Unified Controller Research Articles

Unified Human–Robot–Environment Interaction Control in Contact-Rich Collaborative Manipulation Tasks via Model-Based Reinforcement Learning

Many safety-critical or performance-demanding systems are human-in-the-loop, i.e., the robot interacts with human being and environment, in which human-in-the-loop control becomes a key research topic. In this paper, a unified optimal interaction control in joint space is presented for multi-point human-robot-environment interaction (HREI) problems, which are very common in human-robot collaborative manipulation tasks. Specifically, model-based reinforcement learning method is leveraged for obtaining optimal interaction control. For multi-point human-robot-environment interaction, the interaction forces exerted on each link are isolated and estimated via the backward generalized momentum observer method. In human-robot-environment interaction problems, the environmental as well as the human arm's dynamics parameters are usually unknown, stochastic, and time-varying. To obviate the dependence on these parameters, the GMM/GMR method is employed to learn the unknown external dynamics. It is noteworthy the interaction forces are considered as system states, the time derivatives of which are computed based on the GMM/GMR learning results via chain-rule. Then the iterative linear quadratic Gaussian with learned external dynamics (ILQG-LED) method is utilized to realize optimal multi-point human-robot-environment interaction control. The validity of the proposed method is verified through experimental studies.

METHODS OF PROCESSING ANALOG PARAMETERS OF AIRCRAFT FLIGHT INFORMATION

Modern systems of aircraft objective control make it possible to accumulate a whole mass of information obtained during a certain cycle of operation, which makes it possible to increase the flight safety. It is important to determine the main control parameters and methods of processing the data obtained. The article discusses the main analog parameters for objective flight control and modern methods of their processing. The information technology of statistical data processing is considered, which involves determining the necessary nomenclature of objective information, determining the frequency of data collection and the accuracy of measurement channels, the technology of hardware acquisition, exchange and storage of data, performing statistical data processing and forecasting the state of the control object. It is noted that the accuracy of the measured parameters is always subjective and corresponds to the established tolerances. Parameters always change their values over time under the influence of various factors, creating a general picture of "white noise". Isolation of useful signals ("state signs") from general "noise" requires the recognition of some standard for groups of researched objects (subjects). The number of processed "signs" is determined by the principle of sufficiency, with the desire for "minimalism". In order to fulfill the requirements of ICAO and integration into a unified flight safety control system, with the agreement on common airspace with the EU (Open Skies Program), it is necessary to create (improve) a modern flight control system based on new physical and digital principles of flight data processing using high-speed transmission technologies.

Motion planning and stabilization of nonholonomic systems using gradient flow approximations

Nonlinear control-affine systems described by ordinary differential equations with time-varying vector fields are considered in the paper. We propose a unified control design scheme with oscillating inputs for solving the trajectory tracking and stabilization problems under the bracket-generating condition. This methodology is based on the approximation of a gradient-like dynamics by trajectories of the designed closed-loop system. As an intermediate outcome, we characterize the asymptotic behavior of solutions of the considered class of nonlinear control systems with oscillating inputs under rather general assumptions on the generating potential function. These results are applied to examples of nonholonomic trajectory tracking and obstacle avoidance.

A novel MIMO SMC for comprehensive unified control of the cascaded DC‐DC and DC‐AC converters in grid connected photovoltaic systems

SummaryIt is well known that DC‐DC boost converters are cascaded with DC‐AC inverters for grid connection of the photovoltaic (PV) systems. In the traditional control approaches, the mentioned DC‐DC and DC‐AC converters are controlled separately to facilitate the controller design problem. However, from the controller design viewpoint, the overall structure of the grid connected PV generator is a multi‐input multi‐output (MIMO) system. The duty cycle of the DC‐DC converter and inverter modulation index are the control inputs and, on the other hand, generated photovoltaic DC power and exported power to the grid are control outputs. Moreover, the inverter DC link voltage should be stabilized by the closed‐loop controller as well as an internal control output. If controllers are designed separately, it means that the interaction between DC‐DC and DC‐AC controllers isn't considered accurately, and since the isolated models of DC‐DC converter and DC‐AC inverter are extracted based on some approximated assumptions, separate controller design cannot guarantee stability and robustness of the whole system in a wide range of operation. To cope with these problems, in this paper, a novel MIMO sliding mode controller (SMC) is developed for comprehensive closed‐loop control of the DC‐DC boost converter cascaded with a single‐phase DC‐AC grid connected photovoltaic inverter. In the proposed approach, the dynamic model of whole system is developed comprehensively at first, and then, a unique MIMO controller is designed to control both DC‐to‐AC and DC‐to‐DC converters together. To cope with the nonlinear characteristic of the system and uncertainty of model parameters in a wide range, a fixed‐frequency SMC is developed using the comprehensive state space model of the closed loop system. In the proposed MIMO‐SMC controller, the AC power (which is exported to the grid) and operating point of the PV source are controlled via inverter modulation index and duty cycle of the DC‐DC boost converter, respectively. Another major advantage of the proposed system is mitigating the non‐minimum phase characteristic of the boost converter through the indirect control of inverter DC link capacitor. To evaluate the performance of the designed control system, simulation results are compared with a standard linear PI controller. It is shown that the developed system has zero steady‐state error and enjoys faster dynamic response during the start‐up and step changes of AC and DC current references. Moreover, it can maintain the stability of closed‐loop systems in a wide range of operations.

