Flexible Robot Manipulators: Modelling, Simulation and Control (2nd Edition)
2: College of Engineering and Engineering Technology, Northern Illinois University, DeKalb, USA
Industrial automation is driving the development of robot manipulators in various applications, with much of the research effort focussed on flexible manipulators and their advantages compared to their rigid counterparts. This book reports recent advances and new developments in the analysis and control of these robot manipulators. After a general overview of flexible manipulators the book introduces a range of modelling and simulation techniques based on the Lagrange equation formulation, parametric approaches based on linear input/output models using system identification techniques, neuro-modelling approaches, and numerical techniques for dynamic characterisation using finite difference and finite element techniques. Control techniques are then discussed, including a range of open-loop and closed-loop control techniques based on classical and modern control methods including neuro and iterative control, and a range of softcomputing control techniques based on fuzzy logic, neural networks, and evolutionary and bio-inspired optimisation paradigms. Finally the book presents SCEFMAS, a software environment for analysis, design, simulation and control of flexible manipulators. Flexible Robot Manipulators is essential reading for advanced students of robotics, mechatronics and control engineering and will serve as a source of reference for research in areas of modelling, simulation and control of dynamic flexible structures in general and, specifically, of flexible robotic manipulators.
Inspec keywords: software engineering; flexible manipulators; optimisation; closed loop systems; neurocontrollers; fuzzy logic; control engineering computing; open loop systems; modelling; simulation
Other keywords: system identification techniques; closed-loop control techniques; finite difference techniques; evolutionary optimisation; open-loop control techniques; finite element techniques; iterative control; fuzzy logic; flexible robot manipulators; Lagrange equation formulation; neuro-modelling approaches; bio-inspired optimisation; neuro control; soft-computing control techniques; simulation techniques; numerical modelling; linear input-output models; interactive software environment
Subjects: Software techniques and systems; Optimisation techniques; Manipulators; Control engineering computing; Simulation, modelling and identification; General and management topics; Control theory
- Book DOI: 10.1049/PBCE086E
- Chapter DOI: 10.1049/PBCE086E
- ISBN: 9781849195836
- e-ISBN: 9781849195843
- Page count: 250
- Format: PDF
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Front Matter
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1 Flexible manipulators - an overview
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This chapter presents a general overview of modelling, simulation and control approaches of flexible manipulators. Sample selection flexible manipulator experimental systems are introduced and their features and design merits are described. A structured overview of common applications and future research prospects and applications of flexible and hybrid manipulators are also provided.
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2 Design of a flexible manipulator experimental system
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This chapter presents the design and development of a lightweight single-link planar constrained flexible manipulator. The design process involves selection of material types in respect of physical properties, selection of sensing and actuation types suitable for the system, interface mechanism, and hardware and software requirements. A number of key practical issues arising in the design process are highlighted and resolved. These include the actuator motor gearing mechanism and interference of power line with sensing data. The final design includes an aluminium type single-link manipulator driven by a printed circuit armature type permanent magnet DC motor. The sensing types include a shaft encoder and tachometer at the hub, an accelerometer at the end-point and a set of strain gauges along the manipulator arm. The manipulator thus designed and developed is featured throughout the book and used in case study exercising for demonstrating and verifying the various modelling and control design approaches presented in various chapters of the book.
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3 Dynamic characterisation of a single-link flexible manipulator
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In this chapter, an analytical model of a single-link flexible manipulator, characterised by a set of infinite number of natural modes, is first developed. This is used to develop state-space and equivalent frequency domain models of the system. These models can further be used for controller design exercises. A case study exercise is presented, where an experimental flexible manipulator system is used for identifying model parameters. The model parameter identification procedure involves spectral analysis of collected input-output data from the experimental system. The identified parameters are then used with the developed model and the model response is verified with the experimental system.
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4 Finite difference modelling
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This chapter presents numerical approaches based on finite difference (FD) techniques for dynamic simulation of single-link flexible manipulator systems. A finite-dimensional simulation of the flexible manipulator system is developed using an FD discretisation of the dynamic equation of motion of the manipulator. Structural damping, hub inertia and payload are incorporated in the dynamic model, which is then represented in a state-space form. Case study simulation exercises and associated results characterising the dynamic behaviour of the manipulator are presented and assessed with experimental results in time and frequency domains.
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5 Finite element modelling
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This chapter describes the development of finite element simulation algorithm of a single-link flexible manipulator system while considering its dynamic behaviours. The algorithm is then utilized to obtain various simulation responses using case studies. The performance of the simulation outcome is also compared with the responses from an experimental system.
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6 Linear parametric modelling
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This chapter presents the development of parametric approaches for dynamic modelling of a single-link flexible manipulator system. The least mean squares, recursive least squares and genetic algorithms are used to obtain linear parametric models of the system. The system is in each case modelled from the input torque to hub-angle, hub-velocity and end-point acceleration outputs. The models are validated using several validation tests. Finally, a comparative assessment of the approaches used is presented and discussed in terms of accuracy, efficiency and estimation of vibration modes of the system.
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7 Neural network modelling
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This chapter presents the neural network modelling of a single link flexible manipulator system. The modelling exercise is presented in two parts. The first part of the chapter uses two popular neural network structure, multilayer perceptron (MLP) and radial basis function (RBF). After appropriate training these are used to identify the dominant vibration modes of a flexible manipulator system. The system identification is realised by minimising the prediction error of the actual plant output and the model output. The second part of the chapter deals with the neural network modelling of dynamic systems. This is to perform parametric identification of a physical system and identify structural features and parameter values including the identification of the model structure. The neural network trained through supervised learning is used for both structure identification and parameter estimation. The technique is then used to model a flexible manipulator system using a composite input torque. The models are developed for hub angle, hub velocity and end-point acceleration.
