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4 pages, 204 KiB

Open AccessEditorial

New Trends in the Control of Robots and Mechatronic Systems

by Luca Bruzzone

Appl. Sci. 2023, 13(5), 3112; https://doi.org/10.3390/app13053112 - 28 Feb 2023

Cited by 2 | Viewed by 1047

Abstract

In recent years, research into the control of robotic and mechatronic systems has led to a wide variety of advanced paradigms and techniques, which have been extensively analysed and discussed in the scientific literature [...] Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

Research

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16 pages, 3497 KiB

Open AccessArticle

Numerical Simulation of Time-Optimal Path Planning for Autonomous Underwater Vehicles Using a Markov Decision Process Method

by Mingrui Shu, Xiuyu Zheng, Fengguo Li, Kaiyong Wang and Qiang Li

Appl. Sci. 2022, 12(6), 3064; https://doi.org/10.3390/app12063064 - 17 Mar 2022

Cited by 4 | Viewed by 1944

Abstract

Many path planning algorithms developed for land or air based autonomous vehicles no longer apply under the water. A time-optimal path planning method for autonomous underwater vehicles (AUVs), based on a Markov decision process (MDP) algorithm, is proposed for the marine environment. Its [...] Read more.

Many path planning algorithms developed for land or air based autonomous vehicles no longer apply under the water. A time-optimal path planning method for autonomous underwater vehicles (AUVs), based on a Markov decision process (MDP) algorithm, is proposed for the marine environment. Its performance is examined for different oceanic conditions, including complex coastal bathymetry and time-varying ocean currents, revealing advantages compared to the A* algorithm, a traditional path planning method. The ocean current is predicted using a regional ocean model and then provided to the MDP algorithm as a priori. A computation-efficient and feature-resolved spatial resolution are determined through a series of sensitivity experiments. The simulations demonstrate the importance to incorporate ocean currents in the path planning of AUVs in the real ocean. The MDP algorithm remains robust even if the ocean current is complex. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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20 pages, 1729 KiB

Open AccessArticle

Learning Human Strategies for Tuning Cavity Filters with Continuous Reinforcement Learning

by Zhiyang Wang and Yongsheng Ou

Appl. Sci. 2022, 12(5), 2409; https://doi.org/10.3390/app12052409 - 25 Feb 2022

Cited by 5 | Viewed by 2662

Abstract

Learning to master human intentions and behave more humanlike is an ultimate goal for autonomous agents. To achieve that, higher requirements for intelligence are imposed. In this work, we make an effort to study the autonomous learning mechanism to solve complicated human tasks. [...] Read more.

Learning to master human intentions and behave more humanlike is an ultimate goal for autonomous agents. To achieve that, higher requirements for intelligence are imposed. In this work, we make an effort to study the autonomous learning mechanism to solve complicated human tasks. The tuning task of cavity filters is studied, which is a common task in the communication industry. It is not only time-consuming, but also depends on the knowledge of tuning technicians. We propose an automatic tuning framework for cavity filters based on Deep Deterministic Policy Gradient and design appropriate reward functions to accelerate training. Simulation experiments are carried out to verify the applicability of the algorithm. This method can not only automatically tune the detuned filter from random starting position to meet the design requirements under certain circumstances, but also realize the transfer of learning skills to new situations, to a certain extent. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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17 pages, 22864 KiB

Open AccessArticle

Staircase Detection, Characterization and Approach Pipeline for Search and Rescue Robots

by José Armando Sánchez-Rojas, José Aníbal Arias-Aguilar, Hiroshi Takemura and Alberto Elías Petrilli-Barceló

Appl. Sci. 2021, 11(22), 10736; https://doi.org/10.3390/app112210736 - 14 Nov 2021

Cited by 7 | Viewed by 3330

Abstract

Currently, most rescue robots are mainly teleoperated and integrate some level of autonomy to reduce the operator’s workload, allowing them to focus on the primary mission tasks. One of the main causes of mission failure are human errors and increasing the robot’s autonomy [...] Read more.

