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14 pages, 3040 KiB

Open AccessArticle

Vibration Suppression of a Flexible-Joint Robot Based on Parameter Identification and Fuzzy PID Control

by Jinyong Ju, Yongrui Zhao, Chunrui Zhang and Yufei Liu

Algorithms 2018, 11(11), 189; https://doi.org/10.3390/a11110189 - 20 Nov 2018

Cited by 21 | Viewed by 4560

In order to eliminate the influence of the joint torsional vibration on the system operation accuracy, the parameter identification and the elastic torsional vibration control of a flexible-joint robot are studied. Firstly, the flexible-joint robot system is equivalent to a rotor dynamic system, [...] Read more.

In order to eliminate the influence of the joint torsional vibration on the system operation accuracy, the parameter identification and the elastic torsional vibration control of a flexible-joint robot are studied. Firstly, the flexible-joint robot system is equivalent to a rotor dynamic system, in which the mass block and the torsion spring are used to simulate the system inertia link and elasticity link, for establishing the system dynamic model, and the experimental prototype is constructed. Then, based on the mechanism method, the global electromechanical-coupling dynamic model of the flexible-joint robot system is constructed to clear and define the mapping relationship between the driving voltage of the DC motor and the rotational speed of joint I and joint II. Furthermore, in view of the contradiction between the system response speed and the system overshoot in the vibration suppression effect of the conventional PID controller, a fuzzy PID controller, whose parameters are determined by the different requirements in the vibration control process, is designed to adjust the driving voltage of the DC motor for attenuating the system torsional vibration. Finally, simulation and control experiments are carried out and the results show that the designed fuzzy PID controller can effectively suppress the elastic torsional vibration of the flexible-joint robot system with synchronization optimization of control accuracy and dynamic quality. Full article

(This article belongs to the Special Issue Algorithms for PID Controller)

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13 pages, 3899 KiB

Open AccessArticle

Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control

by Panagiotis Kofinas and Anastasios I. Dounis

Algorithms 2018, 11(10), 148; https://doi.org/10.3390/a11100148 - 30 Sep 2018

Cited by 18 | Viewed by 5015

Abstract

This paper proposes a hybrid Zeigler-Nichols (Z-N) reinforcement learning approach for online tuning of the parameters of the Proportional Integral Derivative (PID) for controlling the speed of a DC motor. The PID gains are set by the Z-N method, and are then adapted [...] Read more.

This paper proposes a hybrid Zeigler-Nichols (Z-N) reinforcement learning approach for online tuning of the parameters of the Proportional Integral Derivative (PID) for controlling the speed of a DC motor. The PID gains are set by the Z-N method, and are then adapted online through the fuzzy Q-Learning agent. The fuzzy Q-Learning agent is used instead of the conventional Q-Learning, in order to deal with the continuous state-action space. The fuzzy Q-Learning agent defines its state according to the value of the error. The output signal of the agent consists of three output variables, in which each one defines the percentage change of each gain. Each gain can be increased or decreased from 0% to 50% of its initial value. Through this method, the gains of the controller are adjusted online via the interaction of the environment. The knowledge of the expert is not a necessity during the setup process. The simulation results highlight the performance of the proposed control strategy. After the exploration phase, the settling time is reduced in the steady states. In the transient states, the response has less amplitude oscillations and reaches the equilibrium point faster than the conventional PID controller. Full article

(This article belongs to the Special Issue Algorithms for PID Controller)

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19 pages, 2992 KiB

Open AccessArticle

Fast Tuning of the PID Controller in An HVAC System Using the Big Bang–Big Crunch Algorithm and FPGA Technology

by Abdoalnasir Almabrok, Mihalis Psarakis and Anastasios Dounis

Algorithms 2018, 11(10), 146; https://doi.org/10.3390/a11100146 - 28 Sep 2018

Cited by 38 | Viewed by 7929

Abstract

This article presents a novel technique for the fast tuning of the parameters of the proportional–integral–derivative (PID) controller of a second-order heat, ventilation, and air conditioning (HVAC) system. The HVAC systems vary greatly in size, control functions and the amount of consumed energy. [...] Read more.