Finite/Fixed-Time Synchronization of Memristor-Based Fuzzy Neural Networks with Markov Jumping Parameters Under Unified Control Schemes

Finite/Fixed-Time Synchronization of Memristor-Based Fuzzy Neural Networks with Markov Jumping Parameters Under Unified Control Schemes

A new robust nonlinear control strategy for UIPC in isolated hybrid microgrids

This paper focuses on the control of unified interphase power controller (UIPC) in isolated hybrid microgrids (HmGs), where the UIPC is used as the main component for interconnecting of AC and DC sub-grids. The investigated HmG encompasses two AC sub-grids and one purely storage DC sub-grid (PSDC) and multiple adjustable loads. A robust nonlinear model reference adaptive control (NMRC) strategy is offered for controlling the UIPC, keeping an acceptable two-way power exchange control with uncomplicated topology. Further, a novel configuration for the DC link power converter is suggested using the dual-active-bridge topology. Also, a harmonic-based modeling method is used to model the UIPC's power converter. The proposed configuration provides bidirectional power flow, increased power density, straightforward adaptation of zero-voltage switching, and direct access to cascading and parallelism to increase the ratings and reliability of UIPC. The simulation results illustrate the feasibility of the suggested topology for isolated HmGs.

A transient stability enhancement framework based on rapid fault-type identification for virtual synchronous generators

The virtual synchronous generator (VSG) control, widely used for voltage source inverters, is prone to the loss of synchronization (LOS) under large disturbances such as grid voltage sags. Despite the significant amount of research that has been conducted on the transient stability of VSGs, the existing conclusions on the influence of virtual inertia under different fault conditions remain controversial. In this paper, the conflicting effect of virtual inertia on VSG transient stability under different severities of grid voltage sag is comprehensively investigated. Faults, like grid voltage sags, are classified into two types in this paper: transient tolerable faults and transient intolerable faults. Moreover, a unified control framework for rapid fault-type identification and transient stability improvement is proposed. The Learning vector quantization (LVQ) neural networks are used to distinguish fault types within 5 ms. Based on the fault-type identification results, the proposed control strategies are applied to enhance the transient stability of VSG. Finally, simulations and control-hardware-in-loop experimental validations are carried out to demonstrate the effectiveness of the proposed framework.

System-Based Protection Method for High-Voltage Pulse Generator Switching Units in Biomass Electroporation

Background High-voltage pulsed electric field (PEF) technology has emerged as a promising technique for enhancing cell membrane permeabilization for biotechnological and medical applications. Given the parametric instability and low resistivity of biomass when used as an electrical load, it is imperative for the switching unit to meet specific requirements in terms of load capacity and protection against overloads and short circuits. Methods We introduce a novel system for safeguarding against overload and short-circuit currents by optimizing existing hardware-software resources. Our approach hinges on using the energy storage capacitor (ESC) both for energy storage and as a current sensor. By monitoring voltage drop during discharge, we estimate current parameters. Rapid discharge signals a short circuit. The microcontroller calculates ESC discharge limits based on preset parameters within a defined timeframe. If this limit is surpassed, the system promptly halts current pulses and discontinues supply to the load, ensuring reliable protection for the switching unit. Results By employing a system-based protection approach, we are able to reduce the complexity of circuit design, thereby enabling a unified control strategy that remains consistent regardless of the number and type of transistors used. This approach eliminates the need for separate circuit solutions to address this issue. Conclusions By utilizing a system method to protect the switching elements from short-circuit currents, we established a unified system structure for the circuit protection that is independent of current surges at the pulse fronts, as well as the specific number and type of transistors and switching modules employed. This method not only enhances the efficiency and reliability of the technology but also simplifies the design process and ensures a unified approach to protection, regardless of the system's configuration.