Currently, most rescue robots are mainly teleoperated and integrate some level of autonomy to reduce the operator’s workload, allowing them to focus on the primary mission tasks. One of the main causes of mission failure are human errors and increasing the robot’s autonomy can increase the probability of success. For this reason, in this work, a stair detection and characterization pipeline is presented. The pipeline is tested on a differential drive robot using the ROS middleware, YOLOv4-tiny and a region growing based clustering algorithm. The pipeline’s staircase detector was implemented using the Neural Compute Engines (NCEs) of the OpenCV AI Kit with Depth (OAK-D) RGB-D camera, which allowed the implementation using the robot’s computer without a GPU and, thus, could be implemented in similar robots to increase autonomy. Furthermore, by using this pipeline we were able to implement a Fuzzy controller that allows the robot to align itself, autonomously, with the staircase. Our work can be used in different robots running the ROS middleware and can increase autonomy, allowing the operator to focus on the primary mission tasks. Furthermore, due to the design of the pipeline, it can be used with different types of RGB-D cameras, including those that generate noisy point clouds from low disparity depth images. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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21 pages, 9555 KiB

Open AccessArticle

Analysis of Inductive Displacement Sensors with Large Range and Nanoscale Resolution

by Qiang He, Shixun Fan, Ning Chen, Ruoyu Tan, Fan Chen and Dapeng Fan

Appl. Sci. 2021, 11(21), 10134; https://doi.org/10.3390/app112110134 - 28 Oct 2021

Cited by 10 | Viewed by 2679

Abstract

With the advantages of high resolution, structural simplicity, reliability, compact size, and high sensitivity, inductive sensors have been widely used in nanopositioning systems. However, the measuring range of traditional inductive sensors are usually limited to 0.2 mm. A novel analysis and design methodology [...] Read more.

With the advantages of high resolution, structural simplicity, reliability, compact size, and high sensitivity, inductive sensors have been widely used in nanopositioning systems. However, the measuring range of traditional inductive sensors are usually limited to 0.2 mm. A novel analysis and design methodology of the miniaturized inductive sensor with large measuring range and nanoscale resolution is proposed. Firstly, an accurate leakage inductance model is established. Secondly, a design rule of armature size is proposed by considering the fringing effect. Then, the error terms introduced by the measurement circuit of differential inductive sensors are analyzed and the corresponding error suppression methods are illustrated. Moreover, A design rule of selecting the optimal excitation frequency is proposed to meet the requirements of high Q value and high bandwidth, and to minimize the impact of core loss resistance on the performance of the sensor. Validated by the experiments, the proposed analysis and design method can effectively guide the design of the miniaturized inductive sensor with nanoscale resolution in the measuring range of ±0.5 mm. The overall size of the fabricated sensor prototypes is less than 6 mm × 6 mm × 3 mm. Combined with large range, high resolution and ideal miniaturization, this inductive sensor can be well suitable for compact and large stroke nanopositioning systems. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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15 pages, 1248 KiB

Open AccessArticle

Distributed Control for Coordinated Tracking of Fixed-Wing Unmanned Aerial Vehicles under Model Uncertainty and Disturbances

by Qipeng Wang, Shulong Zhao and Xiangke Wang

Appl. Sci. 2021, 11(21), 9830; https://doi.org/10.3390/app11219830 - 21 Oct 2021

Cited by 3 | Viewed by 1768

Abstract

In this paper, we consider a control problem where a group of fixed-wing unmanned aerial vehicles (UAVs) with uncertain dynamics tracks the target vehicle cooperatively in the case of external disturbance. Based on the Gaussian process regression, a data-driven model is established, whose [...] Read more.

