Bijan Shirinzadeh

Enhanced stiffness modeling, identification and characterization for robot manipulators

This paper presents the enhanced stiffness modeling and analysis of robot manipulators, and a methodology for their stiffness identification and characterization. Assuming that the manipulator links are infinitely stiff, the enhanced stiffness model contains: 1) the passive and active stiffness of the joints and 2) the active stiffness created by the change in the manipulator configuration, and by external force vector acting upon the manipulator end point. The stiffness formulation not accounting for the latter is known as conventional stiffness formulation, which is obviously not complete and is valid only when: 1) the manipulator is in an unloaded quasistatic configuration and 2) the manipulator Jacobian matrix is constant throughout the workspace. The experimental system considered in this study is a Motoman SK 120 robot manipulator with a closed-chain mechanism. While the deflection of the manipulator end point under a range of external forces is provided by a high precision laser measurement system, a wrist force/torque sensor measures the external forces. Based on the experimental data and the enhanced stiffness model, the joint stiffness values are first identified. These stiffness values are then used to prove that conventional stiffness modeling is incomplete. Finally, they are employed to characterize stiffness properties of the robot manipulator. It has been found that although the component of the stiffness matrix differentiating the enhanced stiffness model from the conventional one is not always positive definite, the resulting stiffness matrix can still be positive definite. This follows that stability of the stiffness matrix is not influenced by this stiffness component. This study contributes to the previously reported work from the point of view of using the enhanced stiffness model for stiffness identification, verification and characterization, and of new experimental results proving that the conventional stiffness matrix is not complete and is valid under certain assumptions.

A Novel Direct Inverse Modeling Approach for Hysteresis Compensation of Piezoelectric Actuator in Feedforward Applications

The Prandtl-Ishlinskii (PI) model is widely utilized in hysteresis modeling and compensation of piezoelectric actuators. For systems with rate-independent hysteresis, the inverse PI model is analytically feasible and it can be adopted as a feedforward compensator for the hysteretic nonlinearity of piezoelectric actuators. However, for the rate-dependent PI model, the applicable valid inversion methodology is not yet available. Although simply replacing all the rate-independent terms in the conventional inversion law with the rate-dependent terms can achieve acceptable results at very slow trajectories. However, a large theoretical modeling error is inevitable at fast trajectories, which is investigated through simulations. This paper proposes a new direct approach to derive the inverse PI model directly from experimental data. As no inversion calculation is involved, the proposed direct approach is efficient and the theoretical modeling error can be avoided. In order to validate the accuracy of the direct approach, a number of experiments have been implemented on a piezo-driven compliant mechanism by utilizing the inverse PI model as a feedforward controller. The tracking performance of the mechanism is significantly improved by the direct approach.

Nanorobot architecture for medical target identification

This work has an innovative approach for the development of nanorobots with sensors for medicine. The nanorobots operate in a virtual environment comparing random, thermal and chemical control techniques. The nanorobot architecture model has nanobioelectronics as the basis for manufacturing integrated system devices with embedded nanobiosensors and actuators, which facilitates its application for medical target identification and drug delivery. The nanorobot interaction with the described workspace shows how time actuation is improved based on sensor capabilities. Therefore, our work addresses the control and the architecture design for developing practical molecular machines. Advances in nanotechnology are enabling manufacturing nanosensors and actuators through nanobioelectronics and biologically inspired devices. Analysis of integrated system modeling is one important aspect for supporting nanotechnology in the fast development towards one of the most challenging new fields of science: molecular machines. The use of 3D simulation can provide interactive tools for addressing nanorobot choices on sensing, hardware architecture design, manufacturing approaches, and control methodology investigation. (Some figures in this article are in colour only in the electronic version)

Sliding-Mode Enhanced Adaptive Motion Tracking Control of Piezoelectric Actuation Systems for Micro/Nano Manipulation

This paper proposes a sliding-mode enhanced adaptive control methodology for piezoelectric actuation systems to track specified motion trajectories. This control methodology is proposed to overcome the problems of unknown or uncertain system parameters, nonlinearities including the hysteresis effect, and external disturbances in the piezoelectric actuation systems, without any form of feedforward compensation. In this paper, a special class of positive definite functions is employed to formulate the control methodology such that the closed-loop system stability can be guaranteed. The control formulation, stability analysis, and analytical closed-loop solution are presented. Furthermore, a precise tracking ability in following a specified motion trajectory is demonstrated in the experimental study. With the capability of motion tracking under the aforementioned conditions, the sliding-mode enhanced adaptive control methodology is very attractive in realising high-performance control applications in the field of micro/nano manipulation.

Topology optimisation and singularity analysis of a 3-SPS parallel manipulator with a passive constraining spherical joint

This study focuses on topology optimisation and singularity analysis of a 3-SPS parallel manipulator with three identical limbs made of spherical + prismatic + spherical joints, and a passive spherical joint connecting a moving platform to a fixed base. First, the number synthesis for spatial parallel manipulators with three limbs is reviewed and the existence conditions for the parallel manipulator considered in this study are presented. Second, the inverse position formulation is established; a constrained optimisation algorithm based on the condition number of the manipulator Jacobian matrix over the entire workspace of the manipulator is employed to determine design parameters. A set of optimisation trials is accomplished to prove that the algorithm is valid for determining the optimum values of the manipulator design parameters. Third, forward and inverse Jacobian matrices are derived, and then the singularity loci of the manipulator with different values of the design parameters are generated using a methodology not requiring the explicit expressions for the roots of the determinant of the Jacobians. The singularity loci have been presented to show that this methodology is an efficient and versatile technique in the singularity determination of manipulators, and that a well-conditioned workspace has the least number of singular configurations.