A Multi-Constraint Guidance and Maneuvering Penetration Strategy via Meta Deep Reinforcement Learning

In response to the issue of UAV escape guidance, this study proposed a unified intelligent control strategy synthesizing optimal guidance and meta deep reinforcement learning (DRL). Optimal control with minor energy consumption was introduced to meet terminal latitude, longitude, and altitude. Maneuvering escape was realized by adding longitudinal and lateral maneuver overloads. The Maneuver command decision model is calculated based on soft-actor–critic (SAC) networks. Meta-learning was introduced to enhance the autonomous escape capability, which improves the performance of applications in time-varying scenarios not encountered in the training process. In order to obtain training samples at a faster speed, this study used the prediction method to solve reward values, avoiding a large number of numerical integrations. The simulation results demonstrated that the proposed intelligent strategy can achieve highly precise guidance and effective escape.

Advancements and challenges in hybrid energy storage systems: Components, control strategies, and future directions

Hybrid energy storage systems (HESSs) can considerably improve the dependability, efficiency, and sustainability of energy storage systems (ESSs). This study examines the components of HESS, including the different types of ESSs that are typically used in hybrid systems. The advantages of integrating various ESS types in hybrid systems are also covered, along with design considerations for HESS and the interconnection topologies, which include passive, active, and semi-active architectures. For HESS, several control techniques both traditional and intelligent-have been suggested, including filter-based control, droop control, sliding mode control, rule-based control, deadbeat control, model predictive control, fuzzy logic control, optimization-based control, supervisory control, feedforward control, unified control, and robust control. The current work also discusses HESS's difficulties and potential possibilities, including technological advancement, standardization, interdisciplinary cooperation, grid integration, environmental sustainability, security and dependability, and legislative and regulatory frameworks.

A Unified Wide-Speed Range Sensorless Control Method for Switched Reluctance Machines Based on Unsaturated Reluctance

A unified unsaturated-reluctance-based wide speed range sensorless control method for switched reluctance machines (SRMs) is proposed in this paper. Firstly, the sensitivity analysis of the estimated position to the possible reluctance calculation error is analyzed. This analysis reveals the feasible regions with low sensitivity and low risk of entering saturation. Then, the unsaturated reluctance in these feasible regions is obtained and modeled. A simple second-order rational expression is proposed to fit the highly nonlinear unsaturated reluctance curve. With this simple model, the rotor position can be analytically calculated only by solving a simple quadratic problem. Based on the above-presented analysis, a wide-speed range sensorless control is developed. The rotor position is universally estimated from the real-time reluctance value and the proposed analytical model. The high-frequency voltage injection and normal conduction phase are combined to calculate the phase reluctance over the wide-speed range. The concerns of calculating reluctance at the low-speed range are also discussed. A 5.1 kW four-phase 8/6 SRM is used to verify the proposed method. Various steady state and transient state experimental results prove the effectiveness of the proposed sensorless control method.

Power quality improvement in distributed generation system using intelligent control methods

Hybrid <span>electric power generation and its integration with the grid to supply consumer demand is main focus of this paper. The use of nonlinear load and advanced power electronic equipment-based devices at the consumer end introduces power quality issues in the power system network in terms of voltage and current. This paper explains the design of a control algorithm for a unified power quality conditioner used for mitigating both voltage and current-based power quality issues. The dynamic voltage restorer of unified power quality controller (UPQC) is designed with a unit vector control algorithm and the distribution static synchronous compensator (DSTATCOM) is designed with fuzzy logic and adaptive network-based fuzzy inference system based instantaneous reactive power theory control algorithm. The simulation model built using the MATLAB platform includes a three-phase voltage source along with hybrid electric power generation connected to linear and non-linear loads operating under different conditions. The result is analyzed in terms of voltage and current total harmonic distortion compared with IEEE 512 power quality standards and power factor improvement. The paper shows the adaptive neuro-fuzzy inference systems (ANFIS)-based control algorithm gives better results in terms of total harmonic distortion (THD) compared to the fuzzy-based control algorithm. The power factor is improved using ANFIS-based controller proving the efficiency of the controller</span>.

