Adaptive PD-SMC for Nonlinear Robotic Manipulator Tracking Control

Kara, Tolgay; Mary, Ali Hussien

doi:10.24846/v26i1y201706

Cited by 22 publications

(20 citation statements)

References 5 publications

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“…sliding mode control (SMC) is an efficient and popular control approach that has been applied effectively for control many nonlinear systems such as robotic systems, DC-DC converter, etc. Fast response and strong robustness are the important advantages of SMC [19][20][21][22]. On the other hand, the chattering and steady-state errors are a major drawback of the SMC.…”

Section: Introductionmentioning

confidence: 99%

System uncertainties estimation based adaptive robust backstepping control for DC DC buck converter

Mary

Miry²,

Miry³

2021

IJECE

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This paper proposed a novel adaptive robust backstepping control scheme for DC-DC buck converter subjected to external disturbance and system uncertainty. Uncertainty in the load resistance and the input voltage represent the big challenge in buck converter control. In this work, an adaptive estimator for matched and mismatched uncertainties based backstepping control is applied for DC-DC buck converter. The updating laws are determined based on the lyapunov theorem. Thus, the difference between the estimated parameters and actual parameters converges to zero. The proposed control method is compared with the conventional sliding mode control and integral sliding mode control. Simulation results demonstrate the effectiveness and robustness of the proposed controller.

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Section: Introductionmentioning

confidence: 99%

System uncertainties estimation based adaptive robust backstepping control for DC DC buck converter

Mary

Miry²,

Miry³

2021

IJECE

Self Cite

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“…Sliding mode control is an efficient, robust control method that widely used in the control of many complex systems [5]. However, chattering is a big drawback of sliding mode control [6]. The most popular controller in the past is a proportional-integral-derivative controller (PID).…”

Section: Introductionmentioning

confidence: 99%

UAV Control Based on Dual LQR and Fuzzy-PID Controller

Al-Isawi¹,

Attiya²,

Adoghe

2020

alkej

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This paper presents the design of a longitudinal controller for an autonomous unmanned aerial vehicle (UAV). This paper proposed the dual loop (inner-outer loop) control based on the intelligent algorithm. The inner feedback loop controller is a Linear Quadratic Regulator (LQR) to provide robust (adaptive) stability. In contrast, the outer loop controller is based on Fuzzy-PID (Proportional, Integral, and Derivative) algorithm to provide reference signal tracking. The proposed dual controller is to control the position (altitude) and velocity (airspeed) of an aircraft. An adaptive Unscented Kalman Filter (AUKF) is employed to track the reference signal and is decreased the Gaussian noise. The mathematical model of aircraft has been (Cessna 172) presented. The stability and robustness of the system have been verified in a simulation experiment.

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“…In (Zhou & Xia, 2015), a cooperation control strategy was designed to complete the escorting mission in the environments with obstacles, and the system is proved to be convergent in finitetime. However, it requires a certain knowledge of system parameters discussed in the above mentioned specialized literatures, while in many practices it is difficult to obtain accurately (Kara & Mary, 2017, Yue et al, 2016. In (Zhang et al, 2018), the APD-SMC method was employed to enhance the capability of 2D overhead crane systems as it is simple and robust.…”

Section: Introductionmentioning

confidence: 99%

Robust Coordinated Tracking Control of Multiple Robots System Under Bounded Inputs

Gao¹,

Song²,

Zheng³

et al. 2020

STUD INFORM CONTROL

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The coordinated tracking control of multiple robots system with input saturation and obstacle avoidance is investigated in this paper. Additionally, a robust hierarchical outer-inner layer controller is proposed. In order to avoid obstacles and realize coordination tracking control, null-space-based behavioural (NSB) control is applied in the outer layer where it generates the velocity required by the inner layer. An improved adaptive radical basis function neural networks (RBFNNS) proportional derivative-sliding mode control (IRPD-SMC) method is proposed in the inner layer to achieve robust tracking, null steady-state errors, and bounded inputs. Finally, in an environment with obstacles, all robots can track the target at a fixed distance and distribute around it evenly. The convergence and stability of the system are certified by Lyapunov stability theory. Simulation results in the 2D and 3D space show that the proposed IRPD-SMC is effective, by comparing the performances of this method with those of proportional derivative-sliding mode control (PD-SMC), adaptive proportional derivative-sliding mode control (APD-SMC), and adaptive sliding mode control (ASMC).

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Adaptive PD-SMC for Nonlinear Robotic Manipulator Tracking Control

Cited by 22 publications

References 5 publications

System uncertainties estimation based adaptive robust backstepping control for DC DC buck converter

System uncertainties estimation based adaptive robust backstepping control for DC DC buck converter

UAV Control Based on Dual LQR and Fuzzy-PID Controller

Robust Coordinated Tracking Control of Multiple Robots System Under Bounded Inputs

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