Hybrid Control System Research Articles

Experimental Study on Performance of Impact Damper For Various Mass Ratios

Vibration plays a vital role in many structural elements. Excessive vibration can cause failure due to resonance and fatigue, malfunctioning of critical components, etc. Vibration can be controlled using passive, active and hybrid control systems. This paper presents the experimental studies carried out on the behaviour of impact dampers, which belong to the category of passive vibration control devices that are used to attenuate vibration of discrete and continuous systems. Impact damping is a technique for improving damping of a dynamic system by means of dissipation of energy due to collision of a free mass on the main mass.
 Experimental studies are carried out for forced vibration conditions by giving sinusoidal varying forcing function to the cantilever beam system using an exciter system and the time/frequency response function is determined. Impact mass is placed at free end of cantilever beam and the system is given constant base excitation and the damping of the system is studied by varying the frequency. Experiments are conducted for various mass ratios with different impact mass materials and the results are compared with theoretical predictions.

Reactive power control of hybrid systems using improved coyote optimizer

SummaryA new method of reactive power control by photovoltaic and hydrogen is presented in this study. Photovoltaic has been employed for harvesting the hydrogen which is based on considering the weather conditions. The proposed system includes a combination of photovoltaic, hydrogen, and fuel cell along with a DG to connect to the grid and to improve the supply power quality. The main contribution of this paper is to direct an improved metaheuristic algorithm, called improved coyote optimization (CO) algorithm for achieving a proper DG placement. The improved version of the CO algorithm has benefitted from a spiral policy that is derived from Whale optimization algorithm. This process makes better control for the social behavior of the coyotes. Reactive power optimization (RPO) has been established after the size selection objective function. Big data technology is also used for improving the historical solution matching‐based RPO appliance. Cosine distance is used for measurement purposes of the historical solution matching‐based similarity technique during the computation time of conventional RPO and PVH‐FC features. As a result of using the suggested CO, the costs for electricity and losses are reduced by around 86.6% and 26.9%, respectively. Additionally, realized profits showed that applying the suggested strategy reduced the overall cost from 9.315e6 to 4.435e6 units. After optimization, the network loss and power are finally reduced to 1325 Kvar and 1371 kW, respectively. The results show that the suggested RPO technique has higher speed of calculation in comparison with some latest algorithms. Achievements also show that the suggested method provides a proper and optimal solution for RPO.

Tailored presolve techniques in branch‐and‐bound method for fast mixed‐integer optimal control applications

AbstractMixed‐integer model predictive control (MI‐MPC) can be a powerful tool for controlling hybrid systems. In case of a linear‐quadratic objective in combination with linear or piecewise‐linear system dynamics and inequality constraints, MI‐MPC needs to solve a mixed‐integer quadratic program (MIQP) at each sampling time step. This paper presents a collection of exact block‐sparse presolve techniques to efficiently remove decision variables, and to remove or tighten inequality constraints, tailored to mixed‐integer optimal control problems. In addition, we describe a novel approach based on a heuristic presolve algorithm to compute a feasible but possibly suboptimal MIQP solution. We present benchmarking results for a C code implementation of the proposed BB‐ASIPM solver, including a branch‐and‐bound (B&B) method with the proposed tailored presolve techniques and an active‐set based interior point method (ASIPM), compared against multiple state‐of‐the‐art MIQP solvers on a case study of motion planning with obstacle avoidance constraints. Finally, we demonstrate the feasibility and computational performance of the BB‐ASIPM solver in embedded system on a dSPACE Scalexio real‐time rapid prototyping unit for a second case study of stabilization for an underactuated cart‐pole with soft contacts.

Decentralized multi-agent control of a three-tank hybrid system based on twin delayed deep deterministic policy gradient reinforcement learning algorithm

Decentralized multi-agent control of a three-tank hybrid system based on twin delayed deep deterministic policy gradient reinforcement learning algorithm

A Piecewise Linear Regression and Classification Algorithm With Application to Learning and Model Predictive Control of Hybrid Systems

This paper proposes an algorithm for solving multivariate regression and classification problems using piecewise linear predictors over a polyhedral partition of the feature space. The resulting algorithm that we call PARC (Piecewise Affine Regression and Classification) alternates between (<i>i</i>) solving ridge regression problems for numeric targets, softmax regression problems for categorical targets, and either softmax regression or cluster centroid computation for piecewise linear separation, and (<i>ii</i>) assigning the training points to different clusters on the basis of a criterion that balances prediction accuracy and piecewise-linear separability. We prove that PARC is a block-coordinate descent algorithm that minimizes a suitably constructed objective function and that it converges in a finite number of steps. The algorithm is used to learn hybrid numerical/categorical (HYNC) dynamical models from data that contain real and discrete labeled values. The resulting model has a piecewise linear structure that is particularly useful to formulate model predictive control problems and solve them by mixed-integer programming. A Python implementation of PARC is available at <uri>http://cse.lab.imtlucca.it/~bemporad/parc</uri>.