Reliable Event-Triggered Load Frequency Control of Uncertain Multiarea Power Systems With Actuator Failures

Load frequency control (LFC) is crucial for the economic operation and safety of power systems. Therefore this paper addresses the LFC problem for uncertain multi-area power systems with actuator failures. Specifically, actuator failures, uncertainties and communication bandwidth constraints appearing in multi-area power systems are taken into account simultaneously, and novel reliable event-triggered LFC schemes are proposed to cope with these troubles. The proposed schemes can ensure the asymptotical stability of the closed-loop system when only matched uncertainty exists. For the case of coexisting matched and mismatched uncertainties, the state trajectories of the closed-loop system can be controlled within a bounded set, where the size of the bounded set is only related to the mismatched uncertainty. To illustrate the theoretical results, a numerical example of three-area interconnected power system is presented. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Load frequency directly affects the quality of electric energy and is one of the main observation states of power systems, hence LFC has been widely investigated in the literature. For multi-area power systems, the system model to be controlled may be subjected to multiple unfavorable factors in practical situations, such as limited bandwidths, model uncertainties and actuator failures. To cope with these unfavorable factors, this paper is devoted to developing a unified control framework to guarantee the stability of the frequency deviation based on the event-triggered mechanism. Considering both matched and unmatched system uncertainties may exist as well as the bound of system uncertainties can be unknown, event-triggered control schemes including static event-triggered LFC and adaptive event-triggered LFC are accordingly designed to deal with aforementioned situations such that the closed-loop system is asymptotically or boundedly stable. The research outcome of this paper provides simple but effective LFC approaches that can be used to maintain the reliable and stable operation of multi-area power systems.

A Unified Controller for Natural Ambulation on Stairs and Level Ground with a Powered Robotic Knee Prosthesis.

Powered lower-limb prostheses have the potential to improve amputee mobility by closely imitating the biomechanical function of the missing biological leg. To accomplish this goal, powered prostheses need controllers that can seamlessly adapt to the ambulation activity intended by the user. Most powered prosthesis control architectures address this issue by switching between specific controllers for each activity. This approach requires online classification of the intended ambulation activity. Unfortunately, any misclassification can cause the prosthesis to perform a different movement than the user expects, increasing the likelihood of falls and injuries. Therefore, classification approaches require near-perfect accuracy to be used safely in real life. In this paper, we propose a unified controller for powered knee prostheses which allows for walking, stair ascent, and stair descent without the need for explicit activity classification. Experiments with one individual with an above-knee amputation show that the proposed controller enables seamless transitions between activities. Moreover, transition between activities is possible while leading with either the sound-side or the prosthesis. A controller with these characteristics has the potential to improve amputee mobility.

Divided government, polarization, and policy: Regression-discontinuity evidence from US states

In this paper we use data on US states and a regression-discontinuity design to study how divided government affects the polarization of the legislature, inter-branch conflict, and policy implementation. We document that Republican legislators serving under a divided government have more conservative ideologies than those serving under a fully unified government. Correspondingly, Democrats have more liberal ideologies. In terms of policy implementation, however, we find evidence of moderation: compared to unified Republican governments, divided ones with a democratic governorship or senate implement more liberal policies. When Democrats lose unified control, instead, policies become more conservative.

Finite-time stochastic integral input-to-state stability and its applications

In this paper, finite-time stabilization is investigated in depth for stochastic nonlinear systems with stochastic inverse dynamics. The contributions of this work are characterized by the following novel features: (i) Motivated by the concept of stochastic integral input-to-state stability (SiISS) and the existing stochastic finite-time stability theorem, a new concept of finite-time stochastic integral input-to-state stability (FT-SiISS) using Lyapunov function is first introduced, and an inclusion relationship among three types of stochastic stability, i.e., FT-SiISS, FT-SISS and SISS, is rigorously established. On the basis of FT-SiISS, we develop two new FT-SiISS small-gain conditions and discuss their relationship. Then, to analyze finite-time stability of stochastic nonlinear systems with FT-SiISS inverse dynamics, a stochastic finite-time stability theorem is improved. (ii) As the application of (i), we propose a unified finite-time control method, which can simultaneously handle stochastic nonlinear systems with FT-SiISS or FT-SISS inverse dynamics, and guarantee that the closed-loop system has an almost surely continuous solution, all the closed-loop signals are bounded almost surely, and its trivial solution is stochastically finite-time stable.