The measurement uncertainties in the laser interferometry-based sensing and tracking technique

The laser interferometry-based sensing and tracking (LIST) technique can be used to perform real time position measurements of dynamic systems such as robot manipulators. These measurements are necessary to provide accurate calibration and performance measures of robot manipulators. Therefore, it is important that the LIST technique is highly accurate. However, due to the dependence of LIST technique on different sensors and transducer sub-systems, there exists some degree of uncertainty in the measurements obtained. This paper presents the measurement uncertainties associated with the LIST technique. The uncertainties contributed by different sub-systems within the LIST apparatus are analysed. A general expression for uncertainty estimation is also developed.

Nanorobot Hardware Architecture for Medical Defense

This work presents a new approach with details on the integrated platform and hardware architecture for nanorobots application in epidemic control, which should enable real time in vivo prognosis of biohazard infection. The recent developments in the field of nanoelectronics, with transducers progressively shrinking down to smaller sizes through nanotechnology and carbon nanotubes, are expected to result in innovative biomedical instrumentation possibilities, with new therapies and efficient diagnosis methodologies. The use of integrated systems, smart biosensors, and programmable nanodevices are advancing nanoelectronics, enabling the progressive research and development of molecular machines. It should provide high precision pervasive biomedical monitoring with real time data transmission. The use of nanobioelectronics as embedded systems is the natural pathway towards manufacturing methodology to achieve nanorobot applications out of laboratories sooner as possible. To demonstrate the practical application of medical nanorobotics, a 3D simulation based on clinical data addresses how to integrate communication with nanorobots using RFID, mobile phones, and satellites, applied to long distance ubiquitous surveillance and health monitoring for troops in conflict zones. Therefore, the current model can also be used to prevent and save a population against the case of some targeted epidemic disease.

Neural Network Motion Tracking Control of Piezo-Actuated Flexure-Based Mechanisms for Micro-/Nanomanipulation

This paper presents a neural network motion tracking control methodology for piezo-actuated flexure-based micro-/nanomanipulation mechanisms. In particular, the radial basis function neural networks are adopted for function approximations. The control objective is to track desired motion trajectories in the presence of unknown system parameters, nonlinearities including the hysteresis effect, and external disturbances. In this study, a lumped-parameter dynamic model that combines the piezoelectric actuator and the micro-/nanomechanism is established for the formulation of the proposed approach. The stability of the control methodology is analyzed, and the convergence of the position-and velocity-tracking errors to zero is proven theoretically. A precise tracking performance in following a desired motion trajectory is demonstrated in the experimental study. An important advantage of this control approach is that no prior knowledge is required for not only the system parameters, but also for the thresholds and weights of the neural networks in the physical realization of the control system. This control methodology is very suitable for the implementation of high-performance flexure-based micro-/nanomanipulation control applications.

Optimum synthesis of planar parallel manipulators based on kinematic isotropy and force balancing

This paper deals with an optimum synthesis of planar parallel manipulators using two constrained optimisation procedures based on the minimization of: (i) the overall deviation of the condition number of manipulator Jacobian matrix from the ideal/isotropic condition number, and (ii) bearing forces throughout the manipulator workspace for force balancing. A revolute jointed planar parallel manipulator is used as an example to demonstrate the methodology. The parameters describing the manipulator geometry are obtained from the first optimisation procedure, and subsequently, the mass distribution parameters of the manipulator are determined from the second optimisation procedure based on force balancing. Optimisation results indicate that the proposed optimisation approach is systematic, versatile and easy to implement for the optimum synthesis of the parallel manipulator and other kinematic chains. This work contributes to previously published work from the point of view of being a systematic approach to the optimum synthesis of parallel manipulators, which is currently lacking in the literature.

Robust Neural Network Motion Tracking Control of Piezoelectric Actuation Systems for Micro/Nanomanipulation

This paper presents a robust neural network motion tracking control methodology for piezoelectric actuation systems employed in micro/nanomanipulation. This control methodology is proposed for tracking of desired motion trajectories in the presence of unknown system parameters, nonlinearities including the hysteresis effect and external disturbances in the control systems. In this paper, the related control issues are investigated, and a control methodology is established including the neural networks and a sliding control scheme. In particular, the radial basis function (RBF) neural networks are chosen for function approximations. The stability of the closed-loop system, as well as the convergence of the position and velocity tracking errors to zero, is assured by the control methodology in the presence of the aforementioned conditions. An offline learning procedure is also proposed for the improvement of the motion tracking performance. Precise tracking results of the proposed control methodology for a desired motion trajectory are demonstrated in the experimental study. With such a motion tracking capability, the proposed control methodology promises the realization of high-performance piezoelectric actuated micro/nanomanipulation systems.