In this paper, we consider a control problem where a group of fixed-wing unmanned aerial vehicles (UAVs) with uncertain dynamics tracks the target vehicle cooperatively in the case of external disturbance. Based on the Gaussian process regression, a data-driven model is established, whose uniform error is bounded with probability. Then a learning-based consensus protocol for multi-UAVs is designed. The stability of the system is proven via Lyapunov function, and the tracking error is guaranteed to be bounded with a high probability. Finally, the effectiveness of the proposed method is shown in the numerical simulation. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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25 pages, 1816 KiB

Open AccessArticle

Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control

by Ipsita Sengupta, Sagar Gupta, Dipankar Deb and Stepan Ozana

Appl. Sci. 2021, 11(20), 9766; https://doi.org/10.3390/app11209766 - 19 Oct 2021

Cited by 9 | Viewed by 3959

Abstract

This paper presents the simulation and calculation-based aspect of constructing a dynamically stable, self-balancing electric monowheel from first principles. It further goes on to formulate a reference model-based adaptive control structure in order to maintain balance as well as the desired output. First, [...] Read more.

This paper presents the simulation and calculation-based aspect of constructing a dynamically stable, self-balancing electric monowheel from first principles. It further goes on to formulate a reference model-based adaptive control structure in order to maintain balance as well as the desired output. First, a mathematical model of the nonlinear system analyzes the vehicle dynamics, followed by an appropriate linearization technique. Suitable parameters for real-time vehicle design are calculated based on specific constraints followed by a proper motor selection. Various control methods are tested and implemented on the state-space model of this system. Initially, classical pole placement control is carried out in MATLAB to observe the responses. The LQR control method is also implemented in MATLAB and Simulink, demonstrating the dynamic stability and self-balancing system property. Subsequently, the system considers an extensive range of rider masses and external disturbances by introducing white noise. The parameter estimation of rider position has been implemented using Kalman Filter estimation, followed by developing an LQG controller for the system, in order to mitigate the disturbances caused by factors such as wind. A comparison between LQR and LQG controllers has been conducted. Finally, a reference model-assisted adaptive control structure has been established for the system to account for sudden parameter changes such as rider mass. A reference model stabilizer has been established for the same purpose, and all results have been obtained by running simulations on MATLAB Simulink. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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20 pages, 1604 KiB

Open AccessArticle

Kalman Filter and Variants for Estimation in 2DOF Serial Flexible Link and Joint Using Fractional Order PID Controller

by Sagar Gupta, Abhaya Pal Singh, Dipankar Deb and Stepan Ozana

Appl. Sci. 2021, 11(15), 6693; https://doi.org/10.3390/app11156693 - 21 Jul 2021

Cited by 12 | Viewed by 2552

Abstract

Robotic manipulators have been widely used in industries, mainly to move tools into different specific positions. Thus, it has become necessary to have accurate knowledge about the tool position using forward kinematics after accessing the angular locations of limbs. This paper presents a [...] Read more.

Robotic manipulators have been widely used in industries, mainly to move tools into different specific positions. Thus, it has become necessary to have accurate knowledge about the tool position using forward kinematics after accessing the angular locations of limbs. This paper presents a simulation study in which an encoder attached to the limbs gathers information about the angular positions. The measured angles are applied to the Kalman Filter (KF) and its variants for state estimation. This work focuses on the use of fractional order controllers with a Two Degree of Freedom Serial Flexible Links (2DSFL) and Two Degree of Freedom Serial Flexible Joint (2DSFJ) and undertakes simulations with noise and a square wave as input. The fractional order controllers fit better with the system properties than integer order controllers. The KF and its variants use an unknown and assumed process and measurement noise matrices to predict the actual data. An optimisation problem is proposed to achieve reasonable estimations with the updated covariance matrices. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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22 pages, 4461 KiB

Open AccessArticle

Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios

by Hiroshi Uchihori, Luca Cavanini, Mitsuhiko Tasaki, Pawel Majecki, Yusuke Yashiro, Michael J. Grimble, Ikuo Yamamoto, Gerrit M. van der Molen, Akihiro Morinaga and Kazuki Eguchi

Appl. Sci. 2021, 11(10), 4368; https://doi.org/10.3390/app11104368 - 11 May 2021

Cited by 18 | Viewed by 2765

Abstract

A control system for driving an Autonomous Underwater Vehicle (AUV) performing docking operations in presence of tidal current disturbances is proposed. The nonlinear model of the vehicle has been modelled in a Linear Parameter-Varying (LPV) form. This is suitable for the design of [...] Read more.

