Development Of Control Algorithms Research Articles

Adaptive consensus control for output-constrained nonlinear multi-agent systems with actuator faults

This paper addresses the problem of leader-follower consensus fault-tolerant control for a class of nonlinear multi-agent systems with output constraints. Specifically, a new nonlinear state transformation function is proposed to deal with the asymmetric constraint on output. Moreover, by integrating backstepping and radial basis function neural network approaches, an adaptive consensus control framework is developed with a single parameter estimator, which mitigates the computation of control algorithm in comparison with conventional adaptive approximation based control techniques. Then an adaptive compensation method is proposed to eliminate the effect of actuator failure. Under the proposed control scheme, all the closed-loop signals of the systems are bounded and the consensus tracking error converges to an adjustable small neighborhood of zero. To evaluate the developed control algorithm, a group of four networked two-stage chemical reactors is used to illustrate the effectiveness of the theoretic results obtained.

Hybrid Antagonistic System With Coiled Shape Memory Alloy and Twisted and Coiled Polymer Actuator for Lightweight Robotic Arm

Artificial muscles have been developed for various input power sources. Among them, thermally driven actuators can be easily actuated at a low voltage without an additional device using the Joule heating. Therefore, these actuators can be considered suitable for developing lightweight robots. Shape memory alloy or twisted and coiled actuators are typically used as both have the advantage of large specific power; however, their disadvantages include speed, hysteresis, and initial tension. In this letter, we developed a hybrid antagonistic system that alleviates the drawbacks of both actuators. Control methods for hysteresis are applied based on actuator models and controlling the cooling fan. The joint position control experiment was conducted, and the advantages of the two actuators could be utilized based on the results. Furthermore, a lightweight robot arm was developed using the hybrid system. By applying the hybrid system, the robotic arm could be fabricated with a light weight of about 1 kg and can move in 4 degrees of freedom. The robotic arm was possible to perform angle control of the elbow with the developed control algorithm. In addition, it was combined with a 3 degrees of freedom orthogonal joint to replace the role of the shoulder, and the robotic arm was able to demonstrate the motion of activities of daily living.

Longitudinal Driving Force Distribution of Four In-wheel Motors Drive Electric Vehicle Based on Fuzzy Logic

In order to improve the vehicle economy, this paper establishes the drive system efficiency optimization objective function and vehicle longitudinal driving constraints, design the drive system efficiency optimization control algorithm based on tolerance. Then establish the objective function of longitudinal adhesion coefficient utilization optimization algorithm, complete the development of longitudinal adhesion coefficient utilization optimization control algorithm to improve the vehicle stability. In order to give consideration to the economy and stability of vehicle longitudinal driving, the switching logic of longitudinal driving force distribution algorithm is designed. Fuzzy control is used in the switching logic to realize the smooth switching of longitudinal driving force in the transition process, and realize the optimal distribution of longitudinal driving force. Finally, compare the simulation to the typical control algorithm under multiple working conditions to comfirm the actively effect of the control algorithm.

АЛГОРИТМЫ УПРАВЛЕНИЯ ЭЛЕКТРИЧЕСКОЙ СЕТЬЮ ГИБРИДНОЙ СИСТЕМЫ ЭНЕРГООБЕСПЕЧЕНИЯ АНПА

The aim of the research was to control the electrical network of a hybrid power supply systemfor an autonomous underwater vehicle designed to travel over ultra-long distances over tensof thousands of kilometers. To overcome ultra-long distances, the urgent task is to minimize thespecific consumption of electricity, provided that all consumers are provided with electricity. Therelevance of the work is determined by the novelty of using a hybrid power supply system in autonomousunmanned underwater vehicles, consisting of heterogeneous sources of electricity operatingon different physical principles. Due to the lack of research to date, related to the control ofthe hybrid power supply system, coordinated with the modes of motion of the vehicle in a wide range of speeds, the problem arose of developing control algorithms for the hybrid power supplysystem. To solve the problem, the reasons for the change in current consumption during themovement of the device were analyzed, the necessary conditions for connecting consumers to thebus ducts were formed, including providing all consumers with electricity in full, excluding theexcess of the rated currents of each bus duct with consumption currents, minimizing electricitylosses when passing through the conductor and through the equipment. In this regard, the possibleconfigurations of the construction of the electrical network using conductors and equipment wereanalyzed, and losses on the current conductors and on the equipment used were estimated. Basedon the results of the research, a graph of consumers' connections to the conductors was formed,and to determine the way of connecting each consumer to the energy source through the powergrid, a connection path was determined that minimizes losses. The problem was formalized asfinding the shortest path in a graph, and Dijkstra's algorithm was used as a basis to solve it. Basedon the research results, algorithms were formed for the formation of ways to connect consumers toelectricity sources through the power grid and an algorithm for controlling the switching of keysin the power grid when the consumption currents change. The developed algorithms were implementedin software, and a numerical experiment was carried out using a simulation model. Theresults of the experiment showed the correctness of the developed algorithms, and can be furtherused for implementation in the devices under development for moving over ultra-long distances.

