1,301 research outputs found
Study of Motion Control of A Flexible Link
20th century has witnessed massive upsurge in the use of manipulators in several industries especially in space, defense, and medical industries. Among the types of manipulators used, single link manipulators are the most widely used. A single link robotic manipulator is nothing but a link controlled by an actuator to carry out a particular function such as placing a payload from point A to point B. For low power requirements single link manipulators are made up of light weight materials which require flexibility considerations.Flexibility makes the dynamics of the link heavily non-linear which induces vibrations and overshoot. In this project initially the dynamic model of rigid flexible manipulator is explained, then the state space model of the manipulator system is incorporated into MATLAB. The link flexibility is studied by a single beam FEmodel, where expressions for kinetic and potential energyare employed to derive the torqueequation.The 3 flexible link equations are coupled in terms of 3 variables, θ, Ø and v. The tip angle is finally given aslvfor flexible case whereas for the rigid manipulator the tip angle is same as the hub angle θ. Thereforeaccurate computation of v is very important. The joint flexibility is excluded from analysis.Several comparisons were made between the rigid and flexible link for torque requirement. The relation between the trajectory and hub angle is also plotted in a graph.Finally a PD controller taking the errors and its derivative is designed based on the rigid link dynamics
Simple Pole Placement Controller for Elastic Joint Manipulator
This paper presents investigations into the development of simple pole placement controller for tip angular position tracking and deflection reduction of an elastic joint manipulator system. A Quanser elastic joint manipulator is considered and the dynamic model of the system is derived using the Euler-Lagrange formulation. The pole placement controller is designed based on integral state feedback structure and the feedback gain is computed based on the desired time response specifications of tip angular position. The proposed control scheme is also compared with a hybrid Linear Quadratic Regulator (LQR) with input shaper control scheme. The performances of the control schemes are assessed in terms of tip angular tracking capability, level of deflection angle reduction and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed
Genetic algorithm optimization and control system design of flexible structures
This paper presents an investigation into the deployment of genetic algorithm (GA)-based controller design and optimization for vibration suppression in flexible structures. The potential of GA is explored in three case studies. In the first case study, the potential of GA is demonstrated in the development and optimization of a hybrid learning control scheme for vibration control of flexible manipulators. In the second case study, an active control mechanism for vibration suppression of flexible beam structures using GA optimization technique is proposed. The third case study presents the development of an effective adaptive command shaping control scheme for vibration control of a twin rotor system, where GA is employed to optimize the amplitudes and time locations of the impulses in the proposed control algorithm. The effectiveness of the proposed control schemes is verified in both an experimental and a simulation environment, and their performances are assessed in both the time and frequency domains
Development of New Adaptive Control Strategies for a Two-Link Flexible Manipulator
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
in 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 handle
unknown 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
uncertainties 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
manipulator. 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
for 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
when subjected to unknown payloads, first a direct adaptive control (DAC) is proposed. The
proposed DAC uses a Lyapunov based nonlinear adaptive control scheme ensuring overall
system stability for the control of TLFM. For the developed control laws, the stability proof of
the closed-loop system is also presented. The design of this DAC involves choosing a control
law with tunable TLFM parameters, and then an adaptation law is developed using the closed
loop error dynamics. The performance of the developed controller is then compared with that of
a fuzzy learning based adaptive controller (FLAC). The FLAC consists of three major
components namely a fuzzy logic controller, a reference model and a learning mechanism. It
utilizes a learning mechanism, which automatically adjusts the rule base of the fuzzy controller
so that the closed loop performs according to the user defined reference model containing
information of the desired behavior of the controlled system.
Although the proposed DAC shows better performance compared to FLAC but it suffers from
the complexity of formulating a multivariable regressor vector for the TLFM. Also, the adaptive
mechanism for parameter updates of both the DAC and FLAC depend upon feedback error based
supervised learning. Hence, a reinforcement learning (RL) technique is employed to derive an
adaptive controller for the TLFM. The new reinforcement learning based adaptive control
(RLAC) has an advantage that it attains optimal control adaptively in on-line. Also, the
performance of the RLAC is compared with that of the DAC and FLAC.
