unknown disturbances. This work presents several improvements of batch to batch ILC strategy with applications to a simulated fed-batch fermentation process. In order to enhance the reliability of ILC, model prediction confidence is incorporated in the ILC optimization objective function. As a result of the incorporation, wide model prediction confidence bounds are penalized in order to avoid unreliable control policy updating. This method has been proven to be very effective for selected model prediction confidence bounds penalty factors. In the attempt to further improve the performance of ILC, averaged reference trajectories and sliding window techniques were introduced. To reduce the influence of measurement noise, control policy is updated on the average input and output trajectories of the past a few batches instead of just the immediate previous batch. The linearised models are re-identified using a sliding window of past batches in that the earliest batch is removed with the newest batch added to the model identification data set. The effects of various parameters were investigated for MLR, PCR and PLS method. The technique significantly improves the control performance. In model based ILC the weighting matrices, Q and R, in the objective function have a significant impact on the control performance. Therefore, in the quest to exploit the potential of objective function, adaptive weighting parameters were attempted to study the performance of batch to batch ILC with updated models. Significant improvements in the stability of the performance for all the three methods were noticed. All the three techniques suggested have established improvements either in stability, reliability and/or convergence speed. To further investigate the versatility of ILC, the above mentioned techniques were combined and the results are discussed in this thesis