Development of New Adaptive Control Strategies for a Two-Link Flexible Manipulator

摘要

Manipulators with thin and light weight arms or links are called as Flexible-Link Manipulators (FLMs). FLMs offer several advantages over rigid-link manipulators such as achieving highspeed operation, lower energy consumption, and increase in payload carrying capacity and find applications where manipulators are to be operated in large workspace like assembly of freeflying space structures, hazardous material management from safer distance, detection of flaws udin large structure like airplane and submarines. However, designing a feedback control system for a flexible-link manipulator is challenging due the system being non-minimum phase, underactuated and non-collocated. Further difficulties are encountered when such manipulators handleudunknown payloads. Overall deflection of the flexible manipulator are governed by the different vibrating modes (excited at different frequencies) present along the length of the link. Due to change in payload, the flexible modes (at higher frequencies) are excited giving rise to uduncertainties in the dynamics of the FLM. To achieve effective tip trajectory tracking whilst quickly suppressing tip deflections when the FLM carries varying payloads adaptive control is necessary instead of fixed gain controller to cope up with the changing dynamics of the udmanipulator. Considerable research has been directed in the past to design adaptive controllers based on either linear identified model of a FLM or error signal driven intelligent supervised learning e.g. neural network, fuzzy logic and hybrid neuro-fuzzy. However, the dynamics of the FLM being nonlinear there is a scope of exploiting nonlinear modeling approach to design adaptive controllers. The objective of the thesis is to design advanced adaptive control strategies udfor a two-link flexible manipulator (TLFM) to control the tip trajectory tracking and its deflections while handling unknown payloads. To achieve tip trajectory control and simultaneously suppressing the tip deflection quickly udwhen subjected to unknown payloads, first a direct adaptive control (DAC) is proposed. The udproposed DAC uses a Lyapunov based nonlinear adaptive control scheme ensuring overall udsystem stability for the control of TLFM. For the developed control laws, the stability proof of udthe closed-loop system is also presented. The design of this DAC involves choosing a control udlaw with tunable TLFM parameters, and then an adaptation law is developed using the closed udloop error dynamics. The performance of the developed controller is then compared with that of uda fuzzy learning based adaptive controller (FLAC). The FLAC consists of three major udcomponents namely a fuzzy logic controller, a reference model and a learning mechanism. It udutilizes a learning mechanism, which automatically adjusts the rule base of the fuzzy controller udso that the closed loop performs according to the user defined reference model containing udinformation of the desired behavior of the controlled system. udAlthough the proposed DAC shows better performance compared to FLAC but it suffers from udthe complexity of formulating a multivariable regressor vector for the TLFM. Also, the adaptive udmechanism for parameter updates of both the DAC and FLAC depend upon feedback error based udsupervised learning. Hence, a reinforcement learning (RL) technique is employed to derive an udadaptive controller for the TLFM. The new reinforcement learning based adaptive control ud(RLAC) has an advantage that it attains optimal control adaptively in on-line. Also, the udperformance of the RLAC is compared with that of the DAC and FLAC.udIn the past, most of the indirect adaptive controls for a FLM are based on linear identified udmodel. However, the considered TLFM dynamics is highly nonlinear. Hence, a nonlinear udautoregressive moving average with exogenous input (NARMAX) model based new Self-Tuning udControl (NMSTC) is proposed. The proposed adaptive controller uses a multivariable Proportional Integral Derivative (PID) self-tuning control strategy. The parameters of the PID udare adapted online using a nonlinear autoregressive moving average with exogenous-input ud(NARMAX) model of the TLFM. Performance of the proposed NMSTC is compared with that udof RLAC.udThe proposed NMSTC law suffers from over-parameterization of the controller. To overcome udthis a new nonlinear adaptive model predictive control using the NARMAX model of the TLFM ud(NMPC) developed next. For the proposed NMPC, the current control action is obtained by udsolving a finite horizon open loop optimal control problem on-line, at each sampling instant,udusing the future predicted model of the TLFM. NMPC is based on minimization of a set of udpredicted system errors based on available input-output data, with some constraints placed on the udprojected control signals resulting in an optimal control sequence. The performance of the udproposed NMPC is also compared with that of the NMSTC. udPerformances of all the developed algorithms are assessed by numerical simulation in udMATLAB/SIMULINK environment and also validated through experimental studies using audphysical TLFM set-up available in Advanced Control and Robotics Research Laboratory, udNational Institute of Technology Rourkela. It is observed from the comparative assessment of the udperformances of the developed adaptive controllers that proposed NMPC exhibits superior ud7performance in terms of accurate tip position tracking (steady state error ≈ 0.01°) while udsuppressing the tip deflections (maximum amplitude of the tip deflection ≈ 0.1 mm) when the udmanipulator handles variation in payload (increased payload of 0.3 kg). udThe adaptive control strategies proposed in this thesis can be applied to control of complex udflexible space shuttle systems, long reach manipulators for hazardous waste management from udsafer distance and for damping of oscillations for similar vibration systems.