Self-balancing Vehicle Research Articles

Research on Stability Control System of Two-Wheel Heavy-Load Self-Balancing Vehicles in Complex Terrain

Research on Stability Control System of Two-Wheel Heavy-Load Self-Balancing Vehicles in Complex Terrain

Product innovation design process combined Kano and TRIZ with AD: Case study.

In the era of rapid product iteration, companies need simple and effective methods to guide the entire process of product innovation design and enhance their product innovation capabilities. Most research focused on improving one or several steps in the product design process. Although some scholars have proposed methods that guided the entire process, they combined more than three different theories, which increased the difficulty of theoretical learning and the complexity of practical implementation. This paper proposed a product innovation design process composed of three theoretical methods: Kano, Axiomatic Design (AD), and Theory of the Solution of Inventive Problems (TRIZ). This new process guided the entire product design process with fewer theoretical methods, reducing the difficulty of learning and implementation. The paper demonstrated the effectiveness of this method through the design practice of a portable two-wheeled self-balancing vehicle. Additionally, the discussion section explored the method's potential from the design management perspective.

Optimization-based adaptive trajectory tracking controller design of self-balanced vehicle with asymptotic prescribed performance

To handle with the nonlinear external disturbances and unmodeled dynamics of self-balanced vehicle (SBV), a novel adaptive trajectory tracking controller based on asymptotic prescribed performance is proposed. First, a velocity planner based on kinematic is constructed to control the velocity signal to improve the motion stability of SBV. Second, the prescribed performance function (PPF) is designed to prescribe transient-state and steady-state performances (TSP). Afterwards, an optimization-based predictive control (OPC) is proposed for accurate trajectory tracking of SBV. Furthermore, a modified radial basis function neural network (RBFNN) approximator is developed to compensate the unmodeled dynamics and the nonlinear external disturbances of the SBV. The overall system stability is proved with the help of Lyapunov theorem. Finally, the tracking performance and anti-interference robustness of the proposed control method are verified by comparative numerical simulations.

Design of a Self-Balancing Vehicle with PID Control Based on Improved Particle Swarm Optimization Algorithm

Design of a Self-Balancing Vehicle with PID Control Based on Improved Particle Swarm Optimization Algorithm

Trajectory Tracking Control Algorithm of Two-wheel Rutting Model

In the development of unmanned two-wheeled self-balancing vehicle, it is very important to consider the trajectory tracking of unmanned two-wheeled self-balancing vehicle. In order to study the track tracking problem of unmanned two-wheeled self-balancing vehicle, the kinematic model of unmanned two-wheeled vehicle and the relation between its roll Angle and the kinematic model were established. The simulation platform is built to realize the unmanned two-wheeled vehicle and realize the circular track tracking at 5m/s, and the tracking performance of the track tracking controller under the circular track is verified.

Simultaneous adjustment of balance maintenance and velocity tracking for a two-wheeled self-balancing vehicle

The purpose of this research is the development a method for simultaneously adjusting the velocity tracking control and the inclination angle stabilization using control techniques for a two-wheeled self-balancing vehicle. The control tasks involve balancing the vehicle around its unstable equilibrium configuration along with steering and velocity tracking. In this study, the mathematical dynamic model of the vehicle is derived using the Lagrange method, under the assumptions of pure rolling and no-slip conditions which are expressed through nonholonomic constraint equations. Along with the mathematical descriptions, a multibody virtual prototype featuring advanced tire-ground interaction modeling has been developed using the MSC Adams software suite. Several classical and modern control strategies are investigated and compared to implement the method. These include Sliding Mode Control (SMC), Proportional Integral Derivative (PID), Feedback Linearization (FL), and Linear Quadratic Regulator Control (LQR) for the under-actuated and unstable subsystem that accounts for the pitch and longitudinal motions. The capabilities of these control strategies are verified and compared not only through Matlab simulation but also using Adams-Matlab co-simulation of the controller and the plant. Although every control technique has its advantages and limitations, the extensive simulation activities conducted for this study suggest that the SMC controller offers superior performances in keeping the system balanced and providing good velocity-tracking responses. Moreover, a Lyapunov-based analysis is used to prove that the sliding mode control achieves finite time convergence to a stable sliding surface. These advantages are counterbalanced by the complexity and the large number of parameters belonging to the designed SMC laws, the scheduling of which can be difficult to implement. For the comparison results another non-linear control strategy, that is, the feedback linearization method, is presented as an alternative. Through the Jacobian linearization approach the mathematical model of the system is linearized, allowing the use of control techniques such as linear quadratic regulation, which are deployed to treat the balancing, steering, and velocity tracking tasks. Finally, the empirical tuning of a PID controller is also demonstrated. The performance and robustness of each controller are evaluated and compared through several driving scenarios both in pure-Matlab and Adams-Matlab co-simulations.