Closed circuit hybrid control system IQ and Diabeloop in type 1 diabetics in open circuit prior treatment: data of 14 days

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Energy Management For Renewable Hybrid System Based On Artificial Neural Networks (ANN)

Abstract: It has become necessary in recent years to use renewable energy sources, such as wind and solar energy, to meet the load demand. Because of their endless existence and environmentally friendly nature, they are useful for power generation. The management of energy in hybrid wind-solar power sources is the topic of this article. Using a common current source interface multiple-input DC-DC converter, the photovoltaic (PV) array, wind turbine, and battery storage are all connected. The power control between intermittent renewable energy generation, energy storage, and the grid is maintained by the artificial neural network (ANN) controller. For wind turbines to produce the most electricity, variable speed control can be used. For solar systems, the Maximum Power Point Tracking (MPPT) algorithm is used. By keeping the common DC voltage constant, the grid interface inverter sends the energy drawn from the PV array and wind turbine into the grid. Using MATLAB Simulink, the complete control strategy is subjected to a simulation study. The results of the simulation show the photovoltaic/wind hybrid system's dynamic behavior and control effectiveness

Energy Efficiency in Electromagnetic and Electro-Permanent Lifting Systems

In industrial settings, mechanical systems for clamping workpieces onto machine tools or for lifting and handling ferrous material are often replaced by magnetic clamping systems. For large lifting applications, the most suitable magnetic clamping systems are electromagnetic (EM) and electro-permanent (EP) approaches. Their configurations, direct and induced poles or direct poles only, need to be discussed in order to choose the most appropriate and economical setup for the specific application. In addition, an evaluation of the difference in the energy efficiency between EM and EP systems, which is often not simple, is presented. To achieve this, a hybrid control system for EM or EP systems was developed and, with the help of this prototype, an analysis of these two magnetic systems was carried out to highlight the main differences. The hybrid control system consists of a single control unit that can be used by both systems, and can be achieved by simply changing the type of lifting magnet. In this research, the realization, operational description, and analysis performed on this prototype were addressed.

Design Optimization of a Hybrid Vibration Control System for Buildings

Control of high-rise structures under seismic excitations was investigated using a passive hybrid control system consisting of a base-isolation (BI) subsystem and a passive tuned liquid column damper (TLCD) system. Both of the systems were optimized considering using the other system in the same structure. An optimization method was developed, and a computer code was written based on dynamic analysis of the structure and metaheuristic optimization methods. Within the scope of the study, a general solution was found by using many earthquake records during the optimization process. Moreover, one of the most suitable and successful metaheuristic algorithms was used in this study. In addition, numerical simulations were performed on a benchmark high-rise building structure to investigate the effectiveness of the optimized hybrid control system in controlling the seismic response of the building. The performance of the base-isolated TLCD-controlled structure was examined when the TLCD was placed on the base floor by using a set of 44 recorded ground motions as base excitations. Based on the results obtained from this study, the use of a base-isolation subsystem decoupling the superstructure from the ground motions by lowering the structure’s fundamental natural frequency reduces the structural responses of the building in most cases. The responses of the base-isolation subsystem were not too large since the parameters of the BI subsystem were optimized specifically for the investigated structure. Nevertheless, displacements of BI might exceed the maximum limit to undesirable values in some cases. The TLCD system appears to be quite effective in protecting the base-isolation subsystem by reducing its displacements to the maximum allowable limit or below when attached to it. Moreover, the proposed passive hybrid control system can effectively reduce the structural responses under seismic excitations.