Experimental evaluation for fluxes, currents and speed estimation of induction motor

Induction motor drive control and estimation is a wide subject. The market for variable speed drives has grown dramatically in the last few years. Manufacturers have recognized the importance of not only managing the speed or torque range, but also reducing power consumption. This necessitates the development of new control algorithms and schemes to include these solutions. Indeed, the speed estimate must be employed in one or more regions of the control scheme, depending on the control objective. This concept, as well as the most common speed estimation methodologies, is investigated. Currently, many tools can be used for the evaluation of the rotor speed without a speed sensor. By modern signal processing methods, it is possible to implement an estimation scheme with the possibility of monitoring currents and voltages. Therefore, in this paper, the concept of currents, speed and fluxes estimation based on the extended Kalman filter is proposed. By monitoring the ratio of the theoretical residual to the actual residual, the measured noise covariance matrix is recursively corrected online to make it gradually approach the real noise level. So that the filter performs the optimal estimation, improves the accuracy of the speed estimation. The effect of the load change on the currents, fluxes and speed estimation was also studied. Simulation and experimental results show that the proposed improved adaptive extended Kalman estimator has a strong ability to suppress random measurement noise. The experimental and simulation results prove the accuracy of the proposed scheme towards the state estimation of an induction motor at different load levels. It can accurately estimate the speed of the motor and has a good anti-error ability to meet the actual needs of the project.

Development of a method of multi-criteria evaluation under uncertainty

The object of research is decision-making support systems. Local wars and armed conflicts of recent decades are characterized by high dynamics of operations (combat operations) and a significant amount of diverse information circulating in information systems. These features determine the search for new approaches to increase the efficiency of decision-making support systems, given their reliability. This article solves the problem of developing a method of multicriteria evaluation in conditions of uncertainty. In the course of the research, the authors used the main provisions of the theory of artificial intelligence, automation theory, theory of complex technical systems and general scientific methods of cognition, namely analysis and synthesis. The proposed methodology was developed taking into account the practical experience of the authors of this work during the military conflicts of the last decade. The method of multicriteria evaluation is universal and can be used to assess the state of the objects of analysis of any architecture. The results of the research will be useful in: – development of new control algorithms in decision-making support systems; – substantiation of recommendations for improving the efficiency of operational management; – analysis of analysis (monitoring) objects in the course of hostilities (operations); – creation of perspective technologies to increase the efficiency of operational management; – assessment of the adequacy, reliability, sensitivity of the scientific and methodological apparatus of operational management in decision-making support systems; – development of new and improvement of existing management models. Areas of further research will be aimed at developing a methodology for intelligent management in special-purpose decision-making support systems.

Neuroadaptive control of information-poor servomechanisms with smooth and nonsmooth uncertainties

For motion control of uncertain servomechanisms, nonlinear dynamics including smooth and nonsmooth types, external disturbances, signal measurement noises, asymmetric input saturation, and so on seriously hinder the further development of high-performance closed-loop control algorithms. However, already existing control strategies cannot address the above-mentioned issues at the same time. It greatly increases the difficulty of controller design especially when some states are not measurable. Inspired by above motivations, this paper exploits neural networks to deal with nonlinear dynamics including discontinuous types, and combines extended state observers to estimate disturbances and unmeasurable states for uncertain nonlinear servomechanisms. Meanwhile, the desired-command-based model compensation approach is integrated into the controller design. It is worth noting that the neural network weights are updated by the combination of the estimation error and tracking error to acquire better approximation accuracy. According to above technologies, a novel extended-state-observer-based neural network adaptive motion control algorithm will be synthesized. The bounded stability of the whole closed-loop system is proved strictly. In addition, the comparisons of the application results on an electro-hydraulic servo system verify the availability and superiority of the developed control algorithm.

