Combined PID and LQR controller using optimized fuzzy rules

Asl, Reza Mohammadi; Mahdoudi, Amir; Pourabdollah, Elham; Klančar, Gregor

doi:10.1007/s00500-018-3180-3

Cited by 17 publications

(6 citation statements)

References 22 publications

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“…The multivariate nature of LQR allows for simultaneous control of displacement angles across the 3-link inverted pendulum [3]. LQR was chosen based on its capacity to deal with significant disturbance events and keep systems stable with no reductions in operational performance [14][15][16][17].…”

Section: Pid Controllermentioning

confidence: 99%

Analysing Various Control Technics for Manipulator Robotic System (Robogymnast)

Mahmoud¹,

Samad²,

Anayi³

et al. 2023

Computers, Materials &Amp; Continua

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The Robogymnast is a highly complex, three-link system based on the triple-inverted pendulum and is modelled on the human example of a gymnast suspended by their hands from the high bar and executing larger and larger upswings to eventually rotate fully. The links of the Robogymnast correspond respectively to the arms, trunk, and lower limbs of the gymnast, and from its three joints, one is under passive operation, while the remaining two are powered. The passive top joint poses severe challenges in attaining the smooth movement control needed to operate the Robogymnast effectively. This study assesses four types of controllers used for systems operation and identifies how far response stabilisation is achieved with each. The system is simulated using MATLAB Simulink, with findings generated regarding rising and settling time, as well as overshoot. The research primarily seeks to examine the application of a linear quadratic regulator controller, proportionalintegral-derivative controller, fuzzy linear quadratic regulator controller and linear quadratic regulator-proportional-integral-derivative controller for this type of system and comparisons between the different controllers to demonstrate successful performance, which highlights the claimed advantages of the proposed system.

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Section: Pid Controllermentioning

confidence: 99%

Analysing Various Control Technics for Manipulator Robotic System (Robogymnast)

Mahmoud¹,

Samad²,

Anayi³

et al. 2023

Computers, Materials &Amp; Continua

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“…Compared to the traditional PID algorithm, the algorithm is able to optimize the calculation process and find a suitable solution. In addition, to understand the effectiveness of the regulation algorithm, a simulation of a one-wheeled robot was used to verify the results, which displayed that its regulation effect was significantly improved [12]. George T et al put forward a stable regulation algorithm combined with an artificial neural network for the sake of improving the regulation performance of the current PID regulator, aiming to solve the rectification time lag system of the traditional PID, through the MATLAB platform.…”

Section: Related Workmentioning

confidence: 99%

Swing Steadiness Regulation of Electric Vehicles with Improved Neural Network PID Algorithm

Song

Hong

2022

Processes

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With the intensification of global environmental pollution and the energy crisis, the new energy EV industry is developing rapidly, and FWID-EV is a popular direction for future vehicle development. For the sake of improving the swing regulate steadiness and safety of EV, the study uses a particle swarm algorithm to optimize and improve the BP neural network PID, and designs an EV steering regulator to regulate the transverse swing torque and slip rate of EV to improve the safety and steadiness of EV steering. The research results display that the maximum value of the transverse swing angular velocity of the regulation algorithm is 0.156 rad/s, that the car slip rate is controlled within 0.046, and the steadiness is high, and that the maximum values of the car torque under the double shift line and snake conditions are 100 N-m and 179.4 N-m, respectively, which can effectively avoid the danger caused by steering. This demonstrates that the improved neural network PID regulator can effectively distribute the steering torque of the EV and improve the steering steadiness and safety of the EV while maintaining the driving dynamics. The use of the improved neural network PID algorithm to achieve the steering steadiness regulation of EV is of great significance to improve the safety of new energy EV, and helps to promote the widespread use of new energy EV.

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“…C F = 67.3s 6 +4.19×10 4 s 5 +1.53×10 10 s 3 +2.73×10 12 s 2 +1.97×10 14 s+3.79×10 15 s 7 +923s 6 +6.50×10 5 s 5 +3.59×10 8 s 4 +7.48×10 10 s 3 +5.45×10 12 s 2 +1.04×10 14 s C M = 34.8s 6 +1.81×10 4 s 5 +1.67×10 7 s 4 +6.21×10 9 s 3 +7.40×10 11 s 2 +3.26×10 13 s+4.63×10 14 s 7 +883.2s 6 +6.19×10 5 s 5 +3.36×10 8 s 4 +6.35×10 10 s 3 +3.60×10 12 s 2 +5.92×10 13 s C S = 12.1s 6 +5912s 5 +5.77×10 6 s 4 +2.00×10 9 s 3 +2.42×10 11 s 2 +1.07×10 13 s+1.52×10 14 s 7 +697s 6 +5.49×10 5 s 5 +2.56×10 8 s 4 +4.20×10 10 s 3 +2.34×10 12 s 2 +3.87×10 13 s (6) where C F , C M , and C S provide fast, intermediate, and smooth responses, respectively, for the PZT stage. The control design processes are illustrated in Appendix A, in which the stability margins of all controllers are greater than the system gap ∆ M ∆ N ∞ ; therefore, internal stability can be guaranteed during operation.…”

Section: Multiple Switching Control For the Pzt Stagementioning

confidence: 99%

“…Armaghan et al [5] designed two PID controllers and a switching logic for a magnetically driven system. Asl et al [6] proposed a fuzzy switching control, which fused a PID controller and a linear quadratic regulator, for a unicycle robot. Rana et al [7] applied model predictive control to improve the high-speed imaging performance of an atomic force microscope.…”

Section: Introductionmentioning

confidence: 99%

Iterative Parameter Optimization for Multiple Switching Control Applied to a Precision Stage for Microfabrication

et al. 2021

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This paper proposes an iteration procedure to derive optimal parameters for a multiple switching control architecture. Control design is usually a compromise between various performance requirements; therefore, switching between multiple controllers that achieve a particular performance under different conditions can potentially improve the overall system behavior. In this paper, we consider a control-switching mechanism that can automatically switch controllers based on the prediction of future responses, and we develop an iteration procedure that can optimize the mechanism parameters, such as the number of controllers and the prediction horizon. We then implement the proposed mechanism in a long-stroke precision stage, and demonstrate the effectiveness of switching robust control with simulations and experiments. Lastly, we integrate the stage with a two-photon polymerization system to fabricate microlenses. The optical properties confirm that the proposed iterative parameter optimization procedure is effective in improving the performance of microfabrication employing multiple switching control.

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Combined PID and LQR controller using optimized fuzzy rules

Cited by 17 publications

References 22 publications

Analysing Various Control Technics for Manipulator Robotic System (Robogymnast)

Analysing Various Control Technics for Manipulator Robotic System (Robogymnast)

Swing Steadiness Regulation of Electric Vehicles with Improved Neural Network PID Algorithm

Iterative Parameter Optimization for Multiple Switching Control Applied to a Precision Stage for Microfabrication

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