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8 Open-loop control using command generation techniques
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This chapter presents open-loop command-generation techniques for control of flexible manipulators based on filtered input, Gaussian-shaped input and input shaping. The assumption is that the motion itself is the main source of system vibration. Thus, torque profiles, which do not contain energy at system natural frequencies do not excite structural vibration and hence incur no additional settling time. Accordingly, shaped torque inputs, including Gaussian-shaped, low-pass and bandstop filtered torque input functions and input shaping profiles, are developed on the basis of identified resonance mode frequencies of the system using parametric and non-parametric modelling methods. Case study exercises assessing performances of these control strategies are presented and discussed. Performances of the techniques are assessed in terms of level of vibration reduction at the natural frequencies, time response specifications and robustness to natural frequency variation and effects of various loading conditions.
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9 Collocated and non-collocated control
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This chapter presents closed-loop collocated and non-collocated control approaches for flexible manipulator systems. A closed-loop control strategy using hub angle and hub velocity feedback for rigid-body motion control and end-point acceleration feedback for flexural motion control is considered. This is then extended to an adaptive collocated non-collocated control mechanism using on-line modelling and controller design. PID-type as well as inverse-model control techniques are considered for flexural motion control. The non-minimum phase behaviour of the plant in the latter case is addressed through conventional techniques. This is further addressed through development of an adaptive neuro-inverse model strategy. Case studies demonstrating and assessing the performances of the control approaches are presented through simulated and experimental exercises.
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10 Hybrid iterative learning control
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This chapter presents hybrid iterative learning control schemes with acceleration feedback for control of flexible manipulator systems. The learning schemes considered are PD-type, PI-type and PID-type. A collocated PD controller is considered for rigid-body motion control, and this is extended to incorporate noncollocated and iterative learning control with acceleration feedback. The control scheme is incorporated into a single-link flexible manipulator and a set of case study exercises assessing the performance of the approach is presented and discussed. The tests include time-domain and frequency-domain analyses with and without acceleration feedback in terms of amount of vibration reduction at resonance modes, robustness and input tracking.
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11 Fuzzy logic control
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This chapter presents the concept offuzzy sets andfuzzy logic control and how this may be adopted in the control offlexible manipulator systems. An overview offuzzy logic control comprising the general structure and constituent components is presented. This is followed by a detailed outline of generic process of design offuzzy controllers. A set of case studies demonstrating the design and implementation of fuzzy logic control types for flexible manipulator systems is then presented and discussed.
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12 Multi-objective genetic algorithm control
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This chapter presents the adoption of multi-objective evolutionary algorithms for control of flexible manipulator systems. Fundamentals of multi-objective optimisation and formulation of multi-objective optimisation algorithms are presented. The process of developing a multi-objective genetic algorithm (MOGA) is presented and is used in design of controllers for set-point tracking and end-point vibration control of a single link flexible manipulator. The potential of MOGA is utilised to develop an approach for automatic design of multi-modal command shapers. Two detailed case studies involving design of multi-modal command shapers for an open-loop case and for a closed-loop case in combination with a proportional derivative (PD) control are presented and their performances in set-point tracking and vibration reduction are assessed and discussed.
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13 Multi-objective particle swarm optimisation control
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This chapter provides a brief introduction to the basic particle swarm optimisation (PSO) algorithm and some of its notable variants available in the literature. Then a process of developing multi-objective PSO (MOPSO) algorithms is described, and the approach is utilised to develop two alternative MOPSO algorithms. A case study exercise is presented where the two MOPSO algorithms are used in the design of command shapers for vibration reduction of a single-link flexible manipulator. The implementation results achieved are assessed and discussed.
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14 Evolutionary neuro-fuzzy control
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This chapter focuses on the use of genetic algorithms (GAs) in the design of FLC. An approach of adopting genetic algorithm search is adopted to determine optimal FLC scaling factors. The approach is then extended by adoption of neural network learning of the scaling factors leading to a neuro-fuzzy control method. This is further combined with genetic algorithm for optimisation of shape of activation function of the neural network. Case study experimental investigation exercises are presented demonstrating the performances of the developed paradigms in the control of a single-link flexible manipulator system.
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15 Software environment for modelling and control of flexible manipulators
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This chapter presents the development of SCEFMAS (Simulation and Control Environment for Flexible MAnipulator Systems) software package. This is a userfriendly interactive software environment utilising MATLAB® and associated toolboxes. The main components of this environment are finite difference and finite element simulations, artificial intelligence modelling using neural networks and genetic algorithm. The package also incorporates a range of control techniques, including open-loop control such asfiltered command, Gaussian-shaped and command shaping, collocated and non-collocated closed-loop control methods offixed and adaptive types, and intelligent soft-computing control techniques. The environment allows the user to set-up the system by providing its physical parameters and to select the controller type through an interactive graphical user interface. Data analyses can be performed in timeand frequency-domains on the controller and system input and output signals. The environment is suitable as an education package and as a research facility for investigating and developing various simulation, modelling, and controller designs for flexible manipulator systems.
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Back Matter
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