Maximizing ozone control by spatial sensitivity-oriented mitigation strategy in the Pearl River Delta Region, China

The Pearl River Delta (PRD) has long been plagued by severe O3 pollution, particularly during the autumn. A regional O3 pollution episode influenced by the Western Pacific Subtropical High in September 2021 was characterized by near-surface O3 escalation due to strong photochemical reactions within the planetary boundary layer. This event was targeted to develop effective control strategies through investigation of precursor control type and scope based on the high-order decoupled direct method (HDDM) and integrated source apportionment method (ISAM) of CMAQ. Generally, the majority of areas (67.0 %) were under NOx-limited regime, which should strengthen afternoon NOx control inferred by positive convex O3 responses. However, high emission and heavily polluted areas located in central PRD were under VOC-limited regime (11.6 %) or mixed regime (15.0 %). The remaining areas (6.4 %) were under NOx-titration or insensitive conditions. Regarding source apportionment, Guangdong province contributed 32.3 %–58.4 % to MDA8 O3 of PRD, especially higher proportion (>50 %) to central areas. Overall, local-focused NOx/VOC emission reductions had limited effects on O3 mitigation for receptor cities compared to regional-cooperative regulation. When region-wide VOC emission reduction was implemented, MDA8 O3 in VOC-limited grids exhibited the largest declines (2.3 %–4.1 %, 3.9– 7.0 μg·m−3). However, unified NOx control contributed to increasing MDA8 O3 in VOC-limited grids (most stations located for air quality evaluation) whereas decreased MDA8 O3 by 2.1 %– 5.7 %, 3.0– 8.2 μg·m−3 in large-scale NOx-limited grids. The sensitivity-oriented regional control avoided O3 rebound and achieved the greatest decline of 3.4 %– 5.0 %, 5.7– 8.4 μg·m−3 in VOC-limited grids; additionally, time-refined dynamic aggressive NOx control decreased peak O3 by an extra 1.2– 6 μg·m−3, both of which facilitate the regulation for the forecasting O3 episodes. These findings suggest that in heavily polluted environments, the enhancement of O3 regulation benefits requires meticulous, coordinated, and dynamic NOx and VOC controls spanning the entire region based on high-resolution analysis of heterogeneous O3-NOx-VOC sensitivity. Furthermore, emission reduction gains should be more reasonably reflected through increasing in-situ observations covering multi-sensitivity regions.

Distributed Consensus Seeking With Different Convergence Performance Requirements: A Unified Control Framework.

In this article, we investigate distributed consensus seeking with multiple convergence performance requirements for single-integrator multiagent systems under undirected graphs. A unified distributed control framework is proposed to ensure consensus or practical consensus as well as performance time requirements, which contains most existing complex protocol schemes as special cases. In the proposed framework, three functions with specific properties in the controller play different roles and can be freely designed to achieve desired convergence performances, which guarantee a high-level scalability for multiple control requirements in addition to convergence time. For highlighting the compatibility and flexibility of the proposed method, four typical scenarios are discussed to reach exponential, finite-time, fixed-time, and appointed-time consensus seeking, respectively. Finally, numerical simulations are carried out to verify the effectiveness of the theoretical analysis.

A unified power flow controller-based robust damping controller considering time delay in electrical power systems

Nowadays, different power systems are connected to each other due to technical and economic reasons and form a complex developed network. Such networks measure and send system data to decision-making centers for control and protection. This information transmission is accompanied by a delay and causes the performance of the system’s damping controllers to be affected. To improve power system dynamic stability, the supplementary controller in flexible alternating current transmission system (FACTS) devices is designed to account for information transmission delays. In these methods, to increase the controllability and observability of the inter-area mode, the remote signals extracted from the wide area measurement system (WAMS) are used as the input of the modulator. WAMS systems’ time delay reduces power system stability and even causes instability, so it’s crucial to find the maximum delay margin that ensures stability. Therefore, it is necessary to carefully study the role of FACTS tools in stabilizing the power system and damping interregional fluctuations, considering the delay. This research shows that supplementary controller design can dampen frequency and load angle fluctuations in multi-zone power systems despite information transmission delays. The method works in power system analysis toolbox (PSAT) simulation and MATLAB programming.

Toward Unified Adaptive Teleoperation Based on Damping ZNN for Robot Manipulators With Unknown Kinematics

Traditional teleoperation systems are usually designed for a single class of slave robots and require the specific kinematic model. This leads to the problems of high computational cost, poor robustness, and poor portability in traditional teleoperation systems. In this paper, a unified adaptive teleoperation system is proposed for different kinds of slave robots. To this end, a closed-loop control system based on damping zeroing neural network (DampZNN) is proposed to achieve the unified kinematic control of both the redundant robot and the continuum robot with unknown kinematics. The control system is constructed based on two DampZNNs, which are utilized to estimate the unknown kinematic model and to solve the inverse kinematics problem respectively. By combining the DampZNN control system with teleoperation technology, a unified adaptive teleoperation system is proposed for human operators to teleoperate both the redundant slave robot and the continuum slave robot easily. The efficacy of the proposed teleoperation system is verified by experiments based on different slave robots and the advantages of the proposed teleoperation system are revealed by comparative studies.