In recent years, wireless microrobots have gotten more attention due to their huge potential in the biomedical field, especially drug delivery. Microrobots have several benefits, including small size, low weight, sensitivity, and flexibility. These characteristics have led to microscale improvements in control systems and power delivery with the development of submillimeter-sized robots. Wireless control of individual mobile microrobots has been achieved using a variety of propulsion systems, and improving the actuation and navigation of microrobots will have a significant impact. On the other hand, actuation tools must be integrated and compatible with the human body to drive these untethered microrobots along predefined paths inside biological environments. This study investigated key microrobot components, including medical applications, actuation systems, control systems, and design schemes. The efficiency of a microrobot is impacted by many factors, including the material, structure, and environment of the microrobot. Furthermore, integrating a hybrid actuation system and multimodal imaging can increase the microrobot’s navigation effect, imaging algorithms, and working environment. In addition, taking into account the human body’s moving distance, autonomous actuating technology could be used to deliver microrobots precisely and quickly to a specific position using a combination of quick approaches.
As a key type of mobile robot, the two-wheel mobile robot has been developed rapidly for varied domestic, health, and industrial applications due to human-like movement and balancing characteristics based on the inverted pendulum theory. This paper presents a developed Two-Wheel Self-Balanced Robot (TWSBR) model under road disturbance effects and simulated using MATLAB Simscape Multibody. The considered physical-mechanical structure of the proposed TWSBS is connected with a Simulink controller scheme by employing physical signal converters to describe the system dynamics efficiently. Through the Simscape environment, the TWSBR motion is visualized and effectively analyzed without the need for complicated analysis of the associated mathematical model. Besides, 3D visualization of real-time behavior for the implemented TWSBR plant model is displayed by Simulink Mechanics Explorer. Robot balancing and stability are achieved by utilizing Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) controllers' approaches considering specific control targets. A comparative study and evaluation of both controllers are conducted to verify the robustness and road disturbance rejection. The realized performance and robustness of developed controllers are observed by varying object-carrying loaded up on mechanical structure layers during robot motion. In particular, the objective weight is loaded on the robot layers (top, middle, and bottom) during disturbance situations. The achieved findings may have the potential to extend the deployment of using TWSBRs in the varied important application.
This paper presents a new strategy for controlling induction motors with unknown parameters. Using a simple linearized model of induction motors, we design robust adaptive controllers and unknown parameters update laws. The control design and parameters estimators are proved to have global stable performance against sudden load variations. All closed loop signals are guaranteed to be bounded. Simulations are performed to show the efficacy of the suggested scheme.
The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PI λ D μ (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.
The control problem for a class of a nonlinear systems that contain the coupling of unmeasured states and unknown parameters is addressed. The system actuation is assumed to suffer from unknown dead zone nonlinearity. The parameters bounds of the unknown dead zone to be considered are unknown. Adaptive sliding mode controller, unmeasured states observer, and unknown parameters estimators are suggested such that global stability is achieved. Simulation for a single link mechanical system with unknown dead zone and friction torque is implemented for proving the efficacy of the suggested scheme.
This paper presents a proposed configuration of paralleling scheme PWM DC/DC buck converter. The topological structure and operation principles are presented. A Bode plot diagram technique is used to study the stability of the scheme for different values of controller parameters and with a number of parallel modules. It is found that the results are confidence, and the proposed scheme can be used in high power applications by increasing the number of parallel modules.