Closed-loop Control Research Articles

Stability analysis of lateral dynamics of a vehicle model utilizing state feedback with integral control and bifurcation method

In this work, a novel control strategy is proposed to obtain the stability boundary in addition to reduce the transients around the equilibrium points. To encounter the described problem, a new approach of combining the bifurcation analysis with the state feedback controller is proposed. A bifurcation analysis at different equilibrium points is performed to obtain the stable region of operation. In addition to this, the transients behaviour of the system is also obtained simultaneously in the form of eigenvalues plots. The objective of the proposed controller is to generate a control law and state variables to reduce the transients keeping the system within the stability boundary by tuning the reference input matrix. From the obtained simulation results, it is seen that, by combining the bifurcation analysis with state feedback controller, the transients and the steady state error are reduced by selecting the purely negative real eigenvalues and reference input matrix respectively. The obtained closed loop control law and the state variables utilizing Ackerman’s Formula are found within the stability limit. A sensitivity index obtained from local sensitivity analysis verifies the relationship between the stability boundary at different longitudinal velocity on a low-friction road obtained from bifurcation analysis.

Self-Calibration Method of Displacement Sensor in AMB-Rotor System Based on Magnetic Bearing Current Control

Displacement sensors are the key components of the active magnetic bearing rotor (AMB-rotor) system to realize the closed-loop control of the rotor displacement. The aging and failure of the displacement sensor components will lead to the changes of sensor sensitivity and zero point, which can directly affect the suspension performance of the rotor and even cause the instability of the AMB-rotor system. Therefore, it is significant to calibrate the sensor regularly. This paper proposes a self calibration method based on the detection and control of the magnetic bearing current without additional calibration equipment and hardware circuits. In addition, a new control structure of the AMB-rotor system is proposed to achieve efficient calibration, which can control the rotor displacement based on current following control method. Finally, experimental results verify the effectiveness of the proposed method on the displacement sensor calibration.

Reinforcement learning for decision-making under deep uncertainty

Planning under complex uncertainty often asks for plans that can adapt to changing future conditions. To inform plan development during this process, exploration methods have been used to explore the performance of candidate policies given uncertainties. Nevertheless, these methods hardly enable adaptation by themselves, so extra efforts are required to develop the final adaptive plans, hence compromising the overall decision-making efficiency. This paper introduces Reinforcement Learning (RL) that employs closed-loop control as a new exploration method that enables automated adaptive policy-making for planning under uncertainty. To investigate its performance, we compare RL with a widely-used exploration method, Multi-Objective Evolutionary Algorithm (MOEA), in two hypothetical problems via computational experiments. Our results indicate the complementarity of the two methods. RL makes better use of its exploration history, hence always providing higher efficiency and providing better policy robustness in the presence of parameter uncertainty. MOEA quantifies objective uncertainty in a more intuitive way, hence providing better robustness to objective uncertainty. These findings will help researchers choose appropriate methods in different applications.

Non-trivial relationship between behavioral avalanches and internal neuronal dynamics in a recurrent neural network.

Neuronal activity gives rise to behavior, and behavior influences neuronal dynamics, in a closed-loop control system. Is it possible then, to find a relationship between the statistical properties of behavior and neuronal dynamics? Measurements of neuronal activity and behavior have suggested a direct relationship between scale-free neuronal and behavioral dynamics. Yet, these studies captured only local dynamics in brain sub-networks. Here, we investigate the relationship between internal dynamics and output statistics in a mathematical model system where we have access to the dynamics of all network units. We train a recurrent neural network (RNN), initialized in a high-dimensional chaotic state, to sustain behavioral states for durations following a power-law distribution as observed experimentally. Changes in network connectivity due to training affect the internal dynamics of neuronal firings, leading to neuronal avalanche size distributions approximating power-laws over some ranges. Yet, randomizing the changes in network connectivity can leave these power-law features largely unaltered. Specifically, whereas neuronal avalanche duration distributions show some variations between RNNs with trained and randomized decoders, neuronal avalanche size distributions are invariant, in the total population and in output-correlated sub-populations. This is true independent of whether the randomized decoders preserve power-law distributed behavioral dynamics. This demonstrates that a one-to-one correspondence between the considered statistical features of behavior and neuronal dynamics cannot be established and their relationship is non-trivial. Our findings also indicate that statistical properties of the intrinsic dynamics may be preserved, even as the internal state responsible for generating the desired output dynamics is perturbed.

Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization

Laser directed-energy deposition (DED) offers notable advantages in additive manufacturing (AM) for producing intricate geometries and facilitating material functional grading. However, inherent challenges such as material property inconsistencies and part variability persist, predominantly due to its layer-wise fabrication approach. Critical to these challenges is heat accumulation during DED, influencing the resultant material microstructure and properties. Although closed-loop control methods for managing heat accumulation and temperature regulation are prevalent in DED literature, few approaches integrate real-time monitoring, physics-based modeling, and control simultaneously in a cohesive framework. To address this, we present a digital twin (DT) framework for real-time model predictive control of process parameters of the DED for achieving a specific process design objective. To enable its implementation, we detail the development of a surrogate model utilizing Long Short-Term Memory (LSTM)-based machine learning which uses Bayesian Inference to predict temperatures across various spatial locations of the DED-built part. This model offers real-time predictions of future temperature states. In addition, we introduce a Bayesian Optimization (BO) method for Time Series Process Optimization (BOTSPO). Its foundational principles align with traditional BO, and its novelty lies in our unique time series process profile generator with a reduced dimensional representation. BOTSPO is used for dynamic process optimization in which we deploy BOTSPO to determine the optimal laser power profile, aiming to achieve desired mechanical properties in a DED build. The identified profile establishes a process trajectory that online process optimizations aim to match or exceed in performance. This paper elucidates components of the digital twin framework, advocating its prospective consolidation into a comprehensive digital twin system for AM.

Design and implementation of high-rigidity and high-precision satellite antenna rotating unit

The satellite antenna rotating unit is an important component that drives the satellite antenna to rotate in orbit, mainly composed of a stepper motor, harmonic reducer, and rotating transformer. This type of rotating unit has the advantages of small size, lightweight, high-rated output torque, and long rotating life. The high-stiffness and high-precision satellite antenna rotation unit is based on the existing antenna rotation unit. Through comprehensive improvement design, the measured closed-loop control has a rotation accuracy of better than 0.005°, a torsional stiffness of better than 6.5 Nm/(Å), a rotation accuracy of 6 times higher than the existing antenna rotation unit, a torsional stiffness increase of 13 times, and a rated output torque increase of 2.4 times. The article introduces the development of components, measurement of torsional stiffness, and measurement of rotational accuracy for the high-stiffness and high-precision satellite antenna rotating unit. Finally, the development conclusion is given. The successful development of a high-stiffness and high-precision satellite-borne antenna rotation unit can not only meet the precise pointing control requirements of the mechanical movable antennas and terahertz antennas of the narrow beam with a width of 0.2° but also solve practical problems such as jitter in ground testing of data transmission or relay antennas and unstable pointing of point beam antennas in orbit.

Revolutionizing motor dysfunction treatment: a novel closed-loop electrical stimulator guided by multiple motor tasks with predictive control

Functional electrical stimulation (FES) has been demonstrated as a viable method for addressing motor dysfunction in individuals affected by stroke, spinal cord injury, and other etiologies. By eliciting muscle contractions to facilitate joint movements, FES plays a crucial role in fostering the restoration of motor function compromised nervous system. In response to the challenge of muscle fatigue associated with conventional FES protocols, a novel biofeedback electrical stimulator incorporating multi-motor tasks and predictive control algorithms has been developed to enable adaptive modulation of stimulation parameters. The study initially establishes a Hammerstein model for the stimulated muscle group, representing a time-varying relationship between the stimulation pulse width and the root mean square (RMS) of the surface electromyography (sEMG). An online parameter identification algorithm utilizing recursive least squares is employed to estimate the time-varying parameters of the Hammerstein model. Predictive control is then implemented through feedback corrections based on the comparison between predicted and actual outputs, guided by an optimization objective function. The integration of predictive control and roll optimization enables closed-loop control of muscle stimulation. The motor training tasks of elbow flexion and extension, wrist flexion and extension, and five-finger grasping were selected for experimental validation. The results indicate that the model parameters were accurately identified, with a RMS error of 3.83% between actual and predicted values. Furthermore, the predictive control algorithm, based on the motor tasks, effectively adjusted the stimulus parameters to ensure that the stimulated muscle groups can achieve the desired sEMG characteristic trajectory. The biofeedback electrical stimulator that was developed has the potential to assist patients experiencing motor dysfunction in achieving the appropriate joint movements. This research provides a foundation for a novel intelligent electrical stimulation model.

