Optimal Nonlinear PID Control of a Micro-Robot Equipped with Vibratory Actuator Using Ant Colony Algorithm: Simulation and Experiment

Karami, Marzieh; Tavakolpour-Saleh, A.R.; Norouzi, Ashkan

doi:10.1007/s10846-020-01165-5

Cited by 27 publications

(20 citation statements)

References 26 publications

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“…For the robotics control, the classical PID methods 37 is still a main part in any controllers and they have been applied widely in industrial applications due to its design simplicity and good performance, particularly in applications, in which the control system parameters are not well known. Thus, in order to overcome the drawbacks of classical PID methods, the researchers have continued to propose the intelligent control algorithms [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] based on PID technique for robotics control, recently. Wen and Jacob 41 solved the problems of big derivative and integral gains to assure asymptotic stability of control system by a neural PID algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In this control scheme, 47 the sliding surface was also designed by PID technique, 42 and its parameters were chosen based on experiments. Similar to the PID gains self-turning strategies by Neural/Fuzzy 40 techniques, Karami et al 49 proposed an ant colony optimization algorithm to optimize the parameters of PID controller for micro-robot equipped with vibratory actuator. And these procedures have guaranteed the adaptability of control system in the presence of uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid adaptive tracking control method for mobile manipulator robot based on Proportional–Integral–Derivative technique

Long

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this research, an adaptive tracking control method for the nonholonomic robot system is addressed based on the hybrid Proportional–Integral–Derivative (PID) technique. The proposed hybrid PID scheme first applies the merits of the traditional PID method, with the online self-learning capability for the PID – gains, to force tracking errors to zero in the presence of uncertainties. Then, in order to improve the tracking performance, an adaptive Fuzzy Neural Networks (FNN) approximator and an adaptive robust controller type-compensator are utilized to relax the uncertainties problems of the robot control system. Moreover, the nonholonomic constraint force stability of the mobile manipulator robot is also considered by an adaptive control scheme. The design of online updating laws for the proposed controllers and FNN approximator are designed by applying the Lyapunov stability theorem. Thus, besides the improvement for tracking control performance, the stability of the proposed control system is also maintained. The effectiveness, robustness and adaptability of the proposed control strategy are verified by comparative numerical simulation results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid adaptive tracking control method for mobile manipulator robot based on Proportional–Integral–Derivative technique

Long

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…In [10,27], the LQR gains were softly automatically adjusted based on Model Reference Adaptive Controller (MRAC) or under variation of a hyperbolic function. In [28], an ant colony algorithm was employed to search for the optimal PID gains. Besides, several fuzzy-logic controllers have also been released [9,15,29].…”

Section: Introductionmentioning

confidence: 99%

“…Since the fuzzy-control mechanism is mostly based on experience of the operators, this approach could be limited in universal applicability. Another research direction of the intelligent nominal controller series is biologically inspired with Artificial Neural Network [24,30], Genetic Algorithm [31,32] and Bacterial Foraging Optimization Algorithm (BFOA), that mimic natural processes such as hybridization, reproduction, natural selection, mutation, etc., [19,28,[31][32][33][34]. In [35], the optimal LQR control performance was enhanced by intelligent control effect of an Adaline neural network, which was adopted for learning control behaviors of another linear controller based on a basic backpropagation adaptation method.…”

Section: Introductionmentioning

confidence: 99%

A LQR Neural Network Control Approach for Fast Stabilizing Rotary Inverted Pendulums

Nghi

Nhien

2021

Int. J. Precis. Eng. Manuf.

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Rotary inverted pendulum (RIP) is a well-known system that is commonly employed as an ideal benchmarking model for verifying linear and nonlinear control algorithms thanks to unique unstable and highly nonlinear natures. In this paper, an intelligent control method is developed for stabilizing such the RIP system in upright posture. The controller is structured from a linear quadratic regulator (LQR) and an online radial basis function (RBF) Neural-Network compensator. The LQR term plays a crucial role in yielding the nominal control signal based on a linearized model. Meanwhile, the neural control term is adopted to suppress the systematic deviation and external disturbances as the system is far from the equilibrium state. A damping segment-wise adaptation rule is proposed to activate the network operation. Stability of the closed-loop system is then proven by Lyapunov analyses. Effectiveness and feasibility of the advanced controller are confirmed throughout comparative simulation and real-time experiments.

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“…In 1992, another bioinspired optimization method based on swarm intelligence, namely Ant Colony Optimization (ACO), was founded. Since then, it has been successfully implemented [20]. The ant colony optimization ACO technique is another bio-inspired method that can be successfully implemented.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Piezoelectric Sensor-Actuator for Plate Vibration Control Using Evolutionary Computation: Modeling, Simulation and Experimentation

et al. 2021

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The development of lightweight, stronger, and more flexible structures has received the utmost interest from many researchers. For this reason, piezoelectric materials, with their inherent electromechanical coupling, have been widely incorporated in the development of such structures to attenuate their vibrations. However, one of the main challenges is to find the optimal control and sensor-actuator placement. This paper presents an active vibration control for flexible structures, whereby a simply supported plate is taken as the benchmark model. A feedback controller with a collocated sensor-actuator configuration is used. Both disturbance and control signal acting on the plate is created by using piezoelectric (PZT) patches. The analytical model is derived based on the Euler-Bernoulli model. Optimal location of the collocated sensoractuator, as well as PID controller gains, are determined using Ant Colony Optimization (ACO) technique, then compared with Genetic Algorithm (GA) and enumerative method (EM). Optimization in this paper is based on minimizing frequency average energy. The optimal performance value of piezoelectric patch sensoractuator position and PID controller gains are verified experimentally. It was found that PID controller gains and collocated sensor-actuator location optimizations using ACO, GA and enumerative methods give similar results, which implies the effectiveness of ACO as an optimization technique. More than 20 % of attenuation achieved using the available hardware setup.

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Optimal Nonlinear PID Control of a Micro-Robot Equipped with Vibratory Actuator Using Ant Colony Algorithm: Simulation and Experiment

Cited by 27 publications

References 26 publications

Hybrid adaptive tracking control method for mobile manipulator robot based on Proportional–Integral–Derivative technique

Hybrid adaptive tracking control method for mobile manipulator robot based on Proportional–Integral–Derivative technique

A LQR Neural Network Control Approach for Fast Stabilizing Rotary Inverted Pendulums

Optimization of Piezoelectric Sensor-Actuator for Plate Vibration Control Using Evolutionary Computation: Modeling, Simulation and Experimentation

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