Adaptive control algorithm with a retraining technique to predict the optimal amount of chilled water in a data center cooling system

We developed control algorithms based on one of three artificial-intelligence-based retraining techniques (sliding window, vector adaptation, and vector augmentation) to provide the optimal indoor temperature and save on the energy expenditure for cooling in data centers. The artificial neural network prediction model predicts the computer room air handler supply air temperature of a central chilled water system and is added to the control algorithm. The proposed algorithm can determine the optimal chilled water flow rate required to cool the server to the set temperature by using the predicted computer room air handler supply air temperature. We developed a control algorithm embedded in an artificial neural network predictive model that includes three retraining techniques. Afterward, we compared the control performance and verified its adaptability by using computer simulation. When using the algorithm with sliding window control, the root mean-squared error between the set temperature and the control temperature was 0.08 °C, the maximum error was 0.81 °C, and the cooling load was 21,026.27 kWh. The accuracy, stability, and energy-saving ability of the sliding window control algorithm were higher those of the other two algorithms, and its superior adaptability and scalability under changing environmental conditions were demonstrated.

Modeling gas turbine electro power station typical operating modes using pre-trained artificial neural network

The article discusses the gas turbine electric power stations model creation on the converted aircraft engines basis. Simulation is necessary for the development and control algorithms computer testing for such electric power stations. For this, the developed control algorithms must be tested in various situations arising during the power stations operation. It is well known that it is difficult or impossible to reproduce the most critical modes in real operation and on test benches. Therefore, such studies are carried out on mathematical models, using various designs semi-natural stands, in which real control equipment is interfaced with a mathematical model that reproduces the electrical system behavior. The article discusses a possible solution to this problem: for individual characteristic modes, simplified, fast-solving models with a limited adequacy area, but with high performance, are built. The article proposes to assign the task of constructing such models to an artificial neural network. This opens up additional opportunities for reducing the time required to obtain such models, for example, by using neural networks that have already been trained for a different mode, which in the future will allow expanding the adequacy area of the created neural network models.

Collective Variational Inference for Personalized and Generative Physiological Modeling: A Case Study on Hemorrhage Resuscitation.

Individual physiological experiments typically provide useful but incomplete information about a studied physiological process. As a result, inferring the unknown parameters of a physiological model from experimental data is often challenging. The objective of this paper is to propose and illustrate the efficacy of a collective variational inference (C-VI) method, intended to reconcile low-information and heterogeneous data from a collection of experiments to produce robust personalized and generative physiological models. To derive the C-VI method, we utilize a probabilistic graphical model to impose structure on the available physiological data, and algorithmically characterize the graphical model using variational Bayesian inference techniques. To illustrate the efficacy of the C-VI method, we apply it to a case study on the mathematical modeling of hemorrhage resuscitation. In the context of hemorrhage resuscitation modeling, the C-VI method could reconcile heterogeneous combinations of hematocrit, cardiac output, and blood pressure data across multiple experiments to obtain (i) robust personalized models along with associated measures of uncertainty and signal quality, and (ii) a generative model capable of reproducing the physiological behavior of the population. The C-VI method facilitates the personalized and generative modeling of physiological processes in the presence of low-information and heterogeneous data. The resulting models provide a solid basis for the development and testing of interpretable physiological monitoring, decision-support, and closed-loop control algorithms.

Position Control of a Pneumatic Drive Using a Fuzzy Controller with an Analytic Activation Function.

The fuzzy logic controller, which uses an analytic activation function for the defuzzification procedure, was applied to the position control of a servo pneumatic drive controlled by a proportional valve. The Gaussian shape of input fuzzy sets, with the possibility of their modification, was used to fuzzify the input signal. The control signal was determined by introducing an analytic function instead of defining the fuzzy rule base. In this way, a conventional 2-D fuzzy rule table base is modified into 1-D fuzzy defuzzification based on an analytic function to calculate the controller output. In this control algorithm, the problem of conventional fuzzy logic control, in terms of the exponential growth in rules as the number of input variables increases, is eliminated. The synthesis controller procedure is adjusted to the flow rate characteristic of the proportional valve. The developed control algorithms are verified by computer simulation and by testing on a real pneumatic rodless cylindrical drive.

