Browsing by Subject "System Identification"

Now showing 1 - 3 of 3

Open Access
Adaptive control of a one-legged hopping robot through dynamically embedded spring-loaded inverted pendulum template
(2011) Uyanık, İsmail
Practical realization of model-based dynamic legged behaviors is substantially more challenging than statically stable behaviors due to their heavy dependence on second-order system dynamics. This problem is further aggravated by the dif- ficulty of accurately measuring or estimating dynamic parameters such as spring and damping constants for associated models and the fact that such parameters are prone to change in time due to heavy use and associated material fatigue. In the first part of this thesis, we present an on-line, model-based adaptive control method for running with a planar spring-mass hopper based on a once-per-step parameter correction scheme. Our method can be used both as a system identifi- cation tool to determine possibly time-varying spring and damping constants of a miscalibrated system, or as an adaptive controller that can eliminate steady-state tracking errors through appropriate adjustments on dynamic system parameters. We use Spring-Loaded Inverted Pendulum (SLIP) model, which is the mostly used, effective and accurate descriptive tool for running animals of different sizes and morphologies, to evaluate our algorithm. We present systematic simulation studies to show that our method can successfully accomplish both accurate tracking and system identification tasks on this model. Additionally, we extend our simulations to Torque-Actuated Dissipative Spring-Loaded Inverted Pendulum (TD-SLIP) model towards its implementation on an actual robot platform. In the second part of the thesis, we present the design and construction of a onelegged hopping robot we built to test the practical applicability of our adaptive control algorithm. We summarize the mechanical, electronics and software design of our robot as well as the performed system identification studies to calibrate the unknown system parameters. Finally, we investigate the robot’s motion achieved by a simple torque-actuated open loop controller.
Open Access
Identification of some nonlinear systems by using least-squares support vector machines
(2010) Yavuzer, Mahmut
The well-known Wiener and Hammerstein type nonlinear systems and their various combinations are frequently used both in the modeling and the control of various electrical, physical, biological, chemical, etc... systems. In this thesis we will concentrate on the parametric identification and control of these type of systems. In literature, various identification methods are proposed for the identification of Hammerstein and Wiener type of systems. Recently, Least Squares-Support Vector Machines (LS-SVM) are also applied in the identification of Hammerstein type systems. In the majority of these works, the nonlinear part of Hammerstein system is assumed to be algebraic, i.e. memoryless. In this thesis, by using LS-SVM we propose a method to identify Hammerstein systems where the nonlinear part has a finite memory. For the identification of Wiener type systems, although various methods are also available in the literature, one approach which is proposed in some works would be to use a method for the identification of Hammerstein type systems by changing the roles of input and output. Through some simulations it was observed that this approach may yield poor estimation results. Instead, by using LS-SVM we proposed a novel methodology for the identification of Wiener type systems. We also proposed various modifications of this methodology and utilized it for some control problems associated with Wiener type systems. We also proposed a novel methodology for identification of NARX (Nonlinear Auto-Regressive with eXogenous inputs) systems. We utilize LS-SVM in our methodology and we presented some results which indicate that our methodology may yield better results as compared to the Neural Network approximators and the usual Support Vector Regression (SVR) formulations. We also extended our methodology to the identification of Wiener-Hammerstein type systems. In many applications the orders of the filter, which represents the linear part of the Wiener and Hammerstein systems, are assumed to be known. Based on LS-SVR, we proposed a methodology to estimate true orders
Open Access
Tracking and regulation control of a two-degree-of-freedom robot arm
(2012) Güler, Samet
In this thesis, servomechanism synthesis for a two-degree-of-freedom (2-DOF) serial chain revolute-revolute joint robot arm that achieves internal stability, reference signal tracking, and torque disturbance regulation is considered. We first derive the dynamic equations of the robot arm with Euler-Lagrange method by ignoring the effects of friction. Then, using system identification methods, we derive a plant model based on data obtained from a real system through experiments. We then employ PD and PID controllers along with the gravity compensation method to stabilize the system using the passivity properties. Alternatively, we linearize the system model and examine the performance of the same controllers. A linear controller is synthesized by invoking the internal model principle and is directly applied to the nonlinear plant model. The proposed fifth order linear controllers at each channel of the robot arm suffices to achieve not only tracking of step, ramp, and sinusoidal signals at one frequency, but also regulation of step, ramp, and sinusoidal disturbances. It is shown via simulations that, even though the plant model is nonlinear, the synthesized linear controller performs better than the commonly used PID controllers.