Performance comparison between LQR and PID controllers for two-wheeled self-balancing vehicle

The two-wheeled balancing vehicle is an energy-efficient and eco-friendly transportation tool used for short distances. Both motors of the vehicle are required both motors to be regulated to maintain stability, and modern control methods have been applied to the system. In this paper, the effectiveness of traditional PID control and modern LQR control in the context of a two-wheeled balancing vehicle is evaluated. The motion principle of the vehicle is analyzed, and a corresponding dynamic model is established. PID and LQR control schemes are designed according to the model and the performance of the angle and speed of the vehicle is compared in MATLAB simulation. The two kinds of control performance are evaluated, offering theoretical feasibility verification for the application of two-wheeled balancing vehicles. It has been found that the LQR controller has better performance than the PID controller. LQR has a shorter response time and smaller steady-state error in controlling speed, and there is still room for improvement for both controllers.

WeMo: A Prototype of a Wearable Mobility Device Adapting to User's Natural Posture Changes.

Mobility is fundamental for human beings. In the current society, many personal mobility solutions have been invented to enable more time-efficient mobility, such as self-balancing vehicles, electric unicycles, and electric scooters. Personal mobility devices can provide flexibility to transportation. However, most personal mobility devices need to be carried by their users in the case that they climb stairs and steps. Therefore, many researchers have focused on developing stair-climbing vehicles, but due to the complicated mechanism, these devices are usually huge and heavy. To realize a new type of personal mobility device with more flexibility, we proposed a novel concept of a personal mobility device design that combines the agile mobility of a wheel type mechanism but does not limit a human's natural stair climbing ability. In this study, we introduced a compact personal mobility device, namely WeMo, under the concept of "wearing mobility", which extends humans' mobility in daily life. The developed hardware realizes "walking mode" and "driving mode". Users can move with the motorized driven wheels of the device during driving mode, and users can walk on their feet without any interference from the device during walking mode. In this manuscript, the detailed design of the hardware and control strategy were explained first.Then, we conducted fundamental user tests and discussed the ability of the developed device from test results. Finally, the conclusions and future work were provided.

Design and Development of Self Balancing Vehicle

Abstract: The project work outlines the design and development of a self-balancing vehicle. The project aimed to create a compact, lightweight, and energy-efficient vehicle that could maintain balance without the need for a rider's input. The prototype was built using a combination of electronic components, sensors, and programming algorithms that allowed the vehicle to maintain balance. The design process involved extensive research on similar vehicles and their components, as well as experimentation with various designs to determine the most efficient and effective approach. Once the design was finalized, the development phase began, with the construction of the vehicle's frame and the integration of the various electronic components. The vehicle's balancing system was developed using a combination of gyroscopes, and a microcontroller that processed data from these sensors to adjust the vehicle's balance. Additionally, the vehicle was equipped with a motor and a battery to allow for movement. During testing, the model was able to maintain balance. The vehicle's energy consumption was also significantly lower than that of similar vehicles, making it an efficient and environmentally-friendly mode of transportation. Overall, the project was successful in achieving its goals of designing and developing a self-balancing vehicle. With further refinement and testing, the vehicle has the potential to become a viable mode of transportation for short distances in urban environments.

Method of TFN-TOPSIS Based on Possibility Degree Relation Model and Its Application in the Patent Value Estimation of Self-Balancing Vehicles

With the advancement of technology and the development of intelligence, self-balancing scooters have gradually developed in the field of short-distance travel and have become a fashionable, popular and leisure means of transportation. In addition, both the export of self-balancing scooters and their safety are closely related to their core patents. Therefore, in order to promote the healthy development of the self-balancing automobile industry, this paper carries out the following research: First, we introduce the background of the self-balancing automobile patent and the research status of patent value evaluation at home and abroad. Then, considering the fuzziness of decision makers’ thinking and the uncertainty of patent indicators, this paper extends the traditional TOPSIS method to the field of triangular fuzzy numbers (TFNs), and proposes a TFN-TOPSIS multi-criteria decision model based on the possibility degree relationship model. In addition, this study establishes a core patent value system using three aspects of technology, law and economy, and applies the TFN-TOPSIS model to the top 20 balanced car patents with the highest comprehensive evaluation to rank and analyze the measurement results. On this basis, this paper provides reference opinions for relevant industry personnel from the aspects of future product technology updates and patent layouts.

