Top PDF Comparison of Control Methods PD, PI, and PID on Two Wheeled Self Balancing Robot

Comparison of Control Methods PD, PI, and PID on Two Wheeled Self Balancing Robot

Comparison of Control Methods PD, PI, and PID on Two Wheeled Self Balancing Robot

Abstract—A robot must employ a suitable control method to obtain a good stability. The Two-Wheeled Self Balancing Robot in this paper is designed using a MPU-6050 IMU sensor module and ATmega128 microcontroller as its controller board. This IMU sensor module is employed to measure any change in the robot’s tilt angle based on gyroscope and accelerometer readings contained in the module. The tilt angle readings are then utilized as the setpoint on the control methods, namely PD (Proportional Derivative), PI (Proportional Integral), or PID (Proportional Integral Derivative). Based on the conducted testing results, the PID controller is the best control strategy when compared to the PD and PI control. With parameters of Kp = 14, Ki = 0005 and Kd = 0.1, the robot is able to adjust the speed and direction of DC motor rotation to maintain upright positions on flat surfaces.
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Two wheeled balancing robot controller designed using pid

Two wheeled balancing robot controller designed using pid

11 enough for robot’s balance. When the appropriate Kp and Ki gain values are chosen for PI controller, it has been observed that the robot can balance itself for a short time and try to maintain its balance by swinging. In addition, when PID controller is applied, the two-wheel robot can stand in upright position longer compare to the previous two cases. This can be happen if only appropriate value of Kp, Ki, and Kd gain are chosen. Meanwhile, Nasir [13] states that PID controller capable to control the nonlinear inverted pendulum system angular and linear position in Matlab Simulink. However, PID controller should be improved so that the maximum overshoot for the linear and angular positions do not have high range as required by the design. W.An [14] claim that Matlab can be used to compare the performance of PID Controller and Linear- quadratic regulator (LQR) in controlling two-wheeled self-balancing robot. It is concluded that LQR has a better performance than PID in self-balancing control in term of the time to achieve the steady state of robot.
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Self-balancing Robot Control Using Fractional-Order PID Controller

Self-balancing Robot Control Using Fractional-Order PID Controller

Abstract—This paper presents a two-wheeled robot control that can balance upright on its own by controlling the angular speed of the motor to keep the robot upright using measured data from the gyroscope sensor. The aim of this study is to demonstrate the design of fractional-order PID controller (FOPID) that has more controllability and the ability to adjust outperform traditional PID controller. The design of optimal FOPID controller based on integer-order PID parameters are explained and validated its performance compared with the traditional PID controller using Matlab simulation. As well as the real system experiment is implemented on Raspberry Pi using IIR filters cascaded second-order section form II. The study revealed the appropriate concept of implementation of FOPID on the real system.
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A Comparative Study of Controllers for a Two Wheeled Self-balancing Robot

A Comparative Study of Controllers for a Two Wheeled Self-balancing Robot

Two wheeled self-balancing vehicle based on the concept of an inverted pendulum is built by researchers at the industrial electronics laboratory. SEGWAY PT is such a one machine developed by Dean Kamen, now commercially obtainable as a battery-powered electric vehicle in the market. Researchers and engineers are working to develop techniques to make a dynamically stable system and to guarantee desired performance and robust solution. Many methods are applied and tested on this system platform. Dual-PID and LQR control techniques are designed and tested in Simulink and analysed for vertical balance and position control [1]. There are many past studies about the stabilization and optimization of two-wheeled inverted pendulum robots. They are state feedback control with pole placement method [2], Proportional-Integral- Derivative (PID) and Proportional-Derivative(PD) controllers, LQR [1], [3] , Model Predictive Control (MPC) . Kalman filtering and PID algorithm is used for a two wheeled car [4]. PI control is not satisfactory for a two wheeled self-balancing robot to act in a real time application. Different new research works has found on inverted pendulum techniques in the implementation of bipedal locomotion [6], [7]. This paper presents LQGand H-infinity mixed sensitivity design for a two wheeled self-balancing robot. Section two presents system modeling. Section three presents the control techniques. The simulation results are discussed in section four. Conclusions of the work are drawn in section five.
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Robust Navigational Control of a Two-Wheeled Self-Balancing Robot in a Sensed Environment

