International Journal of Mechatronics and Automation
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International Journal of Mechatronics and Automation (5 papers in press)
A comparative study for balancing and positioning of an inverted pendulum robot using model-based controllers by Jasem Tamimi Abstract: This paper presents a comparison between four control approaches to solve the
problem of balancing and positioning of the inverted pendulum robot (IPR). These control approaches are proportional-integral-derivative (PID), linear quadratic tracker (LQT), linear model predictive control (LMPC) and nonlinear model predictive control (NMPC). The first three approaches directly depend on the linear control theory and thus on the dynamic linearisation around the stationary point. However, the last control approach depends on the original nonlinear dynamics, therefore, a superior control performance is obtained here. In this study, we test the IPR using these control approaches. In particular, these control approaches are simulated using Simulink/MATLAB dedicated toolboxes like optimal control toolbox and MPC toolbox to test the linear control approach, in addition, a combined multiple shooting with collocation on finite elements method for nonlinear approach. Moreover, some of these scenarios are tested experimentally using a simple lab-made prototype. Keywords: inverted pendulum robot; balancing inverted pendulum; linear/nonlinear model
predictive control. DOI: 10.1504/IJMA.2021.10040909
Automation of single cell surgery in real-time using a vision-based control system
by Armin Eshaghi, James K. Mills Abstract: Micromanipulation of biological cells is a challenging task that requires levels of precision and repeatability which are difficult to achieve by most human operators. Automation of these processes presents an alternative approach which is capable of high precision task execution and much higher throughput, yet with its own limitations. In this paper, we propose automation methods for the image-based visual servo control feedback and tracking of both blastomeres motion and the motion of micromanipulators, in real-time, for blastomere microinjection. An automation procedure is developed for the microinjection or blastomere biopsy of an early stage embryonic cell. These steps involve blastomere z-stack image acquisition, blastomere feature detection (x, y, z) location, and real-time image-based visual servo control of micropipettes to hold and immobilise the embryo while a micropipette injects or
biopsies the blastomere. Experimental results demonstrate acceptable precision evels while performing automation procedure in real-time.
Keywords: automated micromanipulation; single cell surgery; real-time manipulation; image segmentation; blastomere injection; image-based visual servoing. DOI: 10.1504/IJMA.2021.10036409
Model selection for servo control systems by Mathias Tantau, Lars Perner, Mark Wielitzka Abstract: Physically motivated models of electromechanical motion systems are required in
several applications related to control design. However, the effort of modelling is high and automatic modelling would be appealing. The intuitive approach to select the model with the best fit has the shortcoming that the chosen model may be one with high complexity in which some of the parameters are not identiifable or uncertain. Also, ambiguities in selecting the model structure would lead to false conclusions. This paper proposes a strategy for frequency domain model selection ensuring practical identifiability. Also, the paper describes distinguishability analysis of candidate models utilising transfer function coecients and Markov parameters. Model selection and distinguishability analysis are applied to a class of models as they are commonly used to describe servo control systems. It is shown in experiments on an industrial stacker crane that model selection works with little user interaction, except from defining normalised hyperparameters. Keywords: model selection; structure and parameter identification; frequency domain; distinguishability analysis; equivalence of structures; multiple mass resonators; servo control system; electromechanical motion systems; transfer function approach; Markov parameter approach. DOI: 10.1504/IJMA.2021.10038414
Transfer learning-based and originally-designed CNNs for robotic pick and place operation by Fusaomi Nagata, Maki K. Habib, Keigo Watanabe Abstract: The authors have developed a CNN and SVM design and training application for defect detection, and the effectiveness and the usefulness have been proved through several design, training and classification experiments. In this paper, the application further enables to facilitate the design of transfer learning-based CNNs. After introducing the application, a pick and place robot system based on DOBOT is proposed while implementing a visual feedback controller and a transfer learning-based CNN. The visual feedback controller is applied to avoiding the complicated calibration task between image and robot coordinate systems, also the transfer learning-based CNN allows to detect the orientation of target objects for dexterous picking operation. The effectiveness of the proposed system is demonstrated through pick and place tests using gripper type and suction cup type tools. Finally, an originally designed CNN with shallower layers is compared with the AlexNet's transfer learning-based CNN in terms of classification scores. Keywords: convolutional neural network; CNN; transfer learning; pick and place; robot. DOI: 10.1504/IJMA.2021.10041974
An intelligent optimal control approach for motion/force control of constrained non-holonomic mobile manipulators
by Komal Rani, Naveen Kumar Abstract: This paper presents motion/force control problem of constrained non-holonomic mobile manipulators in the presence of uncertainties and external disturbances. The paper proposes an intelligent control scheme utilising the optimal control technique, neural network and adaptive bounds. Firstly, dynamics of mobile manipulator is reduced into state-space form and two sets of variables are created to describe the constrained and unconstrained motion separately. Then the optimal control, which is the explicit solution of Hamilton-Jacobi-Bellman (HJB) equation, is obtained from an algebraic Riccati equation. The nonlinear dynamics of the system are compensated using radial basis function neural network. Bounds on uncertainties of the system and neural network approximation error are estimated with adaptive bound part. The neural networks are trained in online manner using weight update algorithms derived with Lyapunov approach to guarantee the stability of the system. Finally, the proposed approach is verified through numerical simulation studies. Keywords: optimal motion/force control; mobile manipulator; RBFNN; non-holonomic and holonomic constraint; Hamilton-Jacobi-Bellman optimisation; performance index; asymptotic stability. DOI: 10.1504/IJMA.2021.10041982