FalsifAI: Falsification of AI-Enabled Hybrid Control Systems Guided by Time-Aware Coverage Criteria

Modern Cyber-Physical Systems (CPSs) that need to perform complex control tasks (e.g., autonomous driving) are increasingly using AI-enabled controllers, mainly based on deep neural networks (DNNs). The quality assurance of such types of systems is of vital importance. However, their verification can be extremely challenging, due to their complexity and uninterpretable decision logic. Falsification is an established approach for CPS quality assurance, which, instead of attempting to prove the system correctness, aims at finding a time-variant input signal violating a formal specification describing the desired behavior; it often employs a search-based testing approach that tries to minimize the <i>robustness</i> of the specification, given by its quantitative semantics. However, guidance provided by robustness is mostly black-box and only related to the system output, but does not allow to understand whether the temporal internal behavior determined by multiple consecutive executions of the neural network controller has been explored sufficiently. To bridge this gap, in this paper, we make an early attempt at exploring the temporal behavior determined by the repeated executions of the neural network controllers in hybrid control systems and first propose eight time-aware coverage criteria specifically designed for neural network controllers in the context of CPS, which consider different features by design: the simple temporal activation of a neuron, the continuous activation of a neuron for a given duration, and the differential neuron activation behavior over time. Secondly, we introduce a falsification framework, named <inline-formula><tex-math notation="LaTeX">$\mathtt {FalsifAI}$</tex-math></inline-formula>, that exploits the coverage information for better falsification guidance. Namely, inputs of the controller that increase the coverage (so improving the <i>exploration</i> of the DNN behaviors), are prioritized in the <i>exploitation</i> phase of robustness minimization. Our large-scale evaluation over a total of 3 typical CPS tasks, 6 system specifications, 18 DNN models and more than 12,000 experiment runs, demonstrates 1) the advantage of our proposed technique in outperforming two state-of-the-art falsification approaches, and 2) the usefulness of our proposed time-aware coverage criteria for effective falsification guidance.

DE-Based Design of an Intelligent and Conventional Hybrid Control System with IPFC for AGC of Interconnected Power System

In this study, a fuzzy proportional integral derivative controller (FPID) was adjusted using the differential evolution (DE) method to enhance the automated generation control (AGC) of a three-zone reheat-type power system. The objective function used in this study was an integral of the time-weighted absolute error (ITAE). In the optimization, the gain control parameters of the proportional integral (PI), the integral (I), and FPID were optimized and compared to improve the limitations drawn by the controller over a few parameters. To demonstrate that FPID controllers with IPFC produce better and more accurate optimization results than integral and PI controllers optimized by DE, the interline power flow control (IPFC) of a flexible AC transmission system (FACTS) device with suitable connections and control parameter optimization was used. Also, the particle swarm optimization (PSO) PID with IPFC was compared with the proposed DEFPID + IPFC, and better results were achieved by using the DE technique. Similarly, to demonstrate the suggested technology’s strong control capacity, random load changes were applied to the system in various conditions, and it was demonstrated that the suggested control unit easily tolerated random load perturbations and returned the system to a stable functioning state.

Temporal logic explanations for dynamic decision systems using anchors and Monte Carlo Tree Search

For many automated perception and decision tasks, state-of-the-art performance may be obtained by algorithms that are too complex for their behavior to be completely understandable or predictable by human users, e.g., because they employ large machine learning models. To integrate these algorithms into safety-critical decision and control systems, it is particularly important to develop methods that can promote trust into their decisions and help explore their failure modes. In this article, we combine the anchors methodology with Monte Carlo Tree Search to provide local model-agnostic explanations for the behaviors of a given black-box model making decisions by processing time-varying input signals. Our approach searches for descriptive explanations for these decisions in the form of properties of the input signals, expressed in Signal Temporal Logic, which are highly likely to reproduce the observed behavior. To illustrate the methodology, we apply it in simulations to the analysis of a hybrid (continuous-discrete) control system and a collision avoidance system for unmanned aircraft (ACAS Xu) implemented by a neural network.

Predictor-Based Global Sampled-Data Output Feedback Stabilization for Nonlinear Uncertain Systems Subject to Delayed Output

In this article, we address the problem of global sampled-data output feedback stabilization for a class of nonlinear uncertain systems with delayed output using the continuous-discrete method. Thanks to the prediction technique and feedback domination method, a novel coupled design method of predictor-based continuous-discrete observer and linear controller is proposed when only delayed sampled-data output is accessible. The proposed predictor-based observer can effectively estimate the unknown state by compensating the influences of sampling and output delay. The main advantage of the proposed control method is that the full state information and accurate model nonlinearities do not need to be known any more. The global exponential stability of the overall hybrid control system can be ensured when there hold some sufficient conditions with respect to the maximum allowable sampling period and output delay.