Robust Navigational Control of a Two-Wheeled Self-Balancing Robot in a Sensed Environment

ABSTRACT This research presents an improved mobile inverted pendulum robot called Two-wheeled Self-balancing robot (TWSBR) using a Proportional-Derivative Proportional-Integral (PD-PI) robust control design based on 32-bit microcontroller in a sensed environment (SE). The robot keeps itself balance with two wheels and a PD-PI controller based on the Kalman filter algorithm during the navigation process and is able to stabilize while avoiding acute and dynamic obstacles in the sensed environment. The Proportional (P) control is used to implement turn control for obstacle avoidance in SE with ultrasonic waves. Finally, in a SE, the robot can communicate with any of the Internet of Things (IoT) devices (mobile phone or Personal Computer) which have a Java-based transmission application installed and through Bluetooth technology connectivity for wireless control. The simulation results prove the efficiency of the proposed PD-PI controller in path planning, and balancing challenges of the TWSBR under several environmental disturbances. This shows an improved control system as compared to the existing improved Adaptive Fuzzy Controller.
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Bacterial foraging-optimized PID control of a two-wheeled machine with a two-directional handling mechanism

Bacterial foraging-optimized PID control of a two-wheeled machine with a two-directional handling mechanism

The interest in two-wheeled machines (TWMs) is incom- parably increasing, and various linear and nonlinear methods of identification are employed for developing an accurate model of the inverted pendulum and estab- lishing a proper control strategy for the system. Lee et al. [1] concentrated on designing a one-wheel inverted pen- dulum system that employs air power for balancing. The pitch angle was controlled by a DC motor, while the roll angle was regulated by air pressure sent out from ducted fans controlled by linear control methods. Chinnadurai and Ranganathan [2] focused on applying the principle of IP by proposing a two-wheel self-supporting robot controlled by an internet-on-a chip (IOC) controller. The main feature associated with this system is the capabil- ity to control the robot worldwide using the IOC, not to mention the IR, attitude, and tilt sensors installed on the robot. A novel configuration of wheeled robotic machines (WRM) which is based on the principle of two-wheeled inverted pendulum (IP) with an extended intermediate
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Self Balancing Robot

Self Balancing Robot

Abstract: This paper reports the design, construction and control of a two-wheel self-balancing robot. The system architecture comprises a pair of DC motor and an Arduino microcontroller board; 3-axis MEMS (Micro Electrical Mechanical Systems) accelerometer and 3-axis MEMS gyroscope are employed for attitude determination. In addition, a complementary filter is implemented to compensate for gyro drifts i.e. PID. Experimental results show that self-balancing can be achieved with PI-PD control in the vicinity of the upright position.
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Controlling of Two Wheeled Self Balancing Robot using PID

Controlling of Two Wheeled Self Balancing Robot using PID

[2]The system architecture comprises a pair of DC motor and an Arduino microcontroller board; a single-axis gyroscope and a 2-axis accelerometer are employed for attitude determination. In addition, a complementary filter is implemented to compensate for gyro drifts. Electrical and kinematic parameters are determined experimentally; PID and LQR-based PI- PD control designs, respectively, are performed on the linearized equations of motion.[6]The types of control is categorized as linear and non-linear control. In some instances, the linear control is sufficient to control a system. One of the most widely used is the Proportional Derivative Integral controller or better known as the PID controller [10].
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DESIGN AND IMPLEMENTATION OF PID BASED TWO WHEELED SELF BALANCING MOBILE ROBOT

DESIGN AND IMPLEMENTATION OF PID BASED TWO WHEELED SELF BALANCING MOBILE ROBOT

In this project is to design and implementation of PID based two wheeled self-balancing robot to solve the inclination angle problem to balance the movement of robot and to implement in real time. We are designing the code and implement an efficient self-balancing PID algorithm using the embedded controller and to implement in real time. Accelerometer is fitted on the robot to measure the angle of tilt during load imbalance .It gives a summary of the work done in the fields of mechanical design, electronics, software design, system characterization and control theory. This wide array of fields necessary for the realization of the project holds the project up as a leading example in the field of mechatronics. Here special focus will be on the modelling of the robotic system and the simulation results of various control methods required for the stabilization of the system.
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Comparison of PID methods for networked control systems