In the past, most of the indirect adaptive controls for a FLM are based on linear identified
model. However, the considered TLFM dynamics is highly nonlinear. Hence, a nonlinear
autoregressive moving average with exogenous input (NARMAX) model based new Self-Tuning
Control (NMSTC) is proposed. The proposed adaptive controller uses a multivariable Proportional Integral Derivative (PID) self-tuning control strategy. The parameters of the PID
are adapted online using a nonlinear autoregressive moving average with exogenous-input
(NARMAX) model of the TLFM. Performance of the proposed NMSTC is compared with that
of RLAC.
The proposed NMSTC law suffers from over-parameterization of the controller. To overcome
this a new nonlinear adaptive model predictive control using the NARMAX model of the TLFM
(NMPC) developed next. For the proposed NMPC, the current control action is obtained by
solving a finite horizon open loop optimal control problem on-line, at each sampling instant,
using the future predicted model of the TLFM. NMPC is based on minimization of a set of
predicted system errors based on available input-output data, with some constraints placed on the
projected control signals resulting in an optimal control sequence. The performance of the
proposed NMPC is also compared with that of the NMSTC.
Performances of all the developed algorithms are assessed by numerical simulation in
MATLAB/SIMULINK environment and also validated through experimental studies using a
physical TLFM set-up available in Advanced Control and Robotics Research Laboratory,
National Institute of Technology Rourkela. It is observed from the comparative assessment of the
performances of the developed adaptive controllers that proposed NMPC exhibits superior
7performance in terms of accurate tip position tracking (steady state error ≈ 0.01°) while
suppressing the tip deflections (maximum amplitude of the tip deflection ≈ 0.1 mm) when the
manipulator handles variation in payload (increased payload of 0.3 kg).
The adaptive control strategies proposed in this thesis can be applied to control of complex
flexible space shuttle systems, long reach manipulators for hazardous waste management from
safer distance and for damping of oscillations for similar vibration systems
Controlling the non-parametric modeling of Double Link Flexible Robotic Manipulator using Hybrid PID tuned by P-Type ILA
Utilization of robotic manipulator with multi-link structure encompasses a great influence in most of the present industries. However, controlling the motion of multi-link manipulator has become a troublesome errand particularly once the flexible structure is employed. As of now, the framework utilizes the complicated arithmetic to resolve desired hub angle with the coupling result and vibration within the framework. Hence, this research aims to develop a dynamic system and controller for double-link flexible robotics manipulator (DLFRM) with the enhancement on hub angle position and vibration concealment. The research utilised neural network because the model estimation supported NARX model structure. In the controllers’ development, this research focuses on self-tuning controller. P-Type iterative learning algorithm (ILA) control theme was enforced to adapt the controller parameters to fulfill the required performances once there is changes to the system. The hybrid of proportional-integral-derivate (PID) controller was developed for hub motion and end-point vibration suppression of every link respectively. The controllers were tested in MATLAB/Simulink simulation setting. The performance of the controller was compared with the fixed hybrid PID-PID controller in term of input tracking and vibration concealment. The results indicated that the proposed controller was effective to maneuver the double-link flexible robotic manipulator to the specified position with reduction of the vibration at the tip of the DLFRM structure
Hybrid intelligent machine systems : design, modeling and control
To further improve performances of machine systems, mechatronics offers some opportunities. Traditionally, mechatronics deals with how to integrate mechanics and electronics without a systematic approach. This thesis generalizes the concept of mechatronics into a new concept called hybrid intelligent machine system. A hybrid intelligent machine system is a system where two or more elements combine to play at least one of the roles such as sensor, actuator, or control mechanism, and contribute to the system behaviour. The common feature with the hybrid intelligent machine system is thus the presence of two or more entities responsible for the system behaviour with each having its different strength complementary to the others. The hybrid intelligent machine system is further viewed from the system’s structure, behaviour, function, and principle, which has led to the distinction of (1) the hybrid actuation system, (2) the hybrid motion system (mechanism), and (3) the hybrid control system. This thesis describes a comprehensive study on three hybrid intelligent machine systems. In the case of the hybrid actuation system, the study has developed a control method for the “true” hybrid actuation configuration in which the constant velocity motor is not “mimicked” by the servomotor