State-space Control Research Articles

Applied Metal Hydride System Optimization to Improve Hydrogen Fuel Cell System Performance in Mobile Heavy-Duty Applications

Hydrogen fuel cell vehicles face challenges in competing against battery electric systems as their efficiency is 25 % lower. Especially in the heavy-duty sector the efficiency of fuel cell electric vehicles needs to be increased accordingly, as this market will play a major role in future. One promising approach could be to decrease the energy demand of refrigerated trucks by the pressure energy between the hydrogen tank and fuel cell in metal hydrides to provide cooling power. However, the cooling power output is not constant and further efficiency improvements can be done. Here we show a simulative state space controller optimization which can provide a constant power output and can increase the efficiency by 8 % points compared to an uncontrolled system. A future combination with state-of-the-art efficiency improvement measures can potentially increase the system efficiency even further.

The Supplementary Damping Method for CLCC HVDC Based on Projective Control Theory

This paper proposes a design method for an additional damping reduced-order controller based on the projective control theorem. Improvements are made to the projective theorem to achieve additional damping control through Controllable-Line-Commutated Converter (CLCC)-based High Voltage Direct Current (HVDC), which suppresses low-frequency oscillations. Using small disturbance identification techniques, the system’s linear model is first identified, and the state feedback control law of the system is then determined using the state-space pole assignment control method. Then, by retaining the dominant oscillation modes of the closed-loop system through the improved projective theorem, the state feedback is converted into output feedback. Ultimately, the sixth-order state feedback controller is reduced to a second-order one and applied in the CLCC HVDC additional damping strategy to suppress low-frequency oscillations. Simulation results based on the electromagnetic transient simulation software PSCAD demonstrate that the designed CLCC HVDC additional damping reduced-order projective control exhibits good suppression performance, strong robustness, and low order, which are of significant importance for engineering practice.

Development of a fed-batch-scale anaerobic co-digestion control system based on multivariable output error state space and model predictive control

This paper describes a feeding control system for fed-batch-scale anaerobic co-digestion. The proposed system is based on the multivariable output error state space and uses model predictive control to regulate the biogas flow output of anaerobic co-digestion. This control system combines predictive modeling, receding-horizon optimization, and state feedback. An accurate linear model of anaerobic co-digestion is obtained through the multivariable output error state space method using experimental data, and the accuracy of the model is evaluated using a goodness-of-fit index and the root mean square error. After augmenting the model, a predictive control system is established. The system states are observed through a Kalman filter, and the constrained receding-horizon optimization problem is solved using quadratic programming. Finally, the stability of the control system is demonstrated from both open- and closed-loop perspectives. The control system is then constructed and used in actual experiments, with the target flow output set at 100 NmL/h. The final flow output indicates that the control system achieves good control performance. This research enables the biogas output of anaerobic co-digestion to be controlled from a linear perspective through a concise control algorithm with strong practical application prospects.

Improved Model Predictive Speed Control of a PMSM via Laguerre Functions

This paper proposes a model predictive speed control strategy for a surface-mounted permanent magnet synchronous motor by applying Laguerre functions. The model predictive controller (MPC) incorporates an integrator. A quadratic programming procedure is applied to solve the constrained optimization problem online. The paper also provides a solution for stability. The performance efficiency of the proposed scheme is validated by comparing the results with the performance of an optimal linear quadratic regulator, conventional state-space model predictive control, and a simple MPC algorithm with integral action. Extensive simulation results confirm the efficacy of the proposed scheme, showing that it achieves good steady-state performance while maintaining a fast dynamic response.

Reaction Force-Based Position Sensing for Magnetic Levitation Platform with Exceptionally Large Hovering Distance

This work introduces a novel sensing concept based on reaction forces for determining the position of the levitating magnet (mover) for magnetic levitation platforms (MLPs). Besides being effective in conventional magnetic bearings, the applied approach enables operation in systems where the mover is completely isolated from the actuating electromagnets (EMs) of the stator (e.g., located inside a sealed process chamber) while levitating at an extreme levitation height. To achieve active position control of the levitating mover by properly controlling the stator’s EM currents, it is necessary to employ a dynamic model of the complete MLP, including the reaction force sensor, and implement an observer that extracts the position from the force-dependent signals, given that the position is not directly tied to the measured forces. Furthermore, two possible controller implementations are discussed in detail: a basic PID controller and a more sophisticated state-space controller that can be chosen depending on the characteristics of the MLP and the accuracy of the employed sensing method. To show the effectiveness of the proposed position-sensing and control concept, a hardware demonstrator employing a 207 mm outer-diameter (characteristic dimension, CD) stator with permanent magnets, a set of electromagnets, and a commercial multi-axis force sensor is built, where a 0.36 kg mover is stably levitated at an extreme air gap of 104 mm.