Comparison of PID methods for networked control systems

The immune control system is a physiological action that produces antibodies to combat antigens. The primary compo- nents of this system are the recognition cells and the killing cells. When the antigens arrive, recognition cells begin to multiply themselves at the same time they activate the helper T cells (T H ). Then, the helper T cells activate B cells, which secrete the antibodies. APC can also activate the suppressor T cells (T S), which can suppress the secretion of the helper T cells and the B cells. It can be generalised that the immune feedback algorithm is mainly based on the feedback regulating principle of T cell. The principle is as follows: ε(k) is the amount of antigens at the kth generation and it is defined by: ε(k) = γε(k − 1) − u kill (k − d) (25)
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Comparison of Speed control of PMSM with PI, PID& Adaptive PID Controllers

Comparison of Speed control of PMSM with PI, PID& Adaptive PID Controllers

This paper proposes, the speed control scheme for permanent magnet synchronous motor (PMSM) drives using different control methods. The proposed control algorithm was simple and easy to implement in the practical applications. For comparison purpose, the PI & PID controller was tested at the same conditions. From the experimental results it is clear that the adaptive PID controller method gives better performance compared to the other two control techniques.

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A New Approach for Control of Two wheeled Mobile Robot

A New Approach for Control of Two wheeled Mobile Robot

For the two-wheeled mobile robot at hand, a Mamdani Fuzzy Inference System with triangular membership functions and fuzzy sets NL (Negative Large), NB (Negative Big), NM (Negative Medium), NS (Negative Small), ZE (Zero), PS (Positive Small), PM (Positive Medium), PB (Positive Big) and PL (Positive Large) is designed. Position ( 𝑥 ) and orientation ( 𝜃 ) are used to derive the fuzzy rules. In deriving the fuzzy rules, the orientation is dealt with first; that is, the position error is compensated only after the orientation error approaches zero (ZE). In order for the orientation error to approach zero, it is assumed that the torque applied to the wheels must act in the same direction that the chassis deflects. Figure 3 illustrates one of the assigned rules to the orientation and position. If the chassis (i.e. the inverted pendulum of Fig. 3) deviates from the desired ZE orientation toward, say NL, the wheels must move to the left thereby justifying the use of NL as the output value for the wheels’ torque regardless of the position of the robot. Now with the orientation error having approached zero, the position error (i.e. NM in Fig. 3) is gently compensated. The resulting fuzzy rules are shown in Table 1.
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Fuzzy PID Control for a Cubic Robot Balancing on Its Corner

Fuzzy PID Control for a Cubic Robot Balancing on Its Corner

Abstract. In this paper, a cubic self-balancing robot is designed. Taking a prototype of a cubic robot that has been designed as a specific research object. For the attitude control of this institution. Using Lagrangian Method to establish a mathematical model of cubic robot. For its attitude control method. Fuzzy PID control method was proposed. Establishing simulation models in Matlab/Simulink environment. The simulation experiments using conventional PID control and fuzzy PID control were performed for comparison. The results show that the adoption of fuzzy PID control has faster response speed and smaller overshoot than traditional PID control and has better control effect. Introduction
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Development of self balancing platform on mobile robot using PID controller

Development of self balancing platform on mobile robot using PID controller

Control systems are often designed to improve stability, speed of response, steady- state error, or prevent oscillations. Many researchers wants to produce a mathematical equation that is able to determine the position of a very accurate motor position, thus the steady state error should be zero. DC motor systems have played an important role in the improvement and development of the industrial revolution. Therefore, the development of a more efficient control strategy that can be used for the control of a DC servomotor system and a well defined mathematical model that can be used for off line simulation are essential for this type of systems. Servomotor systems are known to have nonlinear parameters and dynamic factors, so to make the systems easy to control, conventional control methods such as PID controllers are very convenient. Also, the dynamics of the servomotor and outside factors add more complexity to the analysis of the system, for example when the load attached to the control system changes.
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Controller Design Of A Two-Wheeled Self Balancing Mobile Robot Base On A Simulation View Point

Controller Design Of A Two-Wheeled Self Balancing Mobile Robot Base On A Simulation View Point

Wheeled robots are the robots that can transport themselves form one place to another with the help of their wheels. A robot with wheeled motion can achieve mechanical term easily and with low cost compared to legged mobile robot. In addition, the control of wheeled moving is generally simpler. Due to these reasons, wheeled robots are becoming one of the most frequently seen robots. The types of wheeled mobile robot that have been developed by other researchers will be introduced at the following section.

