Indoor Autonomous Control of a Two-Wheeled Inverted Pendulum Vehicle Using Ultra Wide Band Technology

Xia, Dunzhu; Yao, Yanhong; Cheng, Limei

doi:10.3390/s17061401

Cited by 7 publications

(7 citation statements)

References 23 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Method in [34] Method in [35]  -shape Fish-shape S-shape We also used the swept error area standard (SEA) [29] to evaluate the proposed method and the traditional methods [34,35] quantitatively. The SEA standard measures the difference between the generated and the reference trajectories by their included area (Figure 9).…”

Section: Proposed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Wheeled Inverted Pendulum Learning Stable and Accurate Control from Demonstrations

Jin

2019

Applied Sciences

View full text Add to dashboard Cite

In order to enable robots to be more intelligent and flexible, one way is to let robots learn human control strategy from demonstrations. It is a useful methodology, in contrast to traditional preprograming methods, in which robots are required to show generalizing capacity in similar scenarios. In this study, we apply learning from demonstrations on a wheeled, inverted pendulum, which realizes the balance controlling and trajectory following simultaneously. The learning model is able to map the robot position and pose to the wheel speeds, such that the robot regulated by the learned model can move in a desired trajectory and finally stop at a target position. Experiments were undertaken to validate the proposed method by testing its capacity of path following and balance guaranteeing.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

“…The results by the proposed method and two traditional control methods [34,35] are shown in Figure 8. It can be seen that the proposed learning method can smoothly follow the reference trajectory and finally converge to the target position.…”

Section: Demonstrationsmentioning

confidence: 99%

A Wheeled Inverted Pendulum Learning Stable and Accurate Control from Demonstrations

Jin

2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Moreover, to extend to the estimation of unknown parameters of the system, the adaptive backstepping methods [6,7] were put forward to estimate some unknown parameters of the system. Furthermore, some adaptive backstepping controllers were used for some linear machines [8,9] to estimate uncertainty. In addition, some neural networks [10,11,12] have been used for the nonlinear systems to estimate unknown parameters for uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive

Lin

Chang

2019

Sensors

View full text Add to dashboard Cite

In order to cut down influence on the uncertainty disturbances of a linear motion single axis robot machine, such as the external load force, the cogging force, the column friction force, the Stribeck force, and the parameters variations, the micrometer backstepping control system, using an amended recurrent Gottlieb polynomials neural network and altered ant colony optimization (AACO) with the compensated controller, is put forward for a linear motion single axis robot machine drive system mounted on the linear-optical ruler with 1 um resolution. To achieve high-precision control performance, an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is proposed to estimate the lumped uncertainty. Besides this, a novel error-estimated law of the compensated controller is also proposed to compensate for the estimated error between the lumped uncertainty and the amended recurrent Gottlieb polynomials neural network with the adaptive law. Meanwhile, the AACO is used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. The main contributions of this paper are: (1) The digital signal processor (DSP)-based current-regulation pulse width modulation (PWM) control scheme being successfully applied to control the linear motion single axis robot machine drive system; (2) the micrometer backstepping control system using an amended recurrent Gottlieb polynomials neural network with the compensated controller being successfully derived according to the Lyapunov function to diminish the lumped uncertainty effect; (3) achieving high-precision control performance, where an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is successfully applied to estimate the lumped uncertainty; (4) a novel error-estimated law of the compensated controller being successfully used to compensate for the estimated error; and (5) the AACO being successfully used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. Finally, the effectiveness of the proposed control scheme is also verified by the experimental results.

show abstract

“…Vrkalovic et al [ 11 ] developed the design of Takagi-Sugeno fuzzy controllers in state feedback form by the use of swarm intelligence optimization algorithms. The adaptive backstepping controllers were applied in linear induction machine control [ 12 , 13 ]. Furthermore, the adaptive backstepping controllers combined with the neural networks to control the nonlinear systems have been proposed in References [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Precision Motion Control of a Linear Permanent Magnet Synchronous Machine Based on Linear Optical-Ruler Sensor and Hall Sensor

Lin

2018

Sensors

View full text Add to dashboard Cite

The linear optical-ruler sensor with 1 μm precision mounted in the linear permanent magnet synchronous machine (LPMSM) is used for measuring the mover position of LPMSM in order to enhance the precision of a measured mover position. Due to nonlinear friction and uncertainty effects, linear controllers are very hard to achieve good mover positioning of LPMSM. The proposed adaptive amended Elman neural network backstepping (AAENNB) control system is adopted for controlling the LPMSM drive system to bring about the mover positioning precision of LPMSM. Firstly, a backstepping scheme is posed for controlling the tracing motion of the LPMSM drive system. The proposed backstepping control system, which is applied in the mover position of the LPMSM drive system, possesses better dynamic control performance and robustness to uncertainties for the tracing trajectories. Because of the LPMSM with nonlinear and time-varying dynamic characteristics, an adaptive amended Elman neural network uncertainty observer (AAENNUO) is posed to estimate the required lumped uncertainty. According to the Lyapunov stability theorem, on-line parameter training methodology of the amended Elman neural network (AENN) can be derived by use of adaptive law. The error estimated law is proposed to compensate for the observed error induced by the AENN with adaptive law. Furthermore, to help improve convergence and to obtain better learning performance, the mended particle swarm optimization (PSO) algorithm is utilized for adjusting the varied learning rate of the weights in the AENN. At last, these experimental results, which show better performance, are verified by the proposed control system.

show abstract

Indoor Autonomous Control of a Two-Wheeled Inverted Pendulum Vehicle Using Ultra Wide Band Technology

Cited by 7 publications

References 23 publications

A Wheeled Inverted Pendulum Learning Stable and Accurate Control from Demonstrations

A Wheeled Inverted Pendulum Learning Stable and Accurate Control from Demonstrations

Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive

Precision Motion Control of a Linear Permanent Magnet Synchronous Machine Based on Linear Optical-Ruler Sensor and Hall Sensor

Contact Info

Product

Resources

About