Seismic performance improvement of continuous rigid-frame bridges with hybrid control system under near-fault ground motions

To improve the seismic performance of continuous rigid-frame bridges (CRFBs) in near-fault earthquakes, this study developed a hybrid seismic control system (HSCS) combining a three-dimensional hybrid seismic isolation bearing (3D-HSIB) with a longitudinal fluid viscous damper (L-FVD). The hybrid numerical model of the HSCS and the nonlinear numerical model of a CRFB prototype were simulated in OpenSees. The study comparatively analyzed the seismic responses of non-controlled bridges and HSCS-controlled bridges under near-fault ground motions, evaluated the seismic control effectiveness of the HSCS, and investigated the effects of vertical excitations on the seismic performance of the CRFB. The numerical results show that the proposed HSCS effectively reduces the three-directional seismic responses of the CRFB. When the PGA is 0.4 g, the seismic isolation ratios of displacement, internal force, and pounding force all exceed 0.4. The hysteresis behavior of the HSCS can be fully utilized under three-directional excitations with pulse-like effects. The girder-end uplift behavior under vertical excitation can be effectively controlled by the proposed 3D-HSIB with a seismic isolation ratio of at least 0.6. When vertical excitation exceeds a certain level, the bearing is tensioned upward while axial tension force may also occur in main piers. Furthermore, the seismic responses under pulse-like near-fault ground motions are significantly larger than those under non-pulse near-fault ground motions. This work may provide a useful reference for the seismic analysis and design of CRFBs.

A Hybrid Systems-Based Hierarchical Control Architecture for Heterogeneous Field Robot Teams.

Field robot systems have recently been applied in a wide range of research fields. Further automation, development, and activation of such systems require cooperation among heterogeneous robots. Classical control theory is inefficient in managing large-scale complex dynamic systems. Therefore, a discrete-event system based on the supervisory control theory must be introduced to overcome this limitation. In this article, we propose a hybrid system-based hierarchical control architecture using a supervisory control-based high-level controller and a traditional control-based low-level controller. The hybrid system and its dynamics are modeled through a formal method, called hybrid automata, and the behavior specifications are designed to express the control objectives for cooperation. In addition, modular supervisors that are more scalable and maintainable than a centralized supervisory controller were synthesized. The proposed hybrid system and hierarchical control architecture were implemented, validated, and evaluated for performance through a physics-based simulator and field tests. The experimental results confirmed that the robot team satisfied the given specifications and presented systematic results, validating the efficiency of the proposed control architecture.

An SMPS-Based Lithium-Ion Battery Test System for Internal Resistance Measurement

A switching mode power supply (SMPS)-based lithium-ion (Li-ion) battery test system (BTS) is proposed to meet the increasing demands of tests in battery research. To improve the flexibility, while building the BTS with low cost and high reliability, an SMPS is employed as a charger/discharger, combined with a digital-to-analog hybrid control system to regulate its output voltage and current. Based on the BTS, two dc internal resistance (DCIR) measurement methods are designed, and a battery state classification method based on the DCIR cluster analysis with similarity as the judgment basis is proposed. An experimental prototype is developed to verify the feasibility of the proposed BTS and methods. The results show that the BTS possesses excellent voltage and current regulation capabilities for charging, discharging, and grid energy recovery. The internal parameter extraction method enables obtaining DCIR and other parameters with high accuracy. The online measurement method allows automatic acquisition of real-time DCIR and its variation trend without affecting the charging, which greatly improves the measurement efficiency. The proposed hierarchical clustering method based on DCIR effectively achieves the classification of battery states with excellent practicality and robustness.

Exploring the Limits of Controlled Markovian Quantum Dynamics with Thermal Resources

Our aim is twofold: First, we rigorously analyse the generators of quantum-dynamical semigroups of thermodynamic processes. We characterise a wide class of gksl-generators for quantum maps within thermal operations and argue that every infinitesimal generator of (a one-parameter semigroup of) Markovian thermal operations belongs to this class. We completely classify and visualise them and their non-Markovian counterparts for the case of a single qubit. Second, we use this description in the framework of bilinear control systems to characterise reachable sets of coherently controllable quantum systems with switchable coupling to a thermal bath. The core problem reduces to studying a hybrid control system (“toy model”) on the standard simplex allowing for two types of evolution: (i) instantaneous permutations and (ii) a one-parameter semigroup of [Formula: see text]-stochastic maps. We generalise upper bounds of the reachable set of this toy model invoking new results on thermomajorisation. Using tools of control theory we fully characterise these reachable sets as well as the set of stabilisable states as exemplified by exact results in qutrit systems.