A control system for driving an Autonomous Underwater Vehicle (AUV) performing docking operations in presence of tidal current disturbances is proposed. The nonlinear model of the vehicle has been modelled in a Linear Parameter-Varying (LPV) form. This is suitable for the design of the control system using a model-based approach. The LPV model was used for a Model Predictive Control (MPC) design for computing the set of forces and moments driving the nonlinear vehicle model. The LPV-MPC control action is mapped into the reference signals for the actuators by using a Thrust Allocation (TA) algorithm. This was based on the nonlinear models for the actuators and their position and orientation on the vehicle’s hull. The structural decomposition of MPC and TA reduces the computational burden involved in computing the control law on-line on an embedded control board. Both MPC and TA algorithms use the vehicle’s linear and angular positions, and velocities that are estimated by an LPV based Kalman Filter (KF). The proposed control system has been tested in different docking scenarios using various tidal current disturbances acting on the vehicle as an unmeasured disturbance. The simulation results show the controller is effective in controlling the AUV over the range of control scenarios meeting the constraints and specifications. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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13 pages, 3365 KiB

Open AccessArticle

Automatic Electromechanical Perturbator for Postural Control Analysis Based on Model Predictive Control

by Daniel Pacheco Quiñones, Maria Paterna and Carlo De Benedictis

Appl. Sci. 2021, 11(9), 4090; https://doi.org/10.3390/app11094090 - 29 Apr 2021

Cited by 5 | Viewed by 2254

Abstract

Objective clinical analyses are required to evaluate balance control performance. To this outcome, it is relevant to study experimental protocols and to develop devices that can provide reliable information about the ability of a subject to maintain balance. Whereas most of the applications [...] Read more.

Objective clinical analyses are required to evaluate balance control performance. To this outcome, it is relevant to study experimental protocols and to develop devices that can provide reliable information about the ability of a subject to maintain balance. Whereas most of the applications available in the literature and on the market involve shifting and tilting of the base of support, the system presented in this paper is based on the direct application of an impulsive (short-lasting) force by means of an electromechanical device (named automatic perturbator). The control of such stimulation is rather complex since it requires high dynamics and accuracy. Moreover, the occurrence of several non-linearities, mainly related to the human–machine interaction, signals the necessity for robust control in order to achieve the essential repeatability and reliability. A linear electric motor, in combination with Model Predictive Control, was used to develop an automatic perturbator prototype. A test bench, supported by model simulations, was developed to test the architecture of the perturbation device. The performance of the control logic has been optimized by iterative tuning of the controller parameters, and the resulting behavior of the automatic perturbator is presented. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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21 pages, 5964 KiB

Open AccessArticle

Fractional-Order PII^1/2DD^1/2 Control: Theoretical Aspects and Application to a Mechatronic Axis

by Luca Bruzzone, Mario Baggetta and Pietro Fanghella

Appl. Sci. 2021, 11(8), 3631; https://doi.org/10.3390/app11083631 - 17 Apr 2021

Cited by 10 | Viewed by 2909

Abstract

Fractional Calculus is usually applied to control systems by means of the well-known PI^λD^μ scheme, which adopts integral and derivative components of non-integer orders λ and µ. An alternative approach is to add equally distributed fractional-order terms to the PID [...] Read more.

Fractional Calculus is usually applied to control systems by means of the well-known PI^λD^μ scheme, which adopts integral and derivative components of non-integer orders λ and µ. An alternative approach is to add equally distributed fractional-order terms to the PID scheme instead of replacing the integer-order terms (Distributed Order PID, DOPID). This work analyzes the properties of the DOPID scheme with five terms, that is the PII^1/2DD^1/2 (the half-integral and the half-derivative components are added to the classical PID). The frequency domain responses of the PID, PI^λD^μ and PII^1/2DD^1/2 controllers are compared, then stability features of the PII^1/2DD^1/2 controller are discussed. A Bode plot-based tuning method for the PII^1/2DD^1/2 controller is proposed and then applied to the position control of a mechatronic axis. The closed-loop behaviours of PID and PII^1/2DD^1/2 are compared by simulation and by experimental tests. The results show that the PII^1/2DD^1/2 scheme with the proposed tuning criterium allows remarkable reduction in the position error with respect to the PID, with a similar control effort and maximum torque. For the considered mechatronic axis and trapezoidal speed law, the reduction in maximum tracking error is −71% and the reduction in mean tracking error is −77%, in correspondence to a limited increase in maximum torque (+5%) and in control effort (+4%). Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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16 pages, 1071 KiB