Novel Magnetization State Closed-Loop Control for Variable Flux Memory Machine

Novel Magnetization State Closed-Loop Control for Variable Flux Memory Machine

Dynamic model and robust control for the PEM fuel cell systems

In response to the escalating challenges posed by climate change, the global energy sector has witnessed a paradigm shift towards sustainable alternatives. The promising fuel cell technology known as the proton exchange membrane fuel cell (PEMFC) has found widespread use in a variety of mobile and stationary applications. This paper presents a super-twisting sliding mode (STSM) control for maximum power point tracking (MPPT) on the proton exchange membrane fuel cell (PEMFC) system incorporated floating interleaved boost converter (FIBC). This work aims to extract the maximum power from the PEMFC by means of robust control in conjunction with FIBC to improve current ripple. The proposed controller is designed for the PEMFC system by combining the STSM and MPPT methods. The designed MPPT control tracks down the maximum power point of the system, and the corresponding current values act as the reference values for the STSM controller. The results show that incorporating the PEMFC with the FIBC can result in current ripple reduction when compared with a traditional interleaved boost converter (IBC). The obtained results demonstrate the capability of the PEMFC closed-loop control system to maintain the system’s robustness under fuel cell parameter variations.

Research on VIENNA rectifier QPR control and soft start

The principle and operation of the VIENNA rectifier is examined and the VIENNA mathematical model is developed. To address the issue that the three-phase VIENNA rectifier’s double PI controller finds it challenging to attain both rapidity and accuracy, a double closed-loop control approach consisting of inner loop quasi-proportional resonant control (QPR) and outer loop PI control is created. Aiming at the problems of inrush current on the grid side and DC bus voltage overshoot caused by the VIENNA rectifier in the startup process, the paper makes a theoretical analysis and designs a soft startup strategy. Then, the simulation model of the three-phase VIENNA rectifier control system was built in Simulink, which verified the correctness of the QPR control strategy and soft start scheme. Finally, an 8-kW prototype is set up, and the experimental results further confirm the effectiveness of the slope setting of the current.

Review on a Power Quality Issues of a Distribution Transformer with Connected Load

In this paper a Voltage control system for power distribution which can solve the power quality problem in distribution. the proposed system consists of a frequency transformer connected to a power transformer receiving connection from both sides. this automatic connection is achieved using high or medium frequency transformers. The input of the converter is single-phase alternating current and the output is single-phase alternating current with different voltage amplitudes. the output voltage is stabilized by a closed-loop controller that controls the voltage at one level. The proposed system meets the smart distribution in terms of improved ability, protection of equipment and efficiency

A Novel and Self-Calibrating Weighing Sensor with Intelligent Peristaltic Pump Control for Real-Time Closed-Loop Infusion Monitoring in IoT-Enabled Sustainable Medical Devices

Technological advancements are propelling medical technology towards automation through the application and widespread use of automatic control, sensing, and Internet of Things (IoT) technologies. Currently, IoT technology has been extensively applied in medical devices, aiming to ensure patient safety through more real-time detection and more effective management. In the monitoring of intravenous infusion, accurately sensing the infusion conditions in real time is particularly important. This article introduces a low-cost smart infusion device based on IoT technology, which controls the infusion rate with a peristaltic pump and monitors the volume of fluid delivered. It uses an improved, self-calibrating weighing sensor to achieve the real-time closed-loop control of the flow rate, ensuring patient safety. Additionally, the Blynk dashboard can be used for monitoring and controlling the flow rate and infusion volume.

PMSM motor drive and their control schemes

This paper investigated different control Techniques of Permanent magnet synchronous motor (PMSM) drive with open loop, voltage/frequency (V/F) control, closed-loop Space Vector Pulse Width Modulation (SVPWM) control, and sensor-less Modified Space Vector Pulse Width Modulation (MSVPWMM) control with MATLAB/Simulink. The different applications of commercial used required different control schemes due to cost-effectiveness. In a small commercial application where not much precision is required, open-loop or V/F control is used, due to their easy calculation and implementation and becomes a low-cost product outcome. Where their high precision has required closed-loop control is being adopted. In this paper, closed-loop SVPWMM and sensor-less MSVPWM techniques are proposed. The algorithm has a very fast response and output has fewer glitches, highly efficient. Constant speed response available at desired load. The control techniques have been modeled and simulated in MATLAB simulation software. The proposed scheme provides efficient performance in steady-state and dynamic load conditions.