which is treated in literature. In the case of the hybrid motion system, the study has resulted in a novel mechanism structure based on the compliant mechanism which allows the micro- and macro-motions to be integrated within a common framework. It should be noted that the existing designs in literature all take a serial structure for micro- and macro-motions. In the case of hybrid control system, a novel family of control laws is developed, which is primarily based on the iterative learning of the previous driving torque (as a feedforward part) and various feedback control laws. This new family of control laws is rooted in the computer-torque-control (CTC) law with an off-line learned torque in replacement of an analytically formulated torque in the forward part of the CTC law. This thesis also presents the verification of these novel developments by both simulation and experiments. Simulation studies are presented for the hybrid actuation system and the hybrid motion system while experimental studies are carried out for the hybrid control system
Modelling and intelligent control of double-link flexible robotic manipulator
The use of robotic manipulator with multi-link structure has a great influence in most of the current industries. However, controlling the motion of multi-link manipulator has become a challenging task especially when the flexible structure is used. Currently, the system utilizes the complex mathematics to solve desired hub angle with the coupling effect and vibration in the system. Thus, this research aims to develop a dynamic system and controller for double-link flexible robotics manipulator (DLFRM) with the improvement on hub angle position and vibration suppression. A laboratory sized DLFRM moving in horizontal direction is developed and fabricated to represent the actual dynamics of the system. The research utilized neural network as the model estimation. Results indicated that the identification of the DLFRM system using multi-layer perceptron (MLP) outperformed the Elman neural network (ENN). In the controllers’ development, this research focuses on two main parts namely fixed controller and adaptive controller. In fixed controller, the metaheuristic algorithms known as Particle Swarm Optimization (PSO) and Artificial Bees Colony (ABC) were utilized to find optimum value of PID controller parameter to track the desired hub angle and supress the vibration based on the identified models obtained earlier. For the adaptive controller, self-tuning using iterative learning algorithm (ILA) was implemented to adapt the controller parameters to meet the desired performances when there were changes to the system. It was observed that self-tuning using ILA can track the desired hub angle and supress the vibration even when payload was added to the end effector of the system. In contrast, the fixed controller degraded when added payload exceeds 20 g. The performance of these control schemes was analysed separately via real-time PC-based control. The behaviour of the system response was observed in terms of trajectory tracking and vibration suppression. As a conclusion, it was found that the percentage of improvement achieved experimentally by the self-tuning controller over the fixed controller (PID-PSO) for settling time are 3.3 % and 3.28 % of each link respectively. The steady state errors of links 1 and 2 are improved by 91.9 % and 66.7 % respectively. Meanwhile, the vibration suppression for links 1 and 2 are improved by 76.7 % and 67.8 % respectively
MODELLING AND CONTROL OF A TWO-LINK RIGID-FLEXIBLE MANIPULATOR
The literature lacks data on the reliability of 3D models created by Autodesk Inventor software and imported to MATLAB Simulink software in comparison to mathematically generated models. In this contribution, a two-link rigid-flexible manipulator modelled in two different methods was demonstrated, one of which is using Lagrange equations and Finite Element Method to generate a mathematical model of the manipulator, and the other is creating a 3D model with the aid of Autodesk Inventor then import to MATLAB Simulink, both models were subsequently controlled by three types of controllers, conventional PID controller, LQR controller, and LQG controller. The research demonstrated the performance of the two models with response to the three types of controllers. Achieved results have proven that the Autodesk Inventor is considered a reliable tool for modelling mechanical systems. Results have also confirmed that modern controllers, i.e., LQR and LQG controllers perform much better than conventional PID controllers with regards to the manipulator movement. The implementation of Autodesk Inventor along with MATLAB Simulink indicates that the Autodesk Inventor can be considered as an instrumental tool for designers and engineers. The results enable future developments in the frontier area of robotics and mechanical systems, where sophisticated models could be generated by Autodesk Inventor instead of being modelled mathematically which will benefit engineers and designers by saving time and effort consumed in modelling using mathematical equations, and by reducing the potential errors associated with such modelling technique
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