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Title: Neuro feedback linearisation in the control of robotic manipulators
Keywords: Nonlinear Feedback, Neural Networks, Robotic Manipulator, Trajectory-tracking, Control, Feedback Linearization
Issue Date: 26-Oct-2004
Citation: NGOO MAY JIN (2004-10-26). Neuro feedback linearisation in the control of robotic manipulators. ScholarBank@NUS Repository.
Abstract: This thesis investigates the trajectory-tracking performance of a robotic system under different control techniques, in particular, the computed-torque control technique and the state feedback linearization. A neural network control approach based on the state feedback linearization technique is also proposed and studied.A two-link manipulator has highly nonlinear dynamic characteristics which are not easily controlled using conventional control approaches. Several model-based control approaches are available which compensates for these non-linear dynamics. However, the performance of such model-based approaches depends highly upon an accurate apriori knowledge of the robota??s dynamic model which, in most cases, is difficult if not impossible to obtain. Neural networks are used in the control schemes here, and they have been found to be able to model the manipulatora??s nonlinear dynamics. The advantage of using neural networks, when they can be trained using only the measured input-output data from the system-under-control, is the elimination of the need for an accurate dynamic model for good control performance.Performance studies on the computed torque and neuro computed torque control schemes were first carried out. The neuro computed torque control scheme was found to have extremely good performance, almost matching the computed-torquea??s theoretically perfect tracking performance. A nonlinear state feedback control scheme was then investigated. This control approach simplifies the system by compensating for the non-linear dynamics, essentially reducing the robot model to a linear system and thus amenable to control by known linear control schemes. The traditional linear approximation approach is not used here since, using this, reasonable performance is achievable over only a small range of state variables. The nonlinear state feedback linearization approach used here allows for operation over the entire operational range of the state variables. Using simulations, the trajectory-tracking performance of this non-linear state feedback linearization approach was compared with that for the computed torque control approach. The computed torque control method is conventionally used to linearize a certain class of systems. The performance of the designed nonlinear feedback law in the present work was found to be comparable to that of the computed torque method. Based on the non-linear state feedback linearization approach, a neural network control approach was developed. In this approach, the neural network controller was trained using only measured input-output data, thus eliminating the need for an accurate model of the system-under-control for good control performance. The performance of this neural network controller was found, through simulation studies, to be comparable to the non-linear controller designed assuming a perfect knowledge of the robota??s dynamic model.The main contribution of this dissertation is the application of the nonlinear state feedback controller for the control of a two-link robotic manipulator and the development of a neural-network controller based on this model-based approach. In this thesis, a nonlinear state feedback control law has been derived mathematically. This feedback law is applied to a two link robotic manipulator in order that the robota??s closed loop system can be made linear. The current simulation work using the developed feedback law contributes towards the application of linearization techniques on nonlinear multi-link robotic system. Based on mathematical analysis and an experimental study, the proposed controller has been shown to give good tracking performance and stability. Simulation studies compare the trajectory-tracking performance of this approach to the more developed computed-torque control approach and its neural network equivalent.
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