This paper presents a robust methodology for constrained motion tracking control of piezo-actuated flexure-based four-bar micro/nano manipulation mechanisms. This unique control approach is established for the tracking of desired motion trajectories in a constrained environment exhibiting some degree of uncertain stiffness. The control methodology is also formulated to accommodate not only the parametric uncertainties and unknown force conversion function, but also nonlinearities including the hysteresis effect and external disturbances in the motion systems. In this paper, the equations for the dynamic modelling of a flexure-hinged four-bar micro/nano manipulation mechanism operating in a constrained environment are established. A lumped parameter dynamic model that combines the piezoelectric actuator and the micro/nano manipulation mechanism is developed for the formulation of the control methodology. Stability analysis of the proposed closed-loop system is conducted, and the convergence of the motion tracking errors is proven theoretically. Furthermore, precise motion tracking ability in following a desired motion trajectory is demonstrated in the experimental study. This robust constrained motion tracking control methodology is very useful for the development of high performance flexure-based micro/nano manipulation applications demanding high-precision motion tracking with force sensing and feedback.

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.

This paper investigates the feasibility of a neural network motion control methodology applied in the piezoelectric actuation systems for micro/nano manipulation. This control methodology is proposed for tracking desired motion trajectories in the presence of unknown system parameters, non-linearities including the hysteresis effect, and external disturbances in the micro/nano manipulation systems. The advantage of this control methodology is that no prior knowledge of the system parameters, or of the initial estimates of the neural networks, is required for the physical realisation of the control system. Simulation results of the control methodology for tracking a desired motion trajectory are presented. This control methodology can be utilised to realise high performance micro/nano manipulation systems driven by piezoelectric actuators.

This paper presents a robust adaptive control methodology for piezoelectric actuation systems to track specified motion trajectories. This methodology is proposed to deal with the control problems of unknown or uncertain system parameters, nonlinearities including the hysteresis effect, and external disturbances in the piezoelectric actuation systems, without any form of feed-forward 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 as well as the stability analysis is detailed. Furthermore, an experimental investigation is also conducted. Implementation of the control methodology is practical as only a knowledge of the estimated system parameters is required. In the experimental study, a promising tracking ability in following a specified motion trajectory is demonstrated. With the capability of motion tracking under the aforementioned conditions, the robust adaptive control methodology is very attractive in realising high performance control applications in the field of micro/nano manipulation.

This work presents chemical communication techniques for nanorobots foraging in fluid environments relevant for medical applications. Unlike larger robots, viscous forces and rapid diffusion dominate their behaviors. Examples range from modified microorganisms to nanorobots using ongoing developments in molecular computation, sensors and motors. The nanorobots use an innovative methodology to achieve decentralized control for a distributed collective action in the combat of cancer. A communication approach is described in the context of recognize a single tumor cell in a small venule as a target for medical treatment. Thus, a higher gradient of signal intensity of E-cadherin is used as chemical parameter identification in guiding nanorobots to identify malignant tissues. A nanorobot can effectively use chemical communication to improve intervention time to identify tumor cells

This paper proposes and evaluates an adaptive technique and a variable structure control approach for piezoelectric actuation systems to track specified motion trajectories. The proposed control methodologies are formulated to accommodate unknown or uncertain system parameters, nonlinearities including the hysteresis effect, and external disturbances in the piezoelectric actuation systems without any form of feed-forward compensation. In this study, both control methodologies are demonstrated to possess a promising motion tracking ability experimentally. In comparison, the variable structure control approach is evaluated to be superior to the adaptive technique in the motion tracking control. With the ability to track motion trajectories under parametric uncertainties, nonlinearities, and external disturbances, the proposed control methodologies are very attractive in realising the high-precision piezoelectric actuation systems for micro/nano manipulation

This paper presents the direct kinematic solutions to 3DOF planar parallel mechanisms. Efforts to solve the direct kinematics of planar parallel mechanisms have concentrated on RPR mechanisms due to its inherent simplicity. It is established that the direct kinematic equations of a general 3DOF planar parallel mechanism can be reduced to a univariate polynomial of degree 8. This paper presents the derivation of this univariate polynomials for both 3RRR and 3RPR mechanisms, showing the similarities and differences between the two common configurations of 3DOF planar parallel mechanisms. This paper also presents the on the direct kinematic solution to a simplified case of the 3RRR planar parallel mechanisms, where it is possible to decouple the polynomial further into two quadratic equations, describing the position and orientation of the end-effector, respectively. This result will provide an efficient computation method for a very useful configuration of planar parallel manipulators

This paper proposes a robust control methodology for piezoelectric actuation systems to track specified motion trajectories. This is motivated by the search for an effective control strategy to deal with the problem of nonlinear behaviour in the piezoelectric actuation systems. The basic concept associated with this approach lies in the specification of a target performance and the formulation of a robust control scheme for the system to ensure the convergence of the position tracking error to zero in the presence of parametric uncertainties and hysteresis effect inclusive of other un-modelled disturbances. Stability of the control system is proven theoretically and the robust control methodology is demonstrated to possess a promising tracking ability through the control experiments. Implementation of the control law requires only a knowledge of the estimated parameters and their corresponding bounds as well as the bound of the hysteresis effect including disturbances. Being capable of handling uncertainties and disturbances, the robust control methodology is very attractive in the field of micro/nanomanipulation in which high-precision control applications could be realised

Task-space regulation of robot manipulators can be classified into two fundamental approaches, namely, transpose Jacobian regulation and inverse Jacobian regulation. In this paper, two inverse Jacobian regulators with gravity compensations are presented, and the stability problems are formulated and solved. It is shown that the inverse Jacobian systems can be stabilized, and there exists a region of attraction such that the system remains stable. Our results show that the two fundamental approaches are two dual controllers, in the sense that the transpose Jacobian matrix can be replaced by the inverse Jacobian matrix and vice versa. The theoretical results are verified experimentally by implementing the inverse Jacobian regulators on an industrial robot, PUMA560.

Many controllers have been developed for setpoint control of robotic manipulators. Adaptive PD controller is one of the simplest and most effective setpoint controller in the presence of uncertainty in gravitational force. However, an exact model of gravity regressor is required in the adaptive PD control. In this paper we propose an adaptive setpoint controller with modeling error in the gravity regressor, and show that convergence can be guaranteed even when the gravity regressor is uncertain. A new Lyapunov function is presented for convergence analysis of such problem. As a by-product of the result we also show that existing setpoint controllers, such as the adaptive SP-D and SP-ID in the literature, can be analyzed and designed in an unifying way as special cases of the proposed controller.

Many controllers have been developed for setpoint control of robotic manipulators. An adaptive proportional and derivative (PD) controller is one of the simplest and most effective setpoint controller in the presence of uncertainty in gravitational force. However, an exact model of gravity regressor is required in the adaptive PD control. In this paper, we propose an adaptive setpoint controller with modeling error in the gravity regressor and show that convergence can be guaranteed even when the gravity regressor is uncertain. A new Lyapunov function is presented for the stability analysis of such problem. As a byproduct of the result, we also show that existing setpoint controllers such as an adaptive saturated proportional-derivative (SP-D) and saturated proportional-integral and derivative (SP-ID) in the literature can be analyzed and designed in a unifying way as special cases of the proposed controller.