A new generalized state-space averaged model, control design and stability analysis for three phase grid-connected quasi-Z-Source inverters

A comprehensive dynamic model of the three-phase grid-connected quasi Z-Source inverter (qZSI) with LCL filter is presented based on the generalized state-space averaging (GSSA) method. The developed model mathematically describes (i) the dynamic interaction between DC and AC side variables of converter and (ii) good dynamical precision that ensures a good description of the input–output dynamics, as well as the internal. Then, based on the developed model, a cascaded PI controller by nesting multi-control loops to regulate DC side capacitor voltage and inductor current is proposed. The PI controllers determine the grid and inverter current references in the d- and q-axes by regulating the DC side capacitor voltage. An LMI approach for stability analysis of the proposed controller, reliability calculations and switching losses are investigated. The accuracy of the developed qZSI inverter model and the effectiveness of proposed controls are verified by time-domain simulations of a study system in the MATLAB/SIMULINK environment and hardware-in-the-loop (HIL) simulation.

Bayesian state-space synthetic control method for deforestation baseline estimation for forest carbon credits

Abstract Carbon credits from the reducing emissions from deforestation and degradation (REDD+) projects have been criticized for issuing junk carbon credits due to invalid ex-ante baselines. Recently, the concept of ex-post baseline has been discussed to overcome the criticism, while ex-ante baseline is still necessary for project financing and risk assessment. To address this issue, we propose a Bayesian state-space model that integrates ex-ante baseline projection and ex-post dynamic baseline updating in a unified manner. Our approach provides a tool for appropriate risk assessment and performance evaluation of REDD+ projects. We apply the proposed model to a REDD+ project in Brazil and show that it may have had a small, positive effect but has been overcredited. We also demonstrate that the 90% predictive interval of the ex-ante baseline includes the ex-post baseline, implying that our ex-ante estimation can work effectively.

Model based optimal magnetic feedback control of multiple resistive wall modes with discrete coil and sensor arrays

A model-based optimal control method for multiple resistive wall mode (RWM) feedback stabilization has been developed and tested in plasma experiments at the EXTRAP T2R reversed field pinch (RFP) device. The controller is designed to target issues that arise in connection with RWM magnetic feedback stabilization systems based on discrete control coil and sensor arrays in tokamak and reversed field pinch devices. Multi-mode control capabilities in these systems is limited due to coupling of modes induced by the control system. The coupling originates from the generation of side-band control field harmonics and from aliasing of multiple harmonics in the sensor measurements. These couplings naturally leads to a multiple-input, multiple-output (MIMO) control problem. A model based state space controller has been designed based on a relatively simple physics model of the RWM plasma response. The physics model, which is applicable for the high aspect ratio RFP, is the basis for Fourier decoupling of the MIMO control problem into a set of single-input, multiple-output (SIMO) systems using the discrete Fourier transform (DFT). The linear, time-invariant physics model allows for the design of a state space model with states representing physical quantities; in this case the Fourier harmonics of the radial field at the resistive wall. Since the states cannot be directly measured, a Kalman filter is used for estimation of the states from the aliased sensor array measurements. A linear–quadratic (LQ) optimal state controller has been implemented. Design parameters, such as the LQ control cost function state weights and the Kalman filter input error covariances have been used to optimize the control operation in various ways. The controller has enhanced multi-mode control capabilities compared to earlier designs. For example it allows the prioritizing of suppression of one of the multiple magnetic field Fourier harmonics produced by a given control current DFT component. The controller has been tested in plasma experiments at EXTRAP T2R device, utilizing a newly installed extended sensor array, and the enhanced capabilities for multiple RWM feedback stabilization has been demonstrated.

