RBF Neural Network Control for Linear Motor-Direct Drive Actuator Based on an Extended State Observer

Hydraulic power and other kinds of disturbance in a linear motor-direct drive actuator (LM-DDA) have a great impact on the performance of the system. A mathematical model of the LM-DDA system is established and a double-loop control system is presented. An extended state observer (ESO) with switched gain was utilized to estimate the influence of the hydraulic power and other load disturbances. Meanwhile, Radial Basis Function (RBF) neural network was utilized to optimize the parameters in this intelligent controller. The results of the dynamic tests demonstrate the performance with rapid response and improved accuracy could be attained by the proposed control scheme

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Reinforcement learning control of a flexible two-link manipulator: an experimental investigation

This article discusses the control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs). A reinforcement learning (RL) control strategy is developed that is based on actor-critic structure to enable vibration suppression while retaining trajectory tracking. Subsequently, the closed-loop system with the proposed RL control algorithm is proved to be semi-global uniform ultimate bounded (SGUUB) by Lyapunov's direct method. In the simulations, the control approach presented has been tested on the discretized ODE dynamic model and the analytical claims have been justified under the existence of uncertainty. Eventually, a series of experiments in a Quanser laboratory platform are investigated to demonstrate the effectiveness of the presented control and its application effect is compared with PD control

ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator

This paper presents an inverse dynamic model estimation based on an artificial neural network of a complete new parallel robot manipulator prototype 6- PGK with six degrees of freedom, built at Petru Maior University of Tirgu-Mures. The model estimation of the parallel robot manipulator is performed with a feedforward artificial neural network. In the control engineering domain there are control structures that need the direct or inverse model of the process for ensuring the process control at the imposed performances. Usually, the determination of the direct/inverse mathematical model is a difficult or impossible task to be achieved. In these cases different non-parametric or parametric, off-line or on-line identification methods are used. A solution that may support the on-line parametric methods is represented by the feedforward artificial neural networks. By implementing feedforward artificial neural networks as a nonlinear autoregressive model with exogenous inputs, the authors investigate the possibility of choosing the optimum parameters that characterize the neural network so that it approximates as better as possible the model of the 6-PGK prototype robot. Finally an innovative algorithm is developed for obtaining the optimal configuration parameters set of the feedforward artificial neural network. The proposed algorithm helps in setting the optimal parameters of the neural network that offer high opportunities to provide satisfactory identification of the robot model. Experimental results obtained by a structure derived from the proposed solution demonstrate a good approximation related to the studied system, which is characterized by nonlinearities and high complexity

A Precise Neural-disturbance Learning Controller of Constrained Robotic Manipulators

An adaptive robust controller is introduced for high-precision tracking control problems of robotic manipulators with output constraints. A nonlinear function is employed to transform the constrained control objective to new free variables that are then synthesized using a sliding-mode-like function as an indirect control mission. A robust nonlinear control signal is derived to ensure the boundedness of the main control objective without violation of physical output constraints. The control performance is improved by adopting a neural-network model with conditioned nonlinear learning laws to deal with nonlinear uncertainties and disturbances inside the system dynamics. A disturbance-observer-based control signal is additionally properly injected into the neural nonlinear system to eliminate the approximation error for achieving asymptotically tracking control accuracy. Performance of the overall control system is validated by intensive theoretical proofs and comparative simulation results

Applications of Mathematical Models in Engineering

The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools

Design and implementation of a soft computing-based controller for a complex mechanical system

