Sliding mode control of robotics systems actuated by pneumatic muscles.

This dissertation is concerned with investigating robust approaches for the control of pneumatic muscle systems. Pneumatic muscle is a novel type of actuator. Besides having a high ratio of power to weight and flexible control of movement, it also exhibits many analogical behaviors to natural skeletal muscle, which makes them the ideal candidate for applications of anthropomorphic robotic systems. In this dissertation, a new phenomenological model of pneumatic muscle developed in the Human Sensory Feedback Laboratory at Wright Patterson Air Force Base is investigated. The closed loop stability of a one-link planar arm actuated by two pneumatic muscles using linear state feedback is proved. Robotic systems actuated by pneumatic muscles are time-varying and nonlinear due to load variations and uncertainties of system parameters caused by the effects of heat. Sliding mode control has the advantage that it can provide robust control performance in the presence of model uncertainties. Therefore, it is mainly utilized and further complemented with other control methods in this dissertation to design the appropriate controller to perform the tasks commanded by system operation. First, a sliding mode controller is successfully proposed to track the elbow angle with bounded error in a one-Joint limb system with pneumatic muscles in bicep/tricep configuration. Secondly, fuzzy control, which aims to dynamically adjust the sliding surface, is used along with sliding mode control. The so-called fuzzy sliding mode control method is applied to control the motion of the end-effector in a two-Joint planar arm actuated by four groups of pneumatic muscles. Through computer simulation, the fuzzy sliding mode control shows very good tracking accuracy superior to nonfuzzy sliding mode control. Finally, a two-joint planar arm actuated by four groups of pneumatic muscles operated in an assumed industrial environment is presented. Based on the model, an integral sliding mode control scheme is proposed as an ultimate solution to the control of systems actuated by pneumatic muscles. As the theoretical proof and computer simulations show, the integral sliding mode controller, with strong robustness to model uncertainties and external perturbations, is superior for performing the commanded control assignment. Based on the investigation in this dissertation, integral sliding mode control proposed here is a very promising robust control approach to handle systems actuated by pneumatic muscles

Nonlinear robust controller design for thyristor controlled series compensation

Makalenin ilk sayfası mevcut.The problem of transient stability for a single machine infinite bus system with thyristor controlled series compensation (TCSC) is addressed in this paper. The system does not need to be linearized, and the damping coefficient is measured inaccurately. A nonlinear robust controller and a parameter updating law are obtained simultaneously based on modified adaptive backstepping sliding mode control and Lyapunov methods. The closed-loop error system is guaranteed to be asymptotically stable. The simulation results show that rapid speed response anal strong robustness can be obtained by the proposed method than the conventional adaptive backstepping and adaptive backstepping sliding mode control methods. The proposed method can be also be applied to other nonlinear systems with lower-triangular structure

Robust structural control of an underactuated floating wind turbine

This paper investigates the dynamic modeling and robust control of an underactuated floating wind turbine for vibration suppression. The offshore wind turbine is equipped with a tuned mass damper on the floating platform. The Lagrange's equation is employed to establish the limited degree‐of‐freedom dynamic model. A novel disturbance observer‐based hierarchical sliding mode control system is developed for mitigating loads of the underactuated floating wind turbine. In the proposed control scheme, two prescribed performance nonlinear disturbance observers are developed to estimate and counteract unknown disturbances, where the load induced by wave is considered as a mismatched disturbance while the load caused by wind is treated as a matched disturbance. The hierarchical sliding mode controller regulates the states of such an underactuated nonlinear system. In particular, the first‐order sliding mode differentiator is used to avoid the tedious analytic computation in the sliding mode control design. The stability of the whole closed‐loop system is rigorously analyzed, and some sufficient conditions are derived to guarantee the convergence of the states for the considered system. Numerical simulations deployed on both the design model and the National Renewable Energy Laboratory 5‐MW wind turbine model are provided, which demonstrate great effectiveness and strong robustness of the proposed control scheme

On data-driven modelling and terminal sliding mode control of dynamic systems with applications

University of Technology Sydney. Faculty of Engineering and Information Technology.This thesis addresses critical issues in system modelling and control with some applications to robotics and automation. The main content is divided into three parts, namely data-driven identification, fast terminal sliding mode control alongside underactuated crane control, and robotic pointing system for thermoelastic stress analysis (TSA). The first part is devoted to system modelling. A dynamic model can be identified from data collected (input and output data from the plant). However, the data obtained is often affected by noise. Hence, such algorithms for modelling the plant should be robust enough to accurately predict the dynamic behaviour of the system in the presence of noisy data. Taking this into account, this thesis focuses on subspace-based identification methods, and proposes an effective algorithm based on the Least-Square Support Vector Regression (LS-SVR). In the proposed algorithm, the system identification is formulated as a regression problem to be solved by applying multi-output LS-SVR. The second part of the thesis deals with the control of underactuated systems which are subjected to uncertainties including nonlinearities, parameter variations, and external disturbances. Among many control methodologies, Sliding Mode Control (SMC) is known for its strong robustness. Conventional SMC usually consists of linear sliding surfaces, which can only guarantee the asymptotic stability of the system, and hence, takes infinite time to reach the equilibrium. Requirements of finite-time stability can be fulfilled by adding the sliding function with a fractional nonlinear term to achieve the Terminal Sliding Mode, and using another attractor can lead to a faster response, called the Fast Terminal Sliding Mode (FTSM). FTSM is theoretically promising but it has limited application in real-time systems. This thesis is devoted to bridging this practical gap by developing a FTSM controller for underactuated mechanical systems. The third part of this thesis presents the applications of the proposed LS-SVR based identification algorithm and FTSM control scheme. Here, theoretical developments are implemented on a laboratorial gantry crane and an optical pointing system, respectively. Performance of both LS-SVR identification and FTSM control is verified through extensive simulation and experimental results. Notably, the work for this thesis has been applied to the RobotEye, an industrial pointing system of Ocular Robotics Pty. Ltd., which consists of a mirror integrated with other sensors such as laser sensors and vision cameras for robotic navigation or structural health monitoring with TSA

Adaptive Discrete Second Order Sliding Mode Control with Application to Nonlinear Automotive Systems

Sliding mode control (SMC) is a robust and computationally efficient model-based controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. However, the implementation of the conventional continuous-time SMC on digital computers is limited, due to the imprecisions caused by data sampling and quantization, and the chattering phenomena, which results in high frequency oscillations. One effective solution to minimize the effects of data sampling and quantization imprecisions is the use of higher order sliding modes. To this end, in this paper, a new formulation of an adaptive second order discrete sliding mode control (DSMC) is presented for a general class of multi-input multi-output (MIMO) uncertain nonlinear systems. Based on a Lyapunov stability argument and by invoking the new Invariance Principle, not only the asymptotic stability of the controller is guaranteed, but also the adaptation law is derived to remove the uncertainties within the nonlinear plant dynamics. The proposed adaptive tracking controller is designed and tested in real-time for a highly nonlinear control problem in spark ignition combustion engine during transient operating conditions. The simulation and real-time processor-in-the-loop (PIL) test results show that the second order single-input single-output (SISO) DSMC can improve the tracking performances up to 90%, compared to a first order SISO DSMC under sampling and quantization imprecisions, in the presence of modeling uncertainties. Moreover, it is observed that by converting the engine SISO controllers to a MIMO structure, the overall controller performance can be enhanced by 25%, compared to the SISO second order DSMC, because of the dynamics coupling consideration within the MIMO DSMC formulation.Comment: 12 pages, 7 figures, 1 tabl