DESIGN And IMPLEMENTATION Of HYDRIC CONTROLLER For TWOWHEELED ROBOT

The two-wheeled robotic machine (TWRM) with five degrees of freedom (DOF) is a type of mobile robot that consists of two wheels and a body that can rotate and move in different directions. The proposed architecture of the TWRM in this paper features five DOFs, which allows for greater flexibility and range of motion. However, this also makes controlling the system more challenging, as thecenter of mass (COM) changes while performing tasks in multiple directions. To address this issue, the study utilizes a state-space model created by linearizing the non-linear modeling equations at the equilibrium point using the Lagrangian modeling. To stabilize the TWRM, several controllers were evaluated, including PID, LQR, and LQR with PID methods. The PID controller is a popular feedbackcontrol strategy that adjusts the control output based on the error between the desired and actual values of the systemoutput. LQR is a state-space control technique that aims to minimize a cost function that expresses the system's performance criteria. The PID with LQR technique combines the strengths of both strategies, with PID used as a feed-forward to control the intermediate body (IB) and end-effector and LQR used as state feedback to regulate all states. The controllers were evaluated under variouscircumstances, including disturbance signals, trackingpathways, and moving actuators. Simulation results showedthat the LQR with LQR controller performed better thanthe other two control systems in terms of least overshoot,rising time, and applied input forces. This suggests that thePID with LQR technique is a robust and effective controlstrategy for stabilizing the TWRM and improving itsperformance for various tasks.

Hierarchy of Quadratic Lyapunov Functions for Linear Time-Varying and Related Systems

A method for constructing homogeneous polynomial Lyapunov functions is presented for linear time-varying or switched-linear systems and the class of nonlinear systems that can be represented as such. The method uses a simple recursion based on the Kronecker product to generate a hierarchy of related dynamical systems, whose first element is the system under study and the second element is the well-known Lyapunov differential equation. It is then proven that a quadratic Lyapunov function for the system at one level in the hierarchy, which can be found via semidefinite programming, is a homogeneous polynomial Lyapunov function for the system at the base level in the hierarchy. Searching for Lyapunov functions of the foregoing kind is equivalent to searching for homogeneous polynomial Lyapunov functions via the formulation of sum-of-squares programs. The quadratic perspective presented in this paper enables the easy development of procedures to compute bounds on pointwise-in-time system metrics, such as peak norms, system stability margins, and many other performance measures. The applications of the theory to analyzing an aircraft model, on the one hand, and an experimental aerospace vehicle, on the other hand, are presented. The theory can be comprehended with a first course on state-space control systems and an elementary knowledge of convex programming.

A State-Space Control Approach for Tracking Isometric Grip Force During BMI Enabled Neuromuscular Stimulation

A State-Space Control Approach for Tracking Isometric Grip Force During BMI Enabled Neuromuscular Stimulation

Real-world application of a discrete feedback control system for flexible biogas production

Using renewable energy is increasingly prevalent as part of a global effort to safeguard the environment, with a reduction in {mathrm{CO}}_{2} being one of the primary objectives. A biogas plant provides an opportunity to produce green energy, but its profitability prevents it from being utilized more frequently. A suitable response to this economic issue would be flexible biogas production to exploit fluctuating energy prices. Nevertheless, the complex nature of the anaerobic digestion process that proceeds within the biogas plant and the wide range of substrates that may be utilized as the plant’s feeds make it challenging to achieve flexible biogas production truly. Most plant operators will rely on their experience and intuition to run the plant without knowing exactly how much biogas they will produce with the feed substrate. This work combines a system model estimation and feedback controller to provide an intuitive yet precise feedback control system. The system model estimation represents the biogas plant mathematically, and a total of six distinct approaches have been compared and evaluated. A PT1 model most accurately approximated the step-down and the step-up by the time percentage method, with the Akaike Information Criterion as the primary evaluation criterion for selecting the best model. The downward model was controlled by a discrete PI controller modified with the Root Locus Method and an Anti-Windup scheme, and the upward model was controlled by a state space controller. The quality of the controller was evaluated in both simulation and at the actual biogas plant in Grub, and the controller was able to reduce the biogas production rate approaching the setpoint in the expected period. Furthermore, the developed feedback control system is effortless enough to be installed in many biogas plants.