MANIPULATOR CONTROL SYSTEMS REVIEW

This review focuses on manipulator control systems, which are systems that control the movement of manipulators, a type of robot programmed to move in specific ways. The control systems send commands to actuators (such as motors or pneumatic cylinders) based on input from sensors (such as position or force sensors) to control the movement of the manipulator's joints. Manipulator control systems have a wide range of applications in industries such as robotics, industrial automation, and medical and surgical robotics. The review covers the different types of manipulator control systems, including open-loop, closed-loop, and hybrid control systems, and their applications. It also discusses control strategies, including Proportional-Integral-Derivative (PID) control, Linear Quadratic Regulator (LQR) control, and Model Predictive Control (MPC). The review also covers sensors and actuators used in manipulator control systems and the challenges and future directions in the field. The review provides a comprehensive understanding of the current state of manipulator control systems and highlights the recent developments and future directions in the field, providing insights for future research and development.

Design of Deliberative and Reactive Hybrid Control System for Autonomous Stuff-Delivery Robot Rover

ABSTRAK Saat ini, robot otonom memiliki peranan signifikan di berbagai bidang. Robot otonom meningkatkan efektivitas dan produktivitas sekaligus menurunkan risiko dan tingkat kesalahan secara signifikan. Dua paradigma dapat digunakan saat merancang robot otonom, yaitu paradigma reaktif dan deliberatif. Paper ini merancang sistem kontrol hybrid untuk menggabungkan elemen terbaik dari sistem kontrol deliberatif dan reaktif untuk robot pengiriman otonom. Model tersebut dibuktikan dengan misi untuk memindahkan tiga objek berwarna, dari posisinya ke tujuan. Dari perancangan dan pengujian, sistem hybrid dapat meminimalisir kelemahan dari masing-masing sistem, sehingga proses pengiriman barang dapat tercapai sesuai dengan rencana dan dapat bereaksi terhadap kondisi yang sebelumnya tidak diketahui dalam perencanaan, seperti hambatan. Penggunaan sistem hybrid membuka kemungkinan untuk merancang sebuah mobile robot otonom yang dapat beroperasi di lingkungan apapun. Kata kunci: deliberative, reactive, hybrid, autonomous, robot rover ABSTRACT Recently, autonomous robots have become increasingly significant in numerous fields. Robots with autonomy increase effectiveness and productivity while significantly lowering risk and error rates. Two paradigms can be used when creating an autonomous robot, namely reactive and deliberative paradigm. This paper proposes a hybrid control system to combine the best elements of deliberative and reactive control system for an autonomous delivery robot rover. The model was proved with a mission to move the three colored objects, from their respective positions to the goal cells. From the design and testing the hybrid systems can minimize the weaknesses of each system, so that the stuff-delivery process can be achieved in accordance with the plan and can react to conditions that were not previously known in planning, such as the obstacle. The use of a hybrid system opens up the possibility of designing an autonomous mobile robot that can operate in any environment. Keywords: deliberative, reactive, hybrid, autonomous, robot rover

Model and Simulation of a Floating Hybrid Wind and Current Turbines Integrated Generator System, Part I: Kinematics and Dynamics

This initial publication is part of a series of publications that will appear soon, which pursue a final objective for the proposal of a fully integrated and controlled hybrid system composed of a floating wind turbine—type “OC3-Hywind”—and two marine current turbines with the aim of increasing the energy generated by the floating installation and, at the same time, use the set of turbines as actuators as part of an integral cooperative control system of the floating hybrid system to ensure the structural stability of the floating hybrid generator system (FHGS) in harsh weather conditions, which is a key issue in this type of floating systems. A specially designed tool to design, analyze, and control this type of FHGSs was developed using Matlab®. In this tool, named Floating Hybrid Generator Systems Simulator (FHYGSYS), several tests were carried out on the structural stability of the system considering the interactive phase of the acting forces. Working in a programming environment like Matlab® allows design freedom and the possibility of evaluating the system with different geometries, aerodynamic airfoils, and external meteorological conditions, and also including or eliminating certain elements, etc. This versatility will be helpful in future studies aimed at evaluating this system and maximizing the production of energy.