Comparison of Data-Driven Modeling and Identification Approaches for a Self-Balancing Vehicle

This paper gives a systematic comparison of different state–of–the–art modeling approaches and the corresponding parameter identification processes for a self–balancing vehicle. In detail, a nonlinear grey box model, its extension to consider friction effects, a parametric black box model based on regression neural networks, and a hybrid approach are presented. The parameters of the models are identified by solving a nonlinear least squares problem. The training, validation, and test datasets are collected in full–scale experiments using a self–balancing vehicle. The performance of the different models used for ego–motion prediction are compared in full–scale scenarios, as well. The investigated model architectures can be used to improve both, simulation environments and model–based controller design. This paper shows the upsides and downsides arising from using the different modeling approaches. Videos showing the self–balancing vehicle in action are available at: https://tinyurl.com/mvn8j7vf

Robust Optimal Controller for Two-wheel Self-Balancing Vehicles Using Particle Swarm Optimization

Robust Optimal Controller for Two-wheel Self-Balancing Vehicles Using Particle Swarm Optimization

Study on Comprehensive Evaluation Based on AHP-MADM Model for Patent Value of Balanced Vehicle

With the development of science and technology, people’s travel modes have become more diversified, and self-balancing vehicles have become a popular travel tool for young people. However, in recent years, due to its quality instability, many regions have issued relevant bans, affecting the development of the balanced vehicle industry. In order to better understand balancing vehicle technology, this paper starts with the balancing vehicle patent, and carries out the following research: This paper first introduces the background and current situation of balanced vehicles and the patent. Then, the principle and model of multi-attribute decision-making based on the analytic hierarchy process (AHP-MADM) are described. According to the three-dimensional patent valuation system issued by the State Intellectual Property Office, a core patent valuation system is established. Then, the weights of patent evaluation attributes are calculated by the improved AHP. After, the patent value of the self-balancing vehicle is evaluated using the established AHP-MADM model. On this basis, the status of patent research and the development of self-balancing vehicles is studied to provide a reference for relevant industry personnel, especially R & D personnel, in future product technology updates and patent layout.

Clonal selection algorithm based control for two-wheeled self-balancing mobile robot

In this study, we propose adaptive control approaches based on optimization for a two-wheeled mobile robot. The mathematical model of the self-balancing vehicle based on the inverted pendulum principle, is derived from the Lagrange method and the vehicle model is simulated within MATLAB/Simulink environment. Five different optimization-based controller models (Model 1–5) are developed based on common control methods, which are the Pole Placement Design Method, PID, and LQR. Besides the stability control of the robot, speed control and trajectory tracking control are also provided with these models. While improving the optimization-based controller models, the CSA, an artificial immune optimization methods, is adapted for use with the system. The benefits of the study are given as follows: 1) The balancing control and trajectory tracking control performance of the two-wheeled vehicle is improved using the proposed optimization-based control approach; 2) A comprehensive study is introduced with different control models based on Pole Placement Design Method, PID, and LQR Method; 3) Since parameter tuning of multiple PID controllers is challenging for a two-wheeled vehicle constructed by using the inverted pendulum principle, one of the proposed control models is developed using multiple PIDs. To alleviate the difficulties of parameter tuning for multiple PIDs, the CSA method is adapted for a self-balancing two-wheeled vehicle for the first time; 4) To exhibit the optimization effect of the CSA, the simulation results are compared with five other optimization methods, namely PSO, ABC, DE, GoldSa-II, and CS; 5) The effectiveness of the proposed optimization-based control approaches and optimization method are demonstrated through analyses and simulation studies. In this concept, it has been also analyzed effects of the external disturbances and uncertain parameters on the proposed control method.

Research on LQR controller of control torque gyro self-balancing vehicle based on QPSO optimization

Abstract The LQR control method based on quantum particle swarm optimization (QPSO) is used to solve the balance problem of the control torque gyro self-balancing vehicle. Firstly, the system dynamics model was established by Lagrange method, and the fitness function of mixed indexes was designed, and the system was simulated numerically. Finally, compared with the results of double closed-loop PID control and LQR control using empirical method, the performance of each algorithm in vehicle body balance control is analyzed. The results show that the LQR control algorithm based on PSO optimization can quickly adjust the roll Angle from the initial Angle to the balance position, and has a faster response time, smaller overshoot, and a stronger ability to resist external interference for the balance control of the torque gyro balancing vehicle.