Fault-tolerant control for satellite autonomous rendezvous with quality characteristics and actuator uncertainties

An autonomous fault detection and fault-tolerant control solution combining Model Predictive Control (MPC) and Label Distributed Learning (LDL) is developed for the position tracking and attitude synchronization problems in autonomous satellite rendezvous and docking. The proposed algorithm can deal with center of mass (CM) variations and time-varying mass characteristics due to satellite fuel consumption, actuator saturation and fault, and external disturbances. Firstly, a six-degree-of-freedom (6-DOF) satellite translation-rotation coupling model was established to express the relative motion between the satellite and the target, taking the above factors into account. Subsequently, an on-board autonomous closed-loop fault-tolerant control algorithm using MPC is proposed, in which an LDL-based fault detection mechanism is embedded. The scheme specifies that propulsion faults are estimated by calculating the difference between the MPC's prediction of the satellite's future state and the measured change in the actual state. And the mapping matrix between the commanded force/torque and the actual force of the propulsion system is updated in real time to achieve autonomous fault detection and compensation on board the satellite. Among them, the LDL-based fault detection scheme is trained offline. It requires only simple matrix operations for on-orbit use, which is low-cost and suitable for resource-limited space environments. The additional creation of a time-varying disturbance observer allows the estimation and compensation of unmodelled errors and external disturbances. The stability of the proposed fault-tolerant control algorithm is rigorously demonstrated using Lyapunov analysis. Finally, numerical simulations show that the proposed method can successfully achieve autonomous satellite docking under different combinations of thruster faults.

Scalable computation of energy functions for nonlinear balanced truncation

Nonlinear balanced truncation is a model order reduction technique that reduces the dimension of nonlinear systems in a manner that accounts for either open- or closed-loop observability and controllability aspects of the system. A computational challenges that has so far prevented its deployment on large-scale systems is that the energy functions required for characterization of controllability and observability are solutions of various high-dimensional Hamilton–Jacobi–(Bellman) equations, which are computationally intractable in high dimensions. This work proposes a unifying and scalable approach to this challenge by considering a Taylor-series-based approximation to solve a class of parametrized Hamilton–Jacobi–Bellman equations that are at the core of nonlinear balancing. The value of a formulation parameter provides either open-loop balancing or a variety of closed-loop balancing options. To solve for the coefficients of Taylor-series approximations to the energy functions, the presented method derives a linear tensor system and heavily utilizes it to numerically solve structured linear systems with billions of unknowns. The strength and scalability of the algorithm is demonstrated on two semi-discretized partial differential equations, namely the Burgers and the Kuramoto–Sivashinsky equations.

A Low-Voltage Self-Starting Boost Converter Using MPPT with Pulse Multiplication for Energy Harvesting

A single-inductor, low-voltage, three-step self-starting boost converter is proposed for photovoltaic (PV) energy harvesting. In order to enhance energy transfer efficiency, a variable-step Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) scheme has been devised based on a novel pulse multiplication technique. Upon overcoming the speed and accuracy limitations, the maximum power point (MPP) of the PV model is accurately tracked. In the boost converter, the average inductor current is utilized to implement closed-loop control of the MPPT loop, enhancing the stability of the tracking process and enabling efficient energy transmission. Finally, the boost converter is implemented using a 0.18 μm CMOS process, which is capable of self-starting and maintaining stable operations at input voltages ranging from 90 mV to 300 mV, achieving a peak efficiency of 93%.

Bidirectional DC - DC Converter Topology for Electric Vehicles Using Fuzzy Logic Controller

This project organizes an application of hybrid electric vehicle systems operated with novel designed bidirectional dc-dc converter (BDC) which interfaces a main energy storage (ES1), an auxiliary energy storage (ES2) and dc bus of different voltage levels. Proposed BDC converter can operate both step up and step-down mode. In which step up mode represents low voltage dual source -powering mode and step-down mode represents high voltage dc link energy –regenerating mode, both the modes are operated under the control of bidirectional power flow. This model can independently control power flow between low voltage dual source buck/boost modes. Here in, the circuit configuration, operation, steady-state analysis, and closed-loop control of the proposed BDC are discussed according to its three modes of power transfer. In this project fuzzy logic controller is used and also system results are validating through MATLAB/SIMULINK software. Index Terms—Bidirectional dc/dc converter (BDC), dual battery storage, hybrid electric vehicle, Fuzzy logic controller.