This article presents a novel technique for the fast tuning of the parameters of the proportional–integral–derivative (PID) controller of a second-order heat, ventilation, and air conditioning (HVAC) system. The HVAC systems vary greatly in size, control functions and the amount of consumed energy. The optimal design and power efficiency of an HVAC system depend on how fast the integrated controller, e.g., PID controller, is adapted in the changes of the environmental conditions. In this paper, to achieve high tuning speed, we rely on a fast convergence evolution algorithm, called Big Bang–Big Crunch (BB–BC). The BB–BC algorithm is implemented, along with the PID controller, in an FPGA device, in order to further accelerate of the optimization process. The FPGA-in-the-loop (FIL) technique is used to connect the FPGA board (i.e., the PID and BB–BC subsystems) with the plant (i.e., MATLAB/Simulink models of HVAC) in order to emulate and evaluate the entire system. The experimental results demonstrate the efficiency of the proposed technique in terms of optimization accuracy and convergence speed compared with other optimization approaches for the tuning of the PID parameters: sw implementation of the BB–BC, genetic algorithm (GA), and particle swarm optimization (PSO). Full article

(This article belongs to the Special Issue Algorithms for PID Controller)

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18 pages, 3877 KiB

Open AccessArticle

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

by Natalia Alekseeva, Ivan Tanev and Katsunori Shimohara

Algorithms 2018, 11(7), 108; https://doi.org/10.3390/a11070108 - 21 Jul 2018

Cited by 3 | Viewed by 4732

Abstract

The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road [...] Read more.

The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions. Full article

(This article belongs to the Special Issue Algorithms for PID Controller)

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23 pages, 2958 KiB

Open AccessArticle

Performance Optimal PI controller Tuning Based on Integrating Plus Time Delay Models

by Christer Dalen and David Di Ruscio

Algorithms 2018, 11(6), 86; https://doi.org/10.3390/a11060086 - 17 Jun 2018

Cited by 13 | Viewed by 5218

Abstract

A method for tuning PI controller parameters, a prescribed maximum time delay error or a relative time delay error is presented. The method is based on integrator plus time delay models. The integral time constant is linear in the relative time delay error, [...] Read more.

A method for tuning PI controller parameters, a prescribed maximum time delay error or a relative time delay error is presented. The method is based on integrator plus time delay models. The integral time constant is linear in the relative time delay error, and the proportional constant is seen inversely proportional to the relative time delay error. The keystone in the method is the method product parameter, i.e., the product of the PI controller proportional constant, the integral time constant, and the integrator plus time delay model, velocity gain. The method product parameter is found to be constant for various PI controller tuning methods. Optimal suggestions are given for choosing the method product parameter, i.e., optimal such that the integrated absolute error or, more interestingly, the Pareto performance objective (i.e., integrated absolute error for combined step changes in output and input disturbances) is minimised. Variants of the presented tuning method are demonstrated for tuning PI controllers for motivated (possible) higher order process model examples, i.e., the presented method is combined with the model reduction step (process–reaction curve) in Ziegler–Nichols. Full article

(This article belongs to the Special Issue Algorithms for PID Controller)

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15 pages, 262 KiB

Open AccessArticle

A Randomized Algorithm for Optimal PID Controllers

by Yossi Peretz

Algorithms 2018, 11(6), 81; https://doi.org/10.3390/a11060081 - 5 Jun 2018

Cited by 9 | Viewed by 4018

Abstract

A randomized algorithm is suggested for the syntheses of optimal PID controllers for MIMO coupled systems, where the optimality is with respect to the

H_{\infty}

-norm, the

H_{2}

-norm and the LQR functional, with possible system-performance specifications defined by regional pole-placement. [...] Read more.

A randomized algorithm is suggested for the syntheses of optimal PID controllers for MIMO coupled systems, where the optimality is with respect to the

H_{\infty}

-norm, the

H_{2}

-norm and the LQR functional, with possible system-performance specifications defined by regional pole-placement. Other notions of optimality (e.g., mixed

H_{2} / H_{\infty}

design, controller norm or controller sparsity) can be handled similarly with the suggested algorithm. The suggested method is direct and thus can be applied to continuous-time systems as well as to discrete-time systems with the obvious minor changes. The presented algorithm is a randomized algorithm, which has a proof of convergence (in probability) to a global optimum. Full article

(This article belongs to the Special Issue Algorithms for PID Controller)

18 pages, 2071 KiB

Open AccessArticle

Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning

by Pengzhan Chen, Zhiqiang He, Chuanxi Chen and Jiahong Xu

Algorithms 2018, 11(5), 65; https://doi.org/10.3390/a11050065 - 5 May 2018

Cited by 40 | Viewed by 9232

Abstract

We developed a novel control strategy of speed servo systems based on deep reinforcement learning. The control parameters of speed servo systems are difficult to regulate for practical applications, and problems of moment disturbance and inertia mutation occur during the operation process. A [...] Read more.