Thermal control of coke furnace by data-driven approach

To achieve efficient and stable coke furnace operation, we developed an operation guidance system to reduce the variance of net coking time (NCT). A control algorithm that predicts the future NCT by locally weighted regression using the individual chamber database and adjusts the fuel gas flow rate was constructed. After a simulation validation using a newly developed one-dimensional transient model, the operation guidance system based on the developed control algorithm was implemented in a real plant. As a result, the root mean square (RMS) of the control error of NCT was reduced by 0.12 h, and the combustion chamber temperature was reduced by 28.8°C. The developed operation guidance system contributed to the reduction of fuel consumption and CO2 emission of the coke furnace.

Identification of particular hydrodynamic parameters for a modular type 4 DOF underwater vehicle by means of CFD method

PurposeThe development of robust control algorithms for the position, velocity and trajectory control of unmanned underwater vehicles (UUVs) depends on the accuracy of their mathematical models. Accuracy of the model is determined by precise estimation of the UUV hydrodynamic parameters. The purpose of this study is to determine the hydrodynamic forces and moments acting on an underwater vehicle with complex body geometry and moving at low speeds and to achieve the accurate coefficients associated with them.Design/methodology/approachA three-dimensional (3D) computer-aided design (CAD) model of UUV is designed with one-to-one dimensions. 3D fluid flow simulations are conducted using computational fluid dynamics (CFD) software programme in the solution of Navier Stokes equations for laminar and turbulent flow analysis. The coefficients depending on the hydrodynamic forces and moments are determined by the external flow analysis using the CFD programme. The Flow Simulation k-ε turbulence model is used for the transition from laminar flow to turbulent flow. Hydrodynamic properties such as lift and drag coefficients and roll and yaw moment coefficients are calculated. The parameters are compared with the coefficient values found by experimental methods.FindingsAlthough the modular type UUV has a complex body geometry, the comparative results of the experiments and simulations confirm that the defined model parameters are accurate and close to the actual experimental values. In the proposed k-ε method, the percentage error in the estimation of drag and lifting coefficients is decreased to 4.2% and 8.39%, respectively.Practical implicationsThe model coefficients determined in this study can be used in high-level control simulations which leads to the development of robust real-time controllers for complex-shaped modular UUVs.Originality/valueThe Lucky Fin UUV with 4 degrees of freedom is a specific design and its CAD model is first extracted. Verification of simulation results by experiments is generally less referenced in studies. However, it provides more precise parameter identification of the model. Proposed study offers a simple and low-cost experimental measurement method for verification of the hydrodynamic parameters. The extracted model and coefficients are worthwhile references for the analysis of modular type UUVs.

Safety of Dynamical Systems With Multiple Non-Convex Unsafe Sets Using Control Barrier Functions

This letter presents an approach to deal with safety of dynamical systems in presence of multiple non-convex unsafe sets. While optimal control and model predictive control strategies can be employed in these scenarios, they suffer from high computational complexity in case of general nonlinear systems. Leveraging control barrier functions, on the other hand, results in computationally efficient control algorithms. Nevertheless, when safety guarantees have to be enforced alongside stability objectives, undesired asymptotically stable equilibrium points have been shown to arise. We propose a computationally efficient optimization-based approach which allows us to ensure safety of dynamical systems without introducing undesired equilibria even in presence of multiple non-convex unsafe sets. The developed control algorithm is showcased in simulation and in a real robot navigation application.

Modular Model-Based Design for Aerial Manipulation with Simulink and ROS

Model-based design utilizing Simulink is state of the art in mechatronic system development. A key advantage is that developed control algorithms are deployed onto the target execution platform by automated code generation. For many robotic applications, the Robotic Operating System (ROS) is an attractive target execution platform with growing interest in UAV development such as aerial manipulation. The presented model-based design approach includes modular Guidance, Navigation and Control (GNC) development in Simulink and code generation of encapsulated ROS nodes for each of the GNC modules. To reduce control cycle latency, an automated code modification is proposed, for which design guidelines are given to define an efficient ROS node execution order. The modular approach is compared to a monolithic approach where just a single ROS node is generated for the complete GNC functionality. The design approach is demonstrated by flight experiments for an aerial screwing process where lateral contact forces at the manipulator tip are generated. Force estimation results compared to ground truth measurements show good agreement in flight experiment and simulation. For this case study, the modular design approach results in more than 73% time saving for code generation.

