Trajectory tracking control of a wheeled mobile robot in the presence of matched uncertainties via a composite control approach

Shafei, H. R.; Bahrami, Mohsen

doi:10.1002/asjc.2418

Cited by 9 publications

(5 citation statements)

References 67 publications

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“…The non‐holonomic constrained nature of mobile robots makes controller design much more difficult. To achieve attitude stabilisation and trajectory tracking, many control methods have been proposed for non‐complete WMRs, such as backstepping controllers [6], adaptive state and output feedback controllers [7] and neural network‐based controllers [8]; the work [9] proposed an adaptive sliding mode control (ASMC) method, and this controller not only controls a non‐linear system robustly against uncertainties and external disturbances but also optimises a quadratic cost function. As computer performance improves, many algorithms based on optimisation theory are gradually being applied to robot control.…”

Section: Introductionmentioning

confidence: 99%

Robust model predictive tracking control for the wheeled mobile robot with boundary uncertain based on linear matrix inequalities

Gao

et al. 2023

IET Cyber-Syst and Robotics

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In this study, a robust model predictive controller is designed for the trajectory tracking problem of non-holonomic constrained wheeled mobile robot based on an elliptic invariant set approach. The controller is based on a time-varying error model of robot kinematics and uses linear matrix inequalities to solve the robust tracking problem taking uncertainties into account. The uncertainties are modelled by linear fractional transform form to contain both parameter perturbations and external disturbances. The control strategy consists of a feedforward term that drives the centre of the ellipse to the reference point and a feedback term that converges the uncertain system state error to the equilibrium point. The strategy stabilises the nominal system and ensures that all states of the uncertain system remain within the ellipsoid at each step, thus achieving robust stability of the uncertain system. Finally, the robustness of the algorithm and its resistance to disturbances are verified by simulation and experiment.

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Section: Introductionmentioning

confidence: 99%

Robust model predictive tracking control for the wheeled mobile robot with boundary uncertain based on linear matrix inequalities

Gao

et al. 2023

IET Cyber-Syst and Robotics

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“…For instance, Sun et al 7 presented an adaptive integral terminal sliding mode (AITSM) control algorithm for a trajectory-tracking task that exhibits great superiority in tracking precision and control robustness. Hamid et al 8 combined the optimal and robust control system with adaptive gains to follow the desired path. By comparing the results of this approach with those of an adaptive sliding mode control (ASMC), they found that their proposed controller exerts less control effort.…”

Section: Introductionmentioning

confidence: 99%

An improved sliding mode approach for trajectory following control of nonholonomic mobile AGV

Jiang

Yang

2022

Sci Rep

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This paper attempts to address the trajectory following control problem of nonholonomic mobile AGV by proposing an improved sliding mode control approach in which, based on the kinematics and attitude deviations established for AGV, the motion characteristics are analyzed and a backstepping sliding mode control with a novel reaching law is designed. This reaching law integrates the merits of the power and exponential reaching laws and promotes the convergence rates of tracking errors. Moreover, with the improved sliding mode controller, the asymptotic stability of tracking deviations can be strictly guaranteed. The simulations have demonstrated the effectiveness and superiority of the proposed approach for mobile AGV.

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“…With the development of science and technology, more and more complex nonlinear systems are emerging. To control such systems, intelligent controllers are constantly used [1][2][3]. In particular, the cerebellar model articulation controller (CMAC) is one of the highly effective intelligent control methods [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Wavelet CMAC for MIMO uncertain nonlinear systems using a modified social ski‐driver algorithm

Lin

et al. 2022

Asian Journal of Control

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This study uses a Mexican hat wavelet membership function for a cerebellar model articulation controller (CMAC) to develop a more efficient adaptive controller for multiple input multiple output (MIMO) uncertain nonlinear systems. The main controller is called the adaptive Mexican hat wavelet CMAC (MWCMAC), and an auxiliary controller is used to remove the residual error. For the MWCMAC, the online learning laws are derived from the gradient descent method. In addition, the learning rate values are very important and have a great impact on the performance of the control system; however, they are difficult to choose accurately. Therefore, a modified social ski driver (SSD) algorithm is proposed to find optimal learning rates for the control parameters. Finally, a magnetic ball levitation system and a nine‐link biped robot are used to illustrate the effectiveness of the proposed SSD‐based MWCMAC control system. The comparisons with other existing control algorithms have shown the superiority of the proposed control system.

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Trajectory tracking control of a wheeled mobile robot in the presence of matched uncertainties via a composite control approach

Cited by 9 publications

References 67 publications

Robust model predictive tracking control for the wheeled mobile robot with boundary uncertain based on linear matrix inequalities

Robust model predictive tracking control for the wheeled mobile robot with boundary uncertain based on linear matrix inequalities

An improved sliding mode approach for trajectory following control of nonholonomic mobile AGV

Wavelet CMAC for MIMO uncertain nonlinear systems using a modified social ski‐driver algorithm

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