Two-wheeled Self-balancing Vehicle Research Articles

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.

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.

Synthesis of a nonlinear control law with efficiency energy for the self-balancing two wheeled vehicle

In the paper, we present a method of synthesizing the controller with optimum energy for two-wheeled self-balancing vehicles, based on the multi-purpose consumption function. The authors select the multi-purpose consumption function based on the quasi-optimality and minimum of energy. The synthesis of control laws based on the analytical design of aggregated controllers (ADAR) with manifold of quasi-optimality. The results of the proposed method are simulated and compared to other methods.

DESIGN AND MODELLING OF SELF BALANCING ELECTRIC TWO-WHEELER USING GYROSCOPE

This paper discusses the concept of gyroscopic design and simulation of a constructed two-wheeled self-balancing vehicle system. The electric powered vehicle is used for this project work. The model is designed with the help of Solidworks computer modelling software, once the design calculations are perfect in all aspects. The concept model has become a two-wheel vehicle under which spinning discs are bestowed as a gyroscope to generate counter balance force (gyroscopic effect) once the prototype of the vehicle loses equilibrium on either side. The engine, then, stabilizes itself. This paper also provides a brief description of the model vehicle built with an added function on similar grounds. Whereas when an external force is applied to the device, the force sensors mounted in the vehicle detects the force and produce a force of equal magnitude but in the reverse direction caused by the presence of two gyroscopes used throughout the vehicle, so the vehicle does not lose its balance even when the external force is applied to any of it.

Robust transition control of underactuated two-wheeled self-balancing vehicle with semi-online dynamic trajectory planning

In order to increase the mobility and dynamic agility of a two-wheeled self-balancing vehicle, a dynamic posture control strategy for the initial state transition is proposed in terms of a well-planned nonlinear reference trajectory. The dynamic trajectory planning for the underactuatedmobile inverted pendulum is conducted by solving a two-point boundary value problem with the constraint equation of internal dynamics. The restriction of the offline trajectories for real-time applications is relaxed by establishing a semi-online planning method by assuming velocity-controlled missions. For the transition tasks requiring high maneuver motions including quick start and stop and quick speed change, the proposed control scheme is profitable to stabilize the initial transient motion and get over the performance limit of the conventional feedback-only control with typical step reference inputs. As a result, it enables to achieve consistent state transition to the target velocity and requires just minimal control efforts.

Robust hierarchical sliding mode control of a two-wheeled self-balancing vehicle using perturbation estimation

This paper presents the design and implementation of hierarchical sliding mode control (HSMC) with perturbation estimation (PE) technique on a two-wheeled self-balancing vehicle (TWSBV), to simultaneously realize real-time balancing and velocity tracking control purposes. Considering the fact that the TWSBV system is a typical second-order underactuated system with one controlled actuator and two required control objectives, two sliding surfaces constructed by the velocity and tilt angle information are first designed and an HSMC is proposed to simultaneously achieve both balancing control and velocity control. In order to further enhance the ability of disturbance rejection of the HSMC control, the PE is used to assist with the proposed control for estimating the perturbations online such that the uncertainty bound information is not required in the control design. The excellent balancing and velocity tracking performance can be well achieved even under external disturbances. The effectiveness of the proposed control is verified by a group of comparative experimental investigations on a real TWSBV.

Control of Different-Axis Two-Wheeled Self-Balancing Vehicles

This paper investigates the controls of straight running and turning of a different-axis two-wheeled self-balancing (DATWSB) vehicle. The inverted pendulum system (IPS) with gyroscopic effect is used to describe the uncertainty caused by the working conditions. Based on the generalized coordinate systems, the nonlinear mathematical model of the IPS is established according to Lagrange equation. The sliding mode controller (SMC) and adaptive sliding mode controller (ASMC) are respectively designed to control the system, in which the roll angle feedback is used. The simulation results of three models with/without the controllers are presented, which indicate that the ASMC can make the IPS recover upright faster in straight running, and better achieve the desired roll angle in turning compared with the SMC. Bikesim, a commercial software, is used to build a two-wheeled vehicle model with self-balancing function in combination with Matlab/Simulink. The results show that the ASMC can guarantee the anti-interference and turning abilities of the DATWSB vehicle.

Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm

Based on the self-balance of free-floating two-wheel self-balancing pendulum robot system, an optimal path planning of two-wheel self-balancing pendulum robot is proposed. Firstly, the corner trajectory of the two-wheel self-balancing pendulum robot is parameterized by quantum particle swarm optimization (QPSO), and the objective function is designed according to the base attitude and the control accuracy of the robot’s terminal position and attitude. The optimal path planning problem of the two-wheel self-balancing pendulum robot system is transformed into the optimization problem of the non-linear system. Quantum particle swarm optimization (QPSO) algorithm is used to solve the non-linear optimization problem, and the goal of optimal path planning is achieved. The experimental results show that the proposed method can converge to the global optimal value with fast convergence speed and less adjustment parameters. The planned joint path satisfies the range of corner, angular velocity, and angular acceleration. The joint path is smooth and suitable for the control of two-wheel self-balancing pendulum robot.

Attitude Control Equation of the Two-wheeled Self-balancing Electric Vehicle

In order to get control of motion characteristics of the two-wheeled self-balancing vehicle, based on the dynamic modeling and the correction of ADAMS simulation, a mathematical model of body posture of body angle and wheel driving torque which contains a correction factor was build. The simulation results of the model show that the horizontal velocity of the center of gravity of the swing rob and the wheel center were almost coincides entirely with each other both on model calculation and ADAMS simulation, so the body of the vehicle could keep relatively static on horizontal direction under the control of this model. The position changing value of swinging rob was always less than 1mm, Thus it proved that the modified equation was proper and could be used to control the motion of the two-wheeled self-balancing electronic vehicle.

Nonlinear dynamics modeling and simulation of two-wheeled self-balancing vehicle

Two-wheeled self-balancing vehicle system is a kind of naturally unstable underactuated system with high-rank unstable multivariable strongly coupling complicated dynamic nonlinear property. Nonlinear dynamics modeling and simulation, as a basis of two-wheeled self-balancing vehicle dynamics research, has the guiding effect for system design of the project demonstration and design phase. Dynamics model of the two-wheeled self-balancing vehicle is established by importing a TSi ProPac package to the Mathematica software (version 8.0), which analyzes the stability and calculates the Lyapunov exponents of the system. The relationship between external force and stability of the system is analyzed by the phase trajectory. Proportional–integral–derivative control is added to the system in order to improve the stability of the two-wheeled self-balancing vehicle. From the research, Lyapunov exponent can be used to research the stability of hyperchaos system. The stability of the two-wheeled self-balancing vehicle is better by inputting the proportional–integral–derivative control. The Lyapunov exponent and phase trajectory can help us analyze the stability of a system better and lay the foundation for the analysis and control of the two-wheeled self-balancing vehicle system.

Method for determination of interaction between a two-wheeled self-balancing vehicle and its rider

The paper presents a method for determination of interaction between a two-wheeled self-balancing vehicle and its rider while driving. The presented approach is a new direction of research, because this kind of interaction is usually overlooked in simulation and analytical studies whose aim is to determine and verify the vehicle control method. The proposed method is based on the comparison of experimental results with the computer simulation results. The mathematical model of the vehicle–rider system used in the simulation includes a postulated qualitative mathematical form of interaction with unknown coefficients (in practice, they estimate parameters of the rider). These unknown coefficients have been obtained by designation of the matching function between the experimental data and simulation data. Based on these coefficients, the generalized form of interaction between a two-wheeled self-balancing vehicle and its rider has been found. DOI: http://dx.doi.org/10.5755/j01.mech.22.5.13315

A Two-Wheeled, Self-Balancing Electric Vehicle Used As an Environmentally Friendly Individual Means of Transport

This paper shows a concept of a model of a two-wheeled self-balancing vehicle with an electric motor drive as an environmentally-friendly personal transporter. The principle of work, modelling of construction and performing a simulation are presented and discussed. The visualization of the designed vehicle was made thanks to using Solid Works a computer-aided design program. The vehicle was modelled as an inverted pendulum. The stability of the mechanism in the equilibrium position was studied. An exemplary steering system was also subjected to the analysis that compared two controllers: PID and LQR which enabled to monitor the balance of the vehicle when the required conditions were fulfilled. Modelling of work of the controllers and the evaluation of the obtained results in required conditions were performed in the MATLAB environment.