A synthetic cardiac episode generator for explainable, pathology based action potential using heuristic polynomial signatures

Synthetic generation of biophysical signals caters to diverse applications of test vectors generation, exploration, data augmentation and machine learning training. Arrhythmiator, our contribution is a Python framework to generate synthetically, action potential for explain-ability and explore-ability of pathology (Arrhythmia) influenced cardiac episodes. In this work, we address the problem of large cardiac data-sets need, for deep learning models which are hindered by privacy laws by synthetic generation. We have addressed the problem of explainable computations, multi-scale, by virtue of novel stack sliced polynomial signatures, piece-wise linear and application context driven fitting/filtering in algorithmic framework. To account the problem of lack of standardised pathology specifications we developed PDL (Pathology descriptive language) portal which can be customised. We also addressed the challenge of high turnaround development cycle, inter-operate-ability computation issue by developing a single unified Python framework. To address computational costs, we leverage domain-specific heuristics through sliced state space control algorithms and a constant temporal resolution. We demonstrate the utility of our framework by generating two cases of Tachycardia, one influenced by sodium pump inhibition of spontaneous diastolic depolarisation (SDD) slope and other by ischaemic episode causing maximum diastolic depolarisation (MDP) shift. The concept of the pacemaker hierarchy is encompassed by developing pathology explainable computation as a structure (PECS). We showcase the flexibility and explore-ability of our framework through ease of filter connect profile plug-ins for action potential states upstroke and plateau. We validated the generated waveforms by developing trace and debug algorithms and a reference deviation score which marks the pathology influence. The validator module is a vectored attribute of action potential based on state space control algorithms.

Modelling and stabilizing control of a two-axis gimbal for an unmanned aerial vehicle

In this paper, the modelling and stabilizing control of a two-axis airborne gimbal frame is presented. A combination of analytical as well as statistical techniques are used to model the dynamics of the plant and the actuators. State-space control, Field Oriented Control, non-linear feed-forward compensation as well as Kalman filter sensor fusion are employed in synthesis to control the mechatronic system. Practical methods are used to estimate and validate system variables and parameters. Simulation results are compared to implemented results and the robustness of the control system is verified by means of rigorous parameter variations.

Design and implementation of a state-space controller for an air pressurization plant with slow dynamics and output delay

This paper presents the design of a servo-integrator type state-space digital control structure for an air pressurization plant. The plant is characterized by slow dynamics with output delay. To evaluate the performance of the servointegrator controller, two control structures, namely, STR-PID and conventional PID are tuned and implemented. With the results obtained, a comparative analysis is performed based on metrics related to time response and control effort. At the end, it is evidenced that the Servointegrator type state space controller outperformed the other two controllers, demonstrating that it is an effective strategy that allows controlling systems with slow dynamics and dead time in the output.

A state-space model-based temperature control system for laser remanufacturing molten pool

ABSTRACT Laser remanufacturing is a key technology for remanufacturing, which has been widely used in aerospace, metallurgy, energy and other fields. However, the problem of shape control has been the bottleneck that restricts the scale and industrial application. In the laser remanufacturing process, the molten pool temperature is the critical to determine the shape and performance stability of remanufactured parts. Based on this, the paper proposes a real-time online control method for laser remanufacturing molten pool temperature based on state space model, which solves the problem of difficult to establish control mechanism model and multiple variable constraints. Develop a real-time online control system for molten pool temperature of laser remanufacturing process based on state space model and incremental integral separation PID control, which uses a single sampling point as temperature feedback, solving the problem of lag generated by traditional feedback cycle, realizing real-time online control of molten pool temperature of laser remanufacturing process. Finally, the experimental results show that the developed molten pool temperature control system can effectively eliminate the sudden and gradual temperature change in the deposition process and significantly improve the thermal accumulation phenomenon in the deposition process, thus improving the forming quality and performance stability of remanufactured parts.

실시간 상태-공간 식별 기반 제어기를 이용한 유한 국부공진 메타물질 평판의 적응형 진동 저감

To improve the internal sound quality of an electric vehicle, the noises (tire and road noises), vibration, and discomfort should be reduced effectively. Compared to the conventional damping materials, the locally resonant metamaterial is advantageous in terms of reducing the vibration in the low frequency band and reducing the weight through a stop band, where vibration is not transmitted. However, the locally resonant metamaterial installed in a finite structure fails to form an ideal stop band. Additionally, there is a problem of occurrence vibration mode at an arbitrary frequency. Accordingly, there is a need for an active vibration control method capable of learning and actively reducing the vibration modes occurring at arbitrary frequencies within the stop band in real-time. In this study, a real-time state-space identification-based active vibration control method was proposed to reduce the vibration modes occurring at arbitrary frequencies within the stationary band of local resonance metamaterials. The active vibration control method proposed in this study, combined with