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Modelling and control of DWR 1.0 – a two wheeled mobile robot

Modelling and control of DWR 1.0 – a two wheeled mobile robot

of the applications of an inverted pendulum on a two wheel. A relatively recent offshoot of the classical inverted pendulum is the wheel inverted pendulum, popularized in contemporary culture by the Segway Personal Transporter. However, the mathematical model for a wheel inverted pendulum do not account for the full complexity of the construction of the platform. The mathematical model obtained in this work with the measured parameters is simulated using Matlab and PID control parameters are determined. Finally, PID control algorithm is implemented on the two-wheeled mobile robot to test the accuracy of the model.
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Autonomous Wheeled Mobile Robot Control

Autonomous Wheeled Mobile Robot Control

The author applied the proposed fuzzy controller to the autonomous wheeled mobile robotic platform moving in an unstructured environment with obstacles. The control strategy was tested through simulations of wheeled mobile robot motion [24-27]. A simulation example of a wheeled autonomous mobile robotic platform is presented in Figure 3. The corresponding fuzzy control is implemented to perform tasks of obstacle and collision avoidance. In particular, the navigation strategy proved to be extremely sensitive to the balance between avoid obstacle and reach the target behaviors. Simulation results are shown in Figure 3.
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Application of model reduction for robust control of self-balancing two-wheeled bicycle

Application of model reduction for robust control of self-balancing two-wheeled bicycle

The vehicle moving by 2 wheels, when the vehicle deviates from the balance position (corresponding to a q angle according to vertical axis). The gravity of the vehicle creates a torque that makes the car tend to fall down. To maintain a state of equilibrium, we put on the vehicle a flywheel that operates on the principle of "the inverted pendulum". This flywheel will rotate around the axis (with an angular acceleration of ) and create a torque to compensate the torque generated by the vehicle's gravity. To control the acceleration of the flywheel, we uses a DC dc motor with the voltage applied to the motor being U. Then, the problem of balancing control becomes the problem of controlling the 𝜃 angle (output) by controlling the voltage U (input) applying to the motor. The problem requires that the 𝜃 angle (output) always go to zero. The self-balancing two-wheeled bicycle that the authors built is shown in Figure 1.
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Research on Double Cascade Control  Algorithm for Self Balancing  Two Wheeled Vehicle

Research on Double Cascade Control Algorithm for Self Balancing Two Wheeled Vehicle

To solve the problem of self-balancing two-wheeled vehicle, this article presents double cascade PID control algorithm. This method reduces the coupling of balance control, speed control and di- rection control, because of the special system structure. This article successfully solved the sensor fusion of gyroscope and accelerometer by using Kalman filtering algorithm, and adding in fuzzy PID algorithm to improve the flexibility of the steering system, thus greatly improving the accura- cy and response rate of the system.
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Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self Balancing Two Wheeled Robot

Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self Balancing Two Wheeled Robot

In psychology, operant is a class of behavior that pro- duces consequences by operating (i.e., acting) upon the environment. Operant conditioning (OC) is a technique of behavior modification through reinforcement and punishment. The research about operant conditioning theory [34] was started in 1938 by Skinner, a psychology professor. Its consequence influences the occurrence and form of behavior. Operant conditioning and classical conditioning [35] are two main learning ways of associa- tive learning, and all animals, including human, have these two learning way. Operant conditioning is distin- guished from classical conditioning in that operant con- ditioning deals with the modification of operant behavior. Operant conditioning reflects the relation between be- havior and its outcome, and the learning with OC theory is called operant learning (i.e. instrument learning). Re- cently, researchers apply OC theory in the robot learning and control and have done plenty of experimental studies. For example, Björn Brembs et al. [36] from Germany applies himself to the research of the operant condition- ing in flies (Drosophila) and snails. 'Pure' operant condi- tioning and parallel operant conditioning at the flight simulator were studied. Chengwei, Yao et al. [37] ap- plied OC theory into emotion development and presented an emotion development agent model based on OCC
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