Controlling wheeled mobile robot considering the effects of uncertainty with neuro-fuzzy cognitive map

Amirkhani, Abdollah; Shirzadeh, Masoud; Shojaeefard, Mohammad Hassan; Abraham, Ajith

doi:10.1016/j.isatra.2019.12.011

Cited by 29 publications

(18 citation statements)

References 35 publications

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“…where c k , i k and d k are the proportional, integral and differential coefficients, respectively. From ( 24), the parameter setting rules of the IMC-PID controller are obtained as follows 22 21 2…”

Section:  mentioning

confidence: 99%

“…In the past, the combination of PID and advanced control methods and PID parameter optimization are always the research hotspots in the field of control engineering. On the one hand, some nonlinear PID control strategies, such as fractional PID [16], [17], neural network PID [18], [19], neural fuzzy PID [20], [21], and particle swarm optimization PID [22], [23] have been proposed. On the other hand, it is particularly important to adjust the PID controller parameters appropriately, using methods such as the well-known Ziegler -Nichols (ZN) tuning rule [24], H 2 /H ∞ robust optimization design [25], internal model control (IMC) principle [26], and other parameter adjustment methods [27], [28].…”

Section: Introductionmentioning

confidence: 99%

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Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

et al. 2021

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Servo systems driven by a permanent magnet linear synchronous motor (PMLSM) are often affected by uncertain disturbances, such as magnetic resistance, friction, and external disturbances, which increase tracking errors and reduce motion accuracy. Traditional control methods may have difficulty to achieve satisfactory control performance in terms of tracking accuracy and disturbance rejection. In this paper, we propose an internal model control PID method based on a model linear extended state observer, which is termed as IMC-PID-MLESO method. With this method, the nominal model parameters of the PMLSM servo system are obtained via system identification. The model linear extended state observer (MLESO), with known parameter information, is designed to improve the estimation accuracy for the system states and total unknown uncertainties. As the uncertainties are compensated in the feedback control law, the PMLSM servo system model is transformed into a known nominal model. Based on the nominal model, the IMC-PID feedback controller is designed to ensure satisfactory performance on disturbance rejection and tracking error reduction. Simulation and experimental results show that the IMC-PID-MLESO method can effectively improve the position tracking accuracy of the PMLSM servo system and rapidly suppress the system disturbance.INDEX TERMS Disturbance suppression, internal model control, permanent magnet linear synchronous motor, tracking accuracy, uncertainties.

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Section:  mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

et al. 2021

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“…The presented controller implemented on a WMR with considering the effect of uncertainties by increasing the mass of the robot with regarding time. 26 Taran and Pirmohammadi presented a fuzzy SMC based on a genetic algorithm for a 2R manipulator with uncertainties. In their model, the masses of the links are considered to be uncertain parameters.…”

Section: Introductionmentioning

confidence: 99%

Designing an optimal control strategy for a mobile manipulator and its application by considering the effect of uncertainties and wheel slipping

Korayem

Ghobadi

Dehkordi

2021

Optim Control Appl Methods

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This article seeks to develop the dynamic model of a mobile robot for controlling purposes while the effects of uncertainties and longitudinal and lateral slip are assumed. The rise in the number of state variables and considering the effects of nonlinear parameters due to slip modeling can complicate the control procedure of wheeled mobile manipulators. In addition, the existence of uncertainties as well as holonomic and nonholonomic constraints would increase this complexity even more. On the one side, as wheels are considered to slip, unwanted movements due to slippage affect the movement of the manipulator and the manipulator may not be able to track the desired path. On the other side, the movement of weighted links of the manipulator constantly changes the normal forces of the wheels. As traction forces are dependent on these normal forces, the path tracked by the platform would be affected. Given these conditions, employing a robust control methodology and developing an optimal algorithm to reduce the effects of uncertainties and wheels slip seems mandatory. To this aim, a sliding mode control (SMC) strategy is formulated in this study. Furthermore, the suggested algorithm is optimized using a state-dependent Riccati equation to reduce the consumed torque and increase the accuracy of the system. Simulations are conducted, and the presented algorithm is also checked with the experimental setup of a Scout robot. Simulation results show that the optimal sliding mode control (OSMC) successfully decreases sudden jumps due to slippage in the system. In addition, the consumed power of wheels and arm for SMC decreases from 9.15 and 1.02 N⋅m to, respectively, 1.46 and 0.54 N⋅m for OSMC.

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“…With the development of AI technology, mobile robots are appearing more and more frequently in public. According to the motion mode, mobile robots can be divided into wheeled robots [1,2], tracked robots [3], and legged robots [4,5]. In the face of complex environments as well as rugged terrains, legged robots have incomparable flexibility and applicability compared with the other two kinds of robots.…”

Section: Introductionmentioning

confidence: 99%

A New Foot Trajectory Planning Method for Legged Robots and Its Application in Hexapod Robots

Xia

Zhang

2021

Applied Sciences

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Compared with wheeled and tracked robots, legged robots have better movement ability and are more suitable for the exploration of unknown environments. In order to further improve the adaptability of legged robots to complex terrains such as slopes, obstacle environments, and so on, this paper makes a new design of the legged robot’s foot sensing structure that can successfully provide accurate feedback of the landing information. Based on this information, a new foot trajectory planning method named three-element trajectory determination method is proposed. For each leg in one movement period, the three elements are the start point in the support phase, the end point in the support phase, and the joint angle changes in the transfer phase where the first two elements are used to control the height, distance, and direction of the movement, and the third element is used make decisions during the lifting process of the leg. For the support phase, the trajectory is described in Cartesian space, and a spline of linear function with parabolic blends is used. For the transfer phase, the trajectory is described in joint-space, and the joint angle function is designed as the superposition of the joint angle reverse-chronological function and the interpolation function which is obtained based on joint angle changes. As an important legged robot, a hexapod robot that we designed by ourselves with triangle gait is chosen to test the proposed foot trajectory planning method. Experiments show that, while the foot’s landing information can be read and based on the three-element trajectory planning method, the hexapod robot can achieve stable movement even in very complex scenes. Although the experiments are performed on a hexapod robot, our method is applicable to all forms of legged robots.

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Controlling wheeled mobile robot considering the effects of uncertainty with neuro-fuzzy cognitive map

Cited by 29 publications

References 35 publications

Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

Designing an optimal control strategy for a mobile manipulator and its application by considering the effect of uncertainties and wheel slipping

A New Foot Trajectory Planning Method for Legged Robots and Its Application in Hexapod Robots

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