Large scale modeling, model reduction and control design for a real-time mechatronic system

Mechatronics is the synergistic integration of the techniques from mechanical engineering, electrical engineering and information technology, which influences each other mutually. As a multidisciplinary domain, mechatronics is more than mechanical or electronics, and the mechatronic systems are always composed of a number of subsystems with various controllers. From this point of view, a lot of such systems can be defined as large scale system. The key element of such systems is integration. Modeling of mechatronic system is a very important step in developing control design of such products, so as to simulate and analyze their dynamic responses for control design, making sure they would meet the desired requirements. The models of large scale systems are always resulted in complex form and high in dimension, making the computation for modeling, simulation and control design become very complicated, or even beyond the solutions provided by conventional engineering methods. Therefore, a simplified model obtained by using model order reduction technique, which can preserve the dominant physical parameters and reveal the performance limiting factor, is preferred. In this dissertation, the research have chosen the two-wheeled self-balancing scooter as the subject of the study in research on large scale mechatronic system, and efforts have been put on developing a completed mathematical modeling method based on a unified framework from varitional method for both mechanical subsystem and electrical subsystem in the scooter. In order to decrease the computation efforts in simulation and control design, Routh model reduction technique was chosen from various model reduction techniques so as to obtain a low dimensional model. Matlab simulation is used to predict the system response based on the simplified model and related control design. Furthermore, the final design parameters were applied in the physical system of two-wheeled self-balancing scooter to test the real performance so as to finish the design evaluation. Conclusion was made based on these results and further research directions can be predicte

Fabrication, Balancing and Analysis of Two Wheeled Robot

Two wheeled self balancing robot is based on the concept of inverted pendulum, which center of mass is above the pivot point. Generally the pendulum is an unstable system on its horizontal plane and must be balanced to remain upright. This can be achieved by applying require amount of torque to the pivot point. Similarly here in two wheeled balancing robot being unstable will deflect from its vertical position and try to fall down. The angle of tilt is calculated by IMU sensor and sends to the microcontroller, which further gives a command to the motor through the motor controller to move in the same direction where the robot has been tilted. When the motor will rotate, it will give an opposite torque to the robot through the pivot point which will counter the angle of deflection and the robot will be stable. This will happen for both the direction of the deflection and hence the robot will move forward and backward and finally it will be balanced. So it require both mechanical and electronics equipment for the robot to achieve the goal

Modelling and robust controller design for an underactuated self-balancing robot with uncertain parameter estimation

A comprehensive literature review of self-balancing robot (SBR) provides an insight to the strengths and limitations of the available control techniques for different applications. Most of the researchers have not included the payload and its variations in their investigations. To address this problem comprehensively, it was realized that a rigorous mathematical model of the SBR will help to design an effective control for the targeted system. A robust control for a two-wheeled SBR with unknown payload parameters is considered in these investigations. Although, its mechanical design has the advantage of additional maneuverability, however, the robot's stability is affected by changes in the rider's mass and height, which affect the robot's center of gravity (COG). Conventionally, variations in these parameters impact the performance of the controller that are designed with the assumption to operate under nominal values of the rider's mass and height. The proposed solution includes an extended Kalman filter (EKF) based sliding mode controller (SMC) with an extensive mathematical model describing the dynamics of the robot itself and the payload. The rider's mass and height are estimated using EKF and this information is used to improve the control of SBR. Significance of the proposed method is demonstrated by comparing simulation results with the conventional SMC under different scenarios as well as with other techniques in literature. The proposed method shows zero steady state error and no overshoot. Performance of the conventional SMC is improved with controller parameter estimation. Moreover, the stability issue in the reaching phase of the controller is also solved with the availability of parameter estimates. The proposed method is suitable for a wide range of indoor applications with no disturbance. This investigation provides a comprehensive comparison of available techniques to contextualize the proposed method within the scope of self-balancing robots for indoor applications

Discrete-time neural network based state observer with neural network based control formulation for a class of systems with unmatched uncertainties

