Trajectory Tracking Control of a Spherical Robot Based on Adaptive PID Algorithm

Song, Zihao Hunter; Wu, Bin; Ting, Zhou

doi:10.1109/ccdc.2019.8833053

Cited by 18 publications

(10 citation statements)

References 5 publications

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“…There are also research focusing on motion planning of spherical robots with optimal planners that place a strong emphasis on obstacles [14], [15]. The majority of spherical robot direction controllers only have simulation results, such as the direction control component in [5], [13], [16]. A fullstate feedback controller for roll motion is provided in [17].…”

Section: Introductionmentioning

confidence: 99%

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Direction and Trajectory Tracking Control for Nonholonomic Spherical Robot by Combining Sliding Mode Controller and Model Prediction Controller

Liu,

Wang,

Guan

et al. 2022

Preprint

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Spherical robot is a nonlinear, nonholonomic and unstable system which increases the difficulty of the direction and trajectory tracking problem. In this study, we propose a new direction controller HTSMC, an instruction planning controller MPC, and a trajectory tracking framework MHH. The HTSMC is designed by integrating a fast terminal algorithm, a hierarchical method, the motion features of a spherical robot, and its dynamics. In addition, the new direction controller has an excellent control effect with a quick response speed and strong stability. MPC can obtain optimal commands that are then transmitted to the velocity and direction controller. Since the two torque controllers in MHH are all Lyapunovbased sliding mode controllers, the MHH framework may achieve optimal control performance while assuring stability. Finally, the two controllers eliminate the requirement for MPC's stability and dynamic constraints. Finally, hardware experiments demonstrate the efficacy of the HTSMC, MPC, and MHH.

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Section: Introductionmentioning

confidence: 99%

“…There are other studies on trajectory tracking and instruction planning [16], [22], [23]. However, they only provide simulation results without on hardware experiments.…”

Section: Introductionmentioning

confidence: 99%

Direction and Trajectory Tracking Control for Nonholonomic Spherical Robot by Combining Sliding Mode Controller and Model Prediction Controller

Liu,

Wang,

Guan

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To get the ideal learning gain, many explores have been finished including variable exponential gain technique, fuzzy PID strategy, and the strategy joining neural network controller and compensation controller. Be that as it may, the learning law and the complex control law will bring a huge measure of calculation time and influence the speed of trajectory tracking [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Optimized LQR-PID Controller Using Squirrel Search Algorithm for Trajectory Tracking of a 6-DoF Parallel Manipulator

Choubey

Ohri

2021

Preprint

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In 6 Degree of Freedom (DOF) parallel manipulator, trajectory tracking is one of the main challenges. To obtain the desired trajectory, the DC motor needs to generate optimal torque. So to obtain optimal torque, an optimized Linear Quadratic Regulator-Proportional–Integral–Derivative (LQR-PID) controller is presented in this paper. For optimizing the Q, R and gain parameters of LQR-PID controller, Squirrel Search Algorithm (SSA) is presented. In this algorithm, minimal cost function of LQR-PID controller is considered as objective function. The SSA based LQR-PID controller leads the motor to generate optimal torque that helps to attain the desired trajectory of 6-DOF parallel manipulator. Results of the work depicts that the SSA based LQR-PID controller achieves the best mean velocity, sum square error (SSE), integral square error (ISE) and integral absolute error (IAE).

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“…It deals with the problem of carrying a robot from its current position to a known destination point [7]. It has been addressed in many ways by using different traditional control techniques, such as nonlinear model predictive control [8], back-stepping control with asymptotic stability [9], continuous time-varying adaptive controllers [10], PID controllers [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Position Control Problem of a Mobile Robot

et al. 2020

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Due to the increase in complexity in autonomous vehicles, most of the existing control systems are proving to be inadequate. Reinforcement Learning is gaining traction as it is posed to overcome these difficulties in a natural way. This approach allows an agent that interacts with the environment to get rewards for appropriate actions, learning to improve its performance continuously. The article describes the design and development of an algorithm to control the position of a wheeled mobile robot using Reinforcement Learning. One main advantage of this approach concerning traditional control algorithms is that the learning process is carried out automatically with a recursive procedure forward in time. Moreover, given the fidelity of the model for the particular implementation described in this work, the whole learning process can be carried out in simulation. This fact avoids damages to the actual robot during the learning stage. It shows that the position control of the robot (or similar specific tasks) can be done without the need to know the dynamic model of the system explicitly. Its main drawback is that the learning stage can take a long time to finish and that it depends on the complexity of the task and the availability of adequate hardware resources. This work provides a comparison between the proposed approach and traditional existing control laws in simulation and real environments. The article also discusses the main effects of using different controlled variables in the performance of the developed control law.

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Trajectory Tracking Control of a Spherical Robot Based on Adaptive PID Algorithm

Cited by 18 publications

References 5 publications

Direction and Trajectory Tracking Control for Nonholonomic Spherical Robot by Combining Sliding Mode Controller and Model Prediction Controller

Direction and Trajectory Tracking Control for Nonholonomic Spherical Robot by Combining Sliding Mode Controller and Model Prediction Controller

Optimized LQR-PID Controller Using Squirrel Search Algorithm for Trajectory Tracking of a 6-DoF Parallel Manipulator

Reinforcement Learning for Position Control Problem of a Mobile Robot

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