Trajectory tracking for uncertainty time delayed-state self-balancing train vehicles using observer-based adaptive fuzzy control

Complex systems, such as the interconnected self-balancing vehicles system, are known to be highly nonlinear, under-actuated, and challenging to control. Their complexity is further increased by the presence of time delays and external disturbances. Therefore, all attempts to control the interconnected self-balancing vehicles system were based on accurate determination of its states. However, it is well known that modelling of interconnected self-balancing vehicles systems is not an easy task. In this paper, trajectory tracking of a series of two-wheeled self-balancing vehicles, named B2-train system, is addressed. The highly nonlinear under-actuated system is analyzed and a nonlinear dynamic model of the B2-train system is derived using the Lagrangian method. The adaptive fuzzy controller is designed to approximate the unknown system parameters under the constraint that only the system output is available for measurement. To address this the constraint, a nonlinear state observer is employed to estimate the states including time delays. The aim is to design a state observer-based adaptive fuzzy controller using variable structure (VS) technique and a time delayed compensator which ensures the robust asymptotic stability of the closed-loop system and guarantees an H∞ norm bound constraint on disturbance attenuation for all admissible uncertainties based on Lyapunov criterion. Finally, a time-delayed B2-train system with 2 vehicles is used to demonstrate the performance and robustness of the proposed control scheme.

Study on Two-Wheeled Self-Balancing Electric Vehicle Based on Fuzzy PD Control Algorithm

A Two-Wheeled Self-Balancing Electric vehicle control system was developed base on the system design, hardware development and software strategies.The paper designed the posture acquisition module by gyroscopes and accelerometer sensors, and improved the control ccuracy using Calman filtering algorithm. Based on the system structure model, construction of Two-Wheeled Self-Balancing Electric vehicle dynamic equations by using the analysis method of Newtonian echanics was developed.A fuzzy PD controller was designed,the displacement and velocity as the input variable is controlled by fuzzy controller, and the tilt angle and angular velocity is controlled by PD controller. Finally, the platform of real-time control system was established for parameters adjustment and real-time control.Experiments show that the system has good robustness,high real-time response and has a higher market value.

The Research of Self-Balancing Vehicle Based on Posture Sensor System

The main subject of this thesis is to study a uneasily two-wheeled self-balancing vehicle system. Two tires are placed on two sides of the body parallel in this system . Controlling the rotation of two DC motors can achieve the goal of walking upright. The circuit part is mainly made up by attitude sensors parts (including Gyroscope and Accelerometer), control circuit and the driver board. Attitude sensors measure the tilt angle and the rate of change of inclination of vehicle, and then the controller calculate the responding data and finally drive two DC motors forward or backward to produce forward or backward acceleration to make the car balancing.

Modeling the Interaction Between two-Wheeled Self-Balancing Vehicle and its Rider

The paper presents a modeling method and mathematical description of a two-wheeled self-balancing vehicle and its rider. A model of the rider that was used contains a model of the ankle joint, so we could determine the interaction between the rider and the vehicle. The paper presents results of computer simulations , which show the fundamental processes during riding, such as acceleration and braking.

Controller design for two-wheeled self-balancing vehicles using feedback linearisation technique

Two-wheeled self-balancing vehicles have been extensively used because of their unique capabilities such as reducing the traffic problems and being environment friendly. The main challenge in these vehicles is to design robust controllers capable of creating smooth and safe movement. This paper focuses on design of such a controller using the feedback linearisation technique. The modelled system comprises of a single-link inverted pendulum, which simulates the rider’s body, mounted atop a two-wheeled platform. While the base is being externally disturbed, the designed controllers guarantee the stability of the system and keep the rider along its equilibrium situation. In order to validate the effectiveness of proposed control schemes, some sets of simulation studies are carried out on different smooth and non-smooth surfaces. It is finally shown that besides having stability on smooth surfaces, the system behaves stably on non-smooth trajectories.

Design and Implementation of the Control System for Two-Wheeled Self-Balancing Vehicles

A control-system design for a two-wheeled self-balancing vehicle is discussed in this paper. We have developed a low-cost hardware platform based on AVR MCU, accelerometer sensor and gyroscope sensor, for which the critical circuits, such as sensors and motor driver, are introduced. The control strategy operates by two steps: a) securing the real-time vehicle posture by integrating the data from accelerometer and gyroscope sensors; b) using a closed-loop PID controller to keep the vehicle balanced. This control system is applied to a prototype two-wheeled self-balancing vehicle, whose performance has turned out to be a satisfaction.