Adaptive optimal tracking control of networked linear systems under two-channel stochastic dropouts

This work investigates the adaptive optimal tracking control problem for networked discrete-time linear systems by directly using the data transmitted via communication networks. It is shown that the concerned problem can be addressed by solving two sub-problems: an adaptive optimal control one and an adaptive output regulation one. Two novel online reinforcement learning based policy iteration and value iteration algorithms are developed, which constitute an integrated framework to learn the optimal feedback control gain and the solutions to regulator equations by directly using the data transmitted via communication networks. Furthermore, it is shown that the tracking error of the closed-loop control system is mean square asymptotically stable even in the case of network packet stochastic dropouts. Simulation results demonstrate the efficacy of the proposed approaches.

An MFC-based friction damper with adjustable normal force: conception, modelling, and experiment

For classic friction dampers, the normal force on the contact interface is designed to be constant and cannot be easily adjusted post-manufacturing. The damping performance will attenuate when the working condition deviates from the target one. To overcome this issue, semi-active friction dampers are developed, where the normal force can be regulated by actuators to match the current working condition. However, the additional mass brought by the actuators limits their application prospects in the aerospace field. In this work, we propose a macro-fiber-composite-based adjustable friction damper (MFC-AFD) for typical thin-walled structures in aircrafts, which has the advantages of being lightweight, easy to install, and inherently fail-safe. The idea is to use the embedded MFC to drive the flexible structure of the damper to deform, thereby regulating the normal force. By adjusting the constant component of the normal force, the effective working range is expected to be enlarged, especially under non-target working condition. By controlling the second harmonic component, the optimal damping performance is expected to be further improved. We conduct the research using a combination of numerical simulations and experiments. To accurately and efficiently predict the damping performance of the MFC-AFD, a homogenization modeling method of MFC is proposed, and the corresponding experimental calibration technique is given. The harmonic balance-based nonlinear solver specifically for calculating the steady-response of electromechanically coupled structures is developed. Concerning the constant normal force control, a step-back to zero control law is established to eliminate the hysteresis behavior of MFC. In terms of the time-varying normal force control, a closed-loop control strategy in the frequency domain for the second harmonic component is proposed and realized. The vibration reduction effects of the MFC-AFD adopting the constant and time-varying normal force control laws are validated experimentally through a cantilever beam. Experiment results are in line with our expectations. When the vibration level is nearly 10 times of the target one, the reduction ratio recovers to 75% (i.e. close to the target working condition) by using the constant normal force control. There is an additional 32% vibration reduction compared to the optimal constant normal force when the time-varying normal force is properly controlled.

Hardware-in-the-loop dynamic load emulation of robotic systems actuated by fluidic artificial muscles

Hardware-in-the-loop (HIL) testing is a popular control system testing method because it bridges the gap between modeling/simulation and experiments. Instead of designing a full hardware-based experiment to validate the results of a simulation, the plant hardware can be replaced with an emulator device that responds to exogenous inputs and effectively emulates the dynamic behavior of a system. This approach can be more cost-effective and modular, since the emulated plant system can be modeled in a simulation environment, implemented on a simplified piece of hardware and changed quickly without having to fabricate new parts. This paper develops the hardware and control scheme for a certain type of HIL device called a dynamic load emulator that consists of a 1-DOF linear hydraulic dynamometer equipped with in-line sensing to measure both its own position and the force exerted on it by a device-under-test. This measured force is passed to a real-time model of the emulated dynamic system. The model outputs the emulated system position, and a closed-loop controller is used to emulate this position. The emulator controller incorporates both model-based feedforward and standard feedback PI control. This paper characterizes the dynamometer-based dynamic load emulator and its controller, determining its hardware limitations and validating its capabilities when experiencing a force input from a linear spring with known parameters. Additionally, this paper demonstrates the ability of the emulator to represent the dynamics of a 1-DOF robotic joint when actuated by a pair of fluidic artificial muscles (FAMs). The primary contribution of this work is to allow for more comprehensive testing of FAM configurations, topologies, and controllers for a wide range of parameters, because the same hardware can be used to emulate multiple systems. As a result, this work will lead to more cost-effective, time-efficient, and energy-efficient designs of robotic systems and the FAMs used to actuate them.