Design of an underactuated self balancing robot using linear quadratic regulator and integral sliding mode controller

Shilpa, B; Indu, V.; Rajasree, S. R.

doi:10.1109/iccpct.2017.8074224

Cited by 13 publications

(3 citation statements)

References 8 publications

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“…The proportional term is proportional to the error signal: The derivative term varies proportionally to how quickly the erroneous signal changes. The error has been decreasing at a rate of 1 degree per second: The control signal is the addition of the proportional, integral, and derivative terms: Control Signal = P + I + D = -5 + (-0.25) + (-0.1) = -5.35 (11) This control signal will be sent to the motor controller, which will adjust the speed and direction of the wheels to bring the robot back to a vertical position. The process is repeated continuously, with the PID controller adjusting the control signal based on the current error signal, to keep the robot always balanced.…”

Section: Calculate the Error Signalmentioning

confidence: 99%

AutomaticSelf Balancing Robot for Crop Monitoring

Shukla,

Ramprabhakar

2023

Preprint

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The Automatic Self Balancing Robot for Monitoring of Crop is a novel robotic system designed for the agricultural industry to improve crop monitoring and management. The robot is equipped with sensors, cameras, and machine learning algorithms that enable it to navigate through fields, collect da-ta on plant health, and identify potential issues here in this case fruit ripening (Early ripe, partially ripe, Ripe and Decay) with the help of camera. The self-balancing feature of the robot allows it to operate on uneven terrain, ensuring stable and accurate data gathering. Data gathered by the robot such as tempera-ture, humidity and soil moisture are sent to a central system for analysis, ena-bling agricultural producers to decide on crop management. The use of this ro-botic system can increase productivity, reduce employment costs, and improve overall harvest yields PID control, a feedback control loop, modifies the robot's behaviors in response to the discrepancy between the desired and actual states. A mathematical process called Kalman filtering is used to calculate and remove noise from sensor measurements, enabling more precise data interpretation. To improve the overall impression of the robot's orientation, sensor fusion com-bines data from several sensors, such as gyroscopes and accelerometers. IMUs are sensor bundles with integrated sensors that provide information on rotation, acceleration, and magnetic field strength, allowing for accurate measurement and control.

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Section: Calculate the Error Signalmentioning

confidence: 99%

AutomaticSelf Balancing Robot for Crop Monitoring

Shukla,

Ramprabhakar

2023

Preprint

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“…In the work of Pham and Lee [18] and Shilpa et al [19], sliding mode control was applied for stability of two wheeled Segway. The advantage of sliding mode control technique was its insensitivity to the parameter uncertainties and modeling errors of the system.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Technique for Upward Stabilization and Control of Two Wheeled Self-Balancing Segway

Khan

Choudhry

Ali

et al. 2022

murjet

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Two wheeled Self-Balancing Segway, which works on the principle of inverted pendulum, is naturally unstable, nonlinear, and under actuated system. Self-Balancing means the capability of the Segway to balance itself on two wheels without falling. Therefore, the system has to be controlled to reach stability in this unstable state. The two wheeled Segway is considered important due to its applications in daily life. In this paper, a hybrid control system is proposed for upward stabilization and control of two wheeled Segway. The control design approaches proposed in the literature for Segway result in a large control effort which requires high torque causing the saturation of actuator field and ultimately failure of the controller. Controller designed using the proposed approach is able to reduce the control effort by 64% compared to the ones available in literature. Moreover, controller designed through the proposed approach is able to improve disturbance rejection for both pitch and yaw angles of the Segway. Simulation results illustrate the effectiveness of the proposed approach over the ones available in the literature.

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“…The integral sliding Mode (ISMC) is considered as one of the most common controllers that are often used to control the TWSB mobile robot. Here are some of the previous works; Nguyen and Son [17,18] design ISMC and LQR for TWSB robot trajectory tracking. A novel implementation of an ISMC for regulation and set point control of a TWSB robot [19].…”

Section: Introductionmentioning

confidence: 99%

Modified Integral Sliding Mode Controller Design based Neural Network and Optimization Algorithms for Two Wheeled Self Balancing Robot

Karam¹,

Mjeed²

2018

IJMECS

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Two-wheeled Self-balancing (TWSB) mobile robot is considered to be highly nonlinear and unstable dynamic system. Unstable means that the robot is free to advance forward or backward without any forces applied. It must, therefore, be controlled. The purpose of this work is to design an intelligent nonlinear Modified Integral Sliding Mode Controller (MISMC) based on simple Adaline neural network for balancing a two-wheeled selfbalancing mobile robot, in addition to improve the performance of this robot in tracking the desired trajectory. The simple Adaline neural network is used to enhance the performance of the conventional Integral Sliding Mode Controller (ISMC) which is an effective and powerful technique because it has a high performance. Also, in this work, a Modified Particle Swarm Optimization (MPSO) and Modified Cuckoo Search (MCS) algorithms have been proposed to find and tune the best MISMC parameters and hence enhance the performance characteristics of the robot system by reducing the processing time as well as improving the response accuracy through minimizing the tracking error of the mobile robot. The Integral Square Error (ISE) method has been used as a performance index for the two algorithms (MPSO, MCS) to measure the performance of the proposed controller. Numerical simulations show the efficiency of the suggested controller by handling the balance and tracking problems of the two-wheeled selfbalancing mobile robot. Index Terms-Modified Integral sliding mode controller, Two-wheeled self-balancing mobile robot, modified PSO, modified CS, optimization algorithms, neural network.

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Design of an underactuated self balancing robot using linear quadratic regulator and integral sliding mode controller

Cited by 13 publications

References 8 publications

AutomaticSelf Balancing Robot for Crop Monitoring

AutomaticSelf Balancing Robot for Crop Monitoring

A Hybrid Technique for Upward Stabilization and Control of Two Wheeled Self-Balancing Segway

Modified Integral Sliding Mode Controller Design based Neural Network and Optimization Algorithms for Two Wheeled Self Balancing Robot

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