Open AccessArticle

Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

by Davide Grande, Catherine A. Harris, Giles Thomas and Enrico Anderlini

Appl. Sci. 2021, 11(4), 1829; https://doi.org/10.3390/app11041829 - 19 Feb 2021

Cited by 4 | Viewed by 2406

Abstract

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are [...] Read more.

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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17 pages, 10043 KiB

Open AccessArticle

Fast Fractional-Order Terminal Sliding Mode Control for Seven-Axis Robot Manipulator

by Jie Wang, Min Cheol Lee, Jae Hyung Kim and Hyun Hee Kim

Appl. Sci. 2020, 10(21), 7757; https://doi.org/10.3390/app10217757 - 2 Nov 2020

Cited by 10 | Viewed by 2091

Abstract

This paper proposes a novel controller, fast fractional-order terminal sliding mode control (FFOTSMC), for a seven-degree-of-freedom (7-DOF) robot manipulator with tracking control. The new controller applies the fractional-order derivative on both the sliding surface design and the sliding control/reaching law. Compared to previous [...] Read more.

This paper proposes a novel controller, fast fractional-order terminal sliding mode control (FFOTSMC), for a seven-degree-of-freedom (7-DOF) robot manipulator with tracking control. The new controller applies the fractional-order derivative on both the sliding surface design and the sliding control/reaching law. Compared to previous research, which only applies the fractional-order derivative on the sliding surface design, the proposed controller has a faster convergence for reaching the sliding surface and maintaining stay on it because of the new fractional-order control law, which helps the tracking accuracy. To implement the controller on the robot with less chattering, a sliding perturbation observer (SPO) is used to estimate the disturbance and uncertainties. Stability analysis is analyzed using Lyapunov functions for fractional-order systems. The controller performance is evaluated by a simulation of a single-input and single-output (SISO) system in MATLAB Simulink and experiments on the robot manipulator. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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18 pages, 3147 KiB

Open AccessArticle

State-Constrained Sub-Optimal Tracking Controller for Continuous-Time Linear Time-Invariant (CT-LTI) Systems and Its Application for DC Motor Servo Systems

by Jihwan Kim, Ung Jon and Hyeongcheol Lee

Appl. Sci. 2020, 10(16), 5724; https://doi.org/10.3390/app10165724 - 18 Aug 2020

Cited by 4 | Viewed by 2706

Abstract

In this paper, we propose an analytic solution of state-constrained optimal tracking control problems for continuous-time linear time-invariant (CT-LTI) systems that are based on model-based prediction, the quadratic penalty function, and the variational approach. Model-based prediction is a concept taken from model-predictive control [...] Read more.

In this paper, we propose an analytic solution of state-constrained optimal tracking control problems for continuous-time linear time-invariant (CT-LTI) systems that are based on model-based prediction, the quadratic penalty function, and the variational approach. Model-based prediction is a concept taken from model-predictive control (MPC) and this is essential to change the direction of calculation for the solution from backward to forward. The quadratic penalty function plays an important role in deriving the analytic solution since it can transform the problem into a form that does not have inequality constraints. For computational convenience, we also propose a sub-optimal controller derived from the steady-state approximation of the analytic solution and show that the proposed controller satisfies the Lyapunov stability. The main advantage of the proposed controller is that it can be implemented in real time with a lower computational load compared to the implicit MPC. Finally, the simulation results for a DC motor servo system are shown and compared with the results of the direct multi-shooting method and the implicit MPC to verify the effectiveness of the proposed controller. Full article

(This article belongs to the Special Issue New Trends in the Control of Robots and Mechatronic Systems)

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