We developed a novel control strategy of speed servo systems based on deep reinforcement learning. The control parameters of speed servo systems are difficult to regulate for practical applications, and problems of moment disturbance and inertia mutation occur during the operation process. A class of reinforcement learning agents for speed servo systems is designed based on the deep deterministic policy gradient algorithm. The agents are trained by a significant number of system data. After learning completion, they can automatically adjust the control parameters of servo systems and compensate for current online. Consequently, a servo system can always maintain good control performance. Numerous experiments are conducted to verify the proposed control strategy. Results show that the proposed method can achieve proportional–integral–derivative automatic tuning and effectively overcome the effects of inertia mutation and torque disturbance. Full article

(This article belongs to the Special Issue Algorithms for PID Controller)

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18 pages, 8876 KiB

Open AccessArticle

An Approach for Setting Parameters for Two-Degree-of-Freedom PID Controllers

by Xinxin Wang, Xiaoqiang Yan, Donghai Li and Li Sun

Algorithms 2018, 11(4), 48; https://doi.org/10.3390/a11040048 - 13 Apr 2018

Cited by 19 | Viewed by 5516

Abstract

In this paper, a new tuning method is proposed, based on the desired dynamics equation (DDE) and the generalized frequency method (GFM), for a two-degree-of-freedom proportional-integral-derivative (PID) controller. The DDE method builds a quantitative relationship between the performance and the two-degree-of-freedom PID controller [...] Read more.

In this paper, a new tuning method is proposed, based on the desired dynamics equation (DDE) and the generalized frequency method (GFM), for a two-degree-of-freedom proportional-integral-derivative (PID) controller. The DDE method builds a quantitative relationship between the performance and the two-degree-of-freedom PID controller parameters and guarantees the desired dynamic, but it cannot guarantee the stability margin. So, we have developed the proposed tuning method, which guarantees not only the desired dynamic but also the stability margin. Based on the DDE and the GFM, several simple formulas are deduced to calculate directly the controller parameters. In addition, it performs almost no overshooting setpoint response. Compared with Panagopoulos’ method, the proposed methodology is proven to be effective. Full article

(This article belongs to the Special Issue Algorithms for PID Controller)

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13 pages, 5309 KiB

Open AccessArticle

Optimization Design by Genetic Algorithm Controller for Trajectory Control of a 3-RRR Parallel Robot

by Lianchao Sheng and Wei Li

Algorithms 2018, 11(1), 7; https://doi.org/10.3390/a11010007 - 15 Jan 2018

Cited by 29 | Viewed by 8907

Abstract

In order to improve the control precision and robustness of the existing proportion integration differentiation (PID) controller of a 3-Revolute–Revolute–Revolute (3-RRR) parallel robot, a variable PID parameter controller optimized by a genetic algorithm controller is proposed in this paper. Firstly, the inverse kinematics [...] Read more.

In order to improve the control precision and robustness of the existing proportion integration differentiation (PID) controller of a 3-Revolute–Revolute–Revolute (3-RRR) parallel robot, a variable PID parameter controller optimized by a genetic algorithm controller is proposed in this paper. Firstly, the inverse kinematics model of the 3-RRR parallel robot was established according to the vector method, and the motor conversion matrix was deduced. Then, the error square integral was chosen as the fitness function, and the genetic algorithm controller was designed. Finally, the control precision of the new controller was verified through the simulation model of the 3-RRR planar parallel robot—built in SimMechanics—and the robustness of the new controller was verified by adding interference. The results show that compared with the traditional PID controller, the new controller designed in this paper has better control precision and robustness, which provides the basis for practical application. Full article

(This article belongs to the Special Issue Algorithms for PID Controller)

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