Novel PID Tracking Controller for 2DOF Robotic Manipulator System Based on Artificial Bee Colony Algorithm

Elkhateeb, Nasr A.; Badr, Ragia I.

doi:10.1515/ecce-2017-0008

Cited by 27 publications

(18 citation statements)

References 13 publications

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“…For comparison purposes, we select the same control parameters to range as presented by ElKhateeb and Badr 30 and we define the control constraint to be MAE < 0.04636. MAE is given in (43): ElKhateeb and Badr 30 assume a 2-DOF arm with specifications of M 1 = 1 .1em kg, M 2 = 1 .1em kg for the masses of the 2-DOF arm, and l 1 = 1 .1em m, l 2 = 1 .1em m for the lengths of the two arms. In our approach, the lengths of the arms and the motors are defined as design variables for the optimizer.…”

Section: Resultsmentioning

confidence: 99%

Towards a formal framework for integrated design-optimization and control of mechatronicsystems

Rahimi

2021

Science Progress

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This paper presents work towards a formal framework to support model-based integrated design and optimization of mechatronic products for early-phase conceptual design. This paper describes an integrated design framework through the introduction of its software implementation and a specific use case. The contribution is to introduce mathematical formalism to define the concepts, semantics, computation rules and system architectures of the formal framework. The advantage of the formal definitions is to clearly expose functionality and the limitations of the design framework and facilitate the software implementation. The modelling capability of the framework is enhanced to include non-linear mechatronic components, such as a two degrees-of-freedom arm. Further, an optimal proportional–integral–derivative control component is added to the software library supporting the framework.

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Section: Resultsmentioning

confidence: 99%

Towards a formal framework for integrated design-optimization and control of mechatronicsystems

Rahimi

2021

Science Progress

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“…MATLAB R2019b is applied as the simulation platform to track the ideal curve trajectories by using the FETSM, the ETSM [27], the ESM [28] and PID [10] methods respectively. By comparing the error of position tracking of each joint, the effects of the trajectory tracking of the above four control methods are analyzed.…”

Section: Analysis On Trajectory Trackingmentioning

confidence: 99%

Trajectory-Tracking Control for Manipulators Based on Fuzzy Equivalence and a Terminal Sliding Mode

Liu

Zhang

et al. 2021

SV-JME

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To suppress the chattering of manipulators under heavy-load operations, a control method called fuzzy equivalence & terminal sliding mode (FETSM) was applied to the trajectory tracking of motion curves for manipulators. Based on the switching term of the equivalent sliding mode (ESM), a fuzzy parameter matrix processed by the simple fuzzy rules was introduced, and the fuzzy switching term was obtained. By summing the fuzzy switching term and the equivalent term of the equivalence and a terminal sliding mode (ETSM), the control law of the FETSM for manipulators was obtained. On this basis, the stability of the system was analysed and the finite arrival time of it was deduced. On the premise of ensuring the stability of the system, the fuzzy rules and membership functions were designed for the fuzzy constants in the fuzzy switching term. Simulation tests show that the proposed FETSM can ensure sufficient trajectory-tracking precision, error convergence speed, and robustness. Compared with the ETSM, the proposed FETSM can reduce the chattering time by 94.75 % on average; compared with the proportion-integral-differential (PID) control method, the maximum chattering amplitude by the FETSM can be reduced by at least 99.21 %. Thus, the proposed FETSM is suitable for those manipulators under heavy-load operations.

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“…Guechi et al [5] did a comparative study between a MPC and Linear Quadratic (LQ) optimal control of a two-link robot arm, and the simulation results showed that the proposed MPC gave a better system performance then the LQ optimal control approach. Elkhateeb [6] developed a novel tuning methodology of a PID controller for trajectory tracking of a manipulator robot. The optimal gains of the PID controller are obtained by using a dynamic inertia weight artificial bee colony optimization algorithm.…”

Section: Related Workmentioning

confidence: 99%

Model Predictive Control of a Differential-Drive Mobile Robot

Bouzoualegh

Guechi

Kelaiaia

2018

Acta Universitatis Sapientiae Electrical and Mechanical Engineering

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This paper presents a model predictive control (MPC) for a differential-drive mobile robot (DDMR) based on the dynamic model. The robot’s mathematical model is nonlinear, which is why an input–output linearization technique is used, and, based on the obtained linear model, an MPC was developed. The predictive control law gains were acquired by minimizing a quadratic criterion. In addition, to enable better tuning of the obtained predictive controller gains, torques and settling time graphs were used. To show the efficiency of the proposed approach, some simulation results are provided.

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Novel PID Tracking Controller for 2DOF Robotic Manipulator System Based on Artificial Bee Colony Algorithm

Cited by 27 publications

References 13 publications

Towards a formal framework for integrated design-optimization and control of mechatronicsystems

Towards a formal framework for integrated design-optimization and control of mechatronicsystems

Trajectory-Tracking Control for Manipulators Based on Fuzzy Equivalence and a Terminal Sliding Mode

Model Predictive Control of a Differential-Drive Mobile Robot

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