A Novel Polarization and DOA Reliable Auto-Tracking Antenna System

In this study, a novel compact auto-tracking antenna system was developed to ensure the high reliability of practical applications. The system consisted of three antennas (two anchor antennas and a tag antenna), three ultra-wide band (UWB) chips, and one compact turntable. On the anchor side, two highly linearly polarized antennas with a short baseline were installed on an auto-control turntable. Two UWB chips were integrated with the two anchor antennas to extract the phase and amplitude information of the receiving signal. On the tag side, a wide-beam circularly polarized antenna with a UWB chip was installed to mitigate the effect of the tag’s pose on the phase measurement. Using a correlation matching algorithm based on the phase difference of arrival, high tracking reliability with a 90% success rate was achieved for all practical direction-of-arrivals and various axial rotating angles of the tag over the maximum radiation deflection angles between –90° and 135°. The operation distance of a demo self-balanced two-wheeled vehicle was 20 m.

Self-Balancing Vehicle Based on Adaptive Neuro-Fuzzy Inference System

The scope of this research is to design and fuse the sensors used in the self-balancing vehicle through Adaptive Neuro-Fuzzy Inference systems (ANFIS) algorithm to optimize the output. The self-balancing vehicle is a wheeled inverted pendulum, which is extremely complex, nonlinear and unstable. Homogeneous and Heterogeneous sensors are involved in this sensor fusion research to identify the best feasible value among them. The data fusion algorithm present inside the controller of the self-balancing vehicle makes the inputs of the homogeneous sensors and heterogeneous sensors separately for ameliorate surrounding perception. Simulation is performed by modeling the sensors in Simulink. The outcomes specifies that the data fusion algorithm allocates minimal root mean square error (RMSE) and mean absolute percentage error (MAPE) when analyzed and compared with that of every sensor in the system. Finally, the output signals of these sensors are examined and viewed along with noise signal and the actual signal is isolated from the noise signal by applying extended Kalman filter. This propounded technique of ANFIS based fusion algorithm has improved RMSE for both homogeneous sensors and heterogeneous type sensors. Robotic systems may execute several control strategies in various proximity levels based on the performance of the data fusing algorithm.

Control System of Two-Wheel Self-Balancing Vehicle

Control System of Two-Wheel Self-Balancing Vehicle

A Two Wheel Self-Balancing Vehicle

Abstract This paper deals with the development of the equation of motion and practical implementation of low cost two-wheel self-balancing model of a Segway transporter. The experimental model of cart was designed and made under this study. Nonlinear equations of motion of real model and linearized model were derived. To develop the mathematical model, Matlab/Simulink was applied. The mechanical part was implemented into Simulink, and a DC motor was considered as a linear system. The real model was tested for its balance by implementation of a control algorithm consisting of a complementary filter and PID algorithm on an Arduino development board with peripheral devices. The fully functional self-balancing model was used as a demonstration in the teaching process of the Mechatronics courses.

Design of a Self-Balancing Vehicle As a Test Rig for Safety Control Strategies Investigations

Abstract The present paper is related to a research activity concerning self-balancing vehicles, with particular reference to the interaction between driver and vehicle’s dynamics, at the aim to investigate safety management and strategies. In particular, the design process of a self-balancing vehicle with the target to be used as a test rig for safety investigations is presented. Besides the definition of the mechanical configuration, the design process includes also the choice of the motor/transmission unit, the design of the control system and the design of sensors related to vehicle/driver interaction. For design purposes, a simplified two degrees-of-freedom (DOF) planar model has been considered with the driver fixed to the vehicle chassis. In order to test the suitability of the designed vehicle for safety investigations, a multibody model of the vehicle designed and of a driver with three driven joints is also presented. Such model allows to simulate the interaction between human (driver) and machine (vehicle), taking into consideration also the coupling between longitudinal motion and turn, and the presence of tires between vehicle and ground. By means of co-simulations between the multibody model (developed with adams) and the controller (modeled with matlab/simulink), tests have been performed showing the possibility to detect influence of the driver’s behavior on the vehicle’s dynamics.