An observer is a dynamic system that estimates the state variables of another system using noisy measurements, either to estimate unmeasurable states, or to improve the accuracy of the state measurements. The Modified State Observer (MSO) is a technique that uses a standard observer structure modified to include a neural network to estimate system states as well as system uncertainty. It has been used in orbit uncertainty estimation and atmospheric reentry uncertainty estimation problems to correctly estimate unmodeled system dynamics. A form of the MSO has been used to control a nonlinear electrohydraulic system with parameter uncertainty using a simplified linear model. In this paper an extension of the MSO into discrete-time is developed using Lyapunov stability theory. Discrete-time systems are found in all digital hardware implementations, such as that found in a Martian rover, a quadcopter UAV, or digital flight control systems, and have the added benefit of reduced computation time compared to continuous systems. The derived adaptive update law guarantees stability of the error dynamics and boundedness of the neural network weights. To prove the validity of the discrete-time MSO (DMSO) simulation studies are performed using a two wheeled inverted pendulum (TWIP) robot, an unstable nonlinear system with unmatched uncertainties. Using a linear model with parameter uncertainties, the DMSO is shown to correctly estimate the state of the system as well as the system uncertainty, providing state estimates orders of magnitude more accurate, and in periods of time up to 10 times faster than the Discrete Kalman Filter. The DMSO is implemented on an actual TWIP robot to further validate the performance and demonstrate the applicability to discrete-time systems found in many aerospace applications. Additionally, a new form of neural network control is developed to compensate for the unmatched uncertainties that exist in the TWIP system using a state variable as a virtual control input. It is shown that in all cases the neural network based control assists with the controller effectiveness, resulting in the most effective controller, performing on average 53.1% better than LQR control alone --Abstract, page iii

U.S. Micromobility Law (Major Road Work Ahead)

Over the past decade electrically powered bicycles, stand-up scooters, skateboards, and more have burst onto the nation’s streets and sidewalks. While some have been owned by their riders, a combination of embedded technology and smartphone apps allowed well-funded start-ups to distribute these novel e-vehicles across urban public spaces, making them available for on-demand, short-term rental. This blossoming of “micromobility” has taken place within physical and legal infrastructures ill-prepared for the change. Indisputably, most of the new types of individual motorized mobility fell outside established vehicle categories. The literal terms of existing law banned their use on all public rights of way, whether road, bicycle lane, or sidewalk. This paper surveys the ad hoc, largely industry-driven, and still-distressingly-incomplete adjustment of U.S. vehicle and traffic laws to accommodate and regulate the rapid spread of electrically-powered personal mobility devices. It also reviews some of the costs of ignoring the phenomenon

How and Where Should I Ride This Thing? “Rules Of The Road” for Personal Transportation Devices

In recent years, “Personal Transportation Devices” (PTDs) have exploded onto streets and sidewalks. These small devices transport individual persons at slow speeds and are either human-powered or motorized. Examples include electric (kick) scooters, skateboards, e-skateboards, roller blades, and Segways. One key to successfully integrating PTDs into community streets will be the implementation of consistent and suitable regulations over user behavior: “rules of the road” for PTD riders. To help local officials identify appropriate rules for rider behavior, this report documents and analyzes existing PTD regulations across 176 jurisdictions and then presents recommendations for a set of state-level “rules of the road” designed to balance safety and freedom of movement for all road users, including PTD riders.To identify the current state of PTD rules of the road, we documented and analyzed the existing regulations at three levels of government: all 50 states and 5 U.S. territories, 101 cities, and 20 college campuses. This review found that PTD users operate in a murky regulatory environment, with rules often poorly defined, contradictory, or altogether absent.Results of this analysis, a literature review, and interviews with 21 stakeholders, were used to craft a model state-level regulatory code that aims to introduce consistent and well-grounded regulation of PTDs. The general philosophy underpinning the model legislation is that PTD rules should protect public safety, permit PTD use as a convenient travel option, be easy to understand and remember, allow for new devices without new regulations, and be based on facts about PTD use and users. Working from these principles, core recommended elements of the recommended PTD regulations are as follows: states should set comprehensive regulations for PTD riders (with local gov-ernments given flexibility to limit certain uses when necessitated by local conditions); PTDs should be regulated as a class, not device-by-device; and PTD users should be permitted to ride on both streets and sidewalks, subject to rules that protect safety and free movement for all travelers