A review of emerging design concepts in applied microgrid technology

<abstract> <p>Most of the research in distributed generation focuses on power flow optimization and control algorithm development and related fields. However, microgrids are evolving on multiple levels with respect to the chemical processes used to manufacture the underlying technologies, deployment strategies, physical architecture (which is important to the economic factor) as well as environmental impact mitigation of microgrids. Special use cases and paradigms of deploying Distributed Generation (DG) in harmony with agricultural or decorative purposes for existing spaces are emerging, propelled by research in frontiers that the DG engineer would benefit from being aware of. Also, offshore photovoltaic (PV) has emerged as an increasingly important research area. Many nascent technologies and concepts have not been techno-economically analyzed to determine and optimize their benefits. These provide ample research opportunities from a big-picture perspective regarding microgrid development. This also provides the avenue for research in distributed generation from a physical integration and space use perspective. This study reviews a selection of developments in microgrid technology with the themes of manufacturing technology, optimal deployment techniques in physical spaces, and impact mitigation approaches to the deployment of renewable energy from a qualitative perspective.</p> </abstract>

Implementation of procedures for optimal control of timed event graphs with resource sharing

It is a well-known fact that the dynamics of timed event graphs (TEGs), a subclass of timed Petri nets able to model delay and synchronization phenomena, admits linear representation in dioids of formal power series. In order to model and control systems of TEGs with resource sharing phenomena, it is useful to extend the standard set of dioid operations between series by including the Hadamard product, its residual, and its dual residual. Until now, however, the characterization of the largest set of series for which the dual residual of the Hadamard product is defined was still incomplete. Rather than being a mere theoretical dilemma, this open problem delayed the development of reliable algorithms for optimal control of the considered class of systems. In this paper, we provide the solution to the problem, and discuss the implementation of procedures for computing the Hadamard product and related operations. Tests are conducted to evaluate their performance in solving optimal control problems for systems of TEGs with resource sharing and output-reference update

Adaptive robust list control for an aircraft with a moving mass

The article is devoted to the development of a high-precision list angle control algorithm for an aircraft with a single moving mass. Taking into account the nonmodulated uncertainty and parametric uncertainties in the system, an adaptive robust control algorithm based on the backstepping scheme is proposed. The algorithm consists of robust and adaptive structures. The robust structure levels out nonmodulated uncertainties and disturbances in the system, while the adaptive structure estimates the system parameters. The proposed controller theoretically guarantees a global limited tracking capability according to the Lyapunov analysis method. The simulation results, confirming the effectiveness of the proposed algorithm are presented. Keywords adaptive robust control; backstepping; moving mass control system; maneuvering reentry vehicle

Dynamic Active Noise Control of Broadband Noise in Fighter Aircraft Pilot Helmet

This paper presents the development of a dynamic Active Noise Control (ANC) algorithm aimed towards reducing the broadband noise inside the helmet earcups of a fighter aircraft pilot helmet. The dynamic ANC involves a Variable Step-Size Griffiths (VSSG) FxLMS algorithm to attenuate noise entering directly through helmet, a LMS based adaptive noise canceller to attenuate noise entering through the pilot microphone, and energy detectors for failure protection and optimized battery power usage. The algorithms are implemented on Texas Instruments’ TMS320C6748 processor and are tested in a helmet ANC experimental setup.

Development of a PMSM motor field-oriented control algorithm for electrical vehicles

Recently, with issues such as global warming, melting of glaciers and climate change, the need for sustainable methods of living has seen a drastic increase. Combustion cars that run on petrol and diesel are slowly being replaced with electrical vehicles (EVs). Currently, EVs face challenges in terms of range of kilometers offered, availability of charging systems, infrastructure for their development, etc. As the need for electrical vehicles increase, the usages of Permanent Magnet Synchronous Motor (PMSM) too have seen a growing trend. Motor control in an electrical vehicle is extremely crucial as a vehicle on road is subject to different terrains and hence, different loads. Throughout all these different scenarios, it is essential that the motor operates at its optimum efficiency in order to provide optimum vehicle performance for the driver and passengers. Currently, several methods are available for the control of electrical vehicles, nevertheless the field-oriented control (FOC) method for permanent magnet motors is widely used due to its efficient nature. This paper discusses with the design of modified FOC control algorithm for control the PMSM motor eliminates an initial jerk seen during the starting condition of a motor thereby providing a smoother start. As the entire simulation is done using Hardware in the Loop model, the values obtained have real life constraints taken into account, as opposed to a software only simulation. Moreover, the transient time taken to reach a steady state value in speed and to also change direction of rotation have been improved as well. The simulation has been designed entirely on the Altair Embed software.