Soft-Computing basierende Regler beinhalten Algorithmen, die im Bereich des Maschinellen Lernens einzuordnen sind. Diese Regler sind in der Lage eine geeignete Steuerungsstrategie durch direkte Interaktion mit einer dynamischen Regelstrecke zu entwerfen. Sowohl klassische als auch moderne Reglerentwurfsmethoden hangen von der Genauigkeit des verwendeten dynamischen Systemmodells ab, was insbesondere bei steigender Komplexitat des Systems und auftretenden Modellunsicherheiten nicht mehr uneingeschrankt gewahrleistet werden kann. Die Ziele von Soft- Computing basierenden Reglern sind die Verbesserung der Gute des Regelverhaltens und eine geeignete Anpassung der Regler ohne eine mathematische Modellbildung auf Grundlage von physikalischen Gesetzen. Im Rahmen dieser Arbeit werden funf Algorithmen zur Modellbildung und Regelung dynamischer Systeme untersucht, welche auf dem Mehrschichten-Perzeptron-Netzwerk (Multi-Layer Perceptron network, MLP), auf der Methode der Support Vector Machine (SVM), der Gau-Prozesse, der radialen Basisfunktionen (Radial Basis Functions, RBF) sowie der Fuzzy-Inferenz-Systeme basieren. Im Anschluss an die Darstellung der zugrunde liegenden mathematischen Zusammenhange dieser Methoden sowie deren Hauptanwendungsfelder im Bereich der Modellbildung und Regelung dynamischer Systeme wird eine systematische Evaluierung der funf Methoden diskutiert. Anhand der Verwendung quantitativer Gutekennziern werden diese Methoden fur die Verwendung in der Modellbildung und Regelung dynamischer Systeme vergleichbar gegenubergestellt. Basierend auf den Ergebnissen der Evaluierung wird der SVM-basierte Algorithmus als Kernalgorithmus des Soft-Computing basierenden Reglers verwendet. Der vorgestellte Regler besteht aus zwei Hauptteilen, wobei der erste Teil aus einer Modellfunktion der dynamischen Regelstrecke und einem SVM-basierten Beobachter besteht, und der zweite Teil basierend auf dem Systemmodell eine geeignete Regelstrategie generiert. Die Verikation des SVM-basierten Regleralgorithmus erfolgt anhand eines FEM-Modells eines dynamischen elastischen Balken bzw. einseitig eingespannten elastischen Balkens. Dieses Modell kann z. B. als Ersatzmodell fur das mechanische Verhalten eines exiblen Roboterarms oder einer Flugzeugtrag ache verwendet werden. Der Hauptteil der Modellfunktion besteht aus einem automatischen Systemidentikationsalgorithmus, der auch die Integration eines systematischen Modellbildungsansatzes fur dynamische Systeme ermoglicht.Die Ergebnisse des SVM-basierten Beobachter zeigen ahnliches Verhalten zum Kalman- Bucy Beobachter. Auch die Sensitivitatsanalyse der Parameter zeigt eine bessere Gute der SVM-basierten Beobachter im Vergleich mit den Kalman-Bucy Beobachtern. Im Anschluss wird der SVM-basierte Regler zur Schwingungsregelung des Kragtragers verwendet. Hierbei werden vergleichbare Ergebnisse zum LQR-Regler erzielt. Eine experimentelle Validierung des SVM basierten Reglers erfolgt an Versuchsst anden eines elastischen Biegebalkens sowie eines invertierten Biegebalkens. Die Zustandsbeobachtung fuhrt zu vergleichbaren Ergebnissen verglichen mit einem Kalman-Bucy Beobachter. Auch die Modellbildung des elastischen Balkens fuhrt zu guten Ubereinstimmungen. Die Regelgute des Soft-Computing basierenden Reglers wurde am Versuchsstand des invertierten Biegebalkens experimentell erprobt. Es wird deutlich, dass Ergebnisse im Rahmen der erforderlichen Vorgaben erzielt werden konnen.The focus of this thesis is to obtain a soft computing-based controller for complex mechanical system. soft computing based controllers are based on machine learning algorithm that able to develop suitable control strategies by direct interaction with targeted dynamic systems. Classical and modern control design methods depend on the accuracy of the system dynamic model which cannot be achieved due to the dynamic system complexity and modeling uncertainties. A soft computing-based controller aims to improve the performance of the close loop system and to give the controller adaptation ability as well as to reduce the need for mathematical modeling based on physical laws. In this work ve dierent softcomputing algorithms used in the eld of modeling and controlling dynamic systems are investigated.These algorithms are Multi-Layer Perceptron(MLP) network, Support Vector Machine (SVM),Gaussian process, Radial Basis Function (RBF), and Fuzzy Inference System (FIS). The basic mathematical description of each algorithm is given. Additionally, the most recent applications in modeling and controlling of dynamic system are summarized. A systematic evaluation of the ve algorithms is proposed. The goal of the evaluation is to provide quantitative measure of the performance of soft computing algorithms when used in modeling and controlling a dynamic system. Based on the evaluation, the SVM algorithm is selected as the core learning algorithm for the soft computing based controller. The controller has two main units. The rst unit has two functions of modeling dynamic system and obtaining a SVM-based observer. The second unit is in charge of generating suitable control strategy based on the dynamic model obtained. The verication of the controller using SVM algorithm is done using an elastic cantilever beam modeled using Finite Element Method (FEM). An elastic cantilever beam can be considered as a representation of exible single-link manipulator or aircraft wing. In the core of the modeling unit, an automatic system identication algorithm which allows a systematic modeling approach of dynamic systems is implemented. The results show that the system dynamic model using SVM algorithm is accurate with respect to the FEM model. As for the SVM-based observer the results show that it has good estimation in comparison with to dierent Kalman-Bucy observers. The sensitivity to parameters variations analysis shows that the SVM-based observer has better performance than Kalman-Bucy observer. The SVM based controller is used to control the vibration of the cantilever beam; the results show that the model reference controller using SVM has a similar performance to LQR controller. The validation of the controller using SVM algorithm is carried out using the elastic cantilever beam test rig and the inverted cantilever beam test rig. The states estimation using SVM-based observer of the elastic cantilever beam test rig is successful and accurate compared to a Kalman-Bucy observer. Modeling of the elastic cantilever beam using the SVM algorithm shows good accuracy. The performance of controller is tested on the inverted cantilever beam test rig. The results show that required performance objective can be realized using this control strategy

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

Advanced control designs for output tracking of hydrostatic transmissions

The work addresses simple but efficient model descriptions in a combination with advanced control and estimation approaches to achieve an accurate tracking of the desired trajectories. The proposed control designs are capable of fully exploiting the wide operation range of HSTs within the system configuration limits. A new trajectory planning scheme for the output tracking that uses both the primary and secondary control inputs was developed. Simple models or even purely data-driven models are envisaged and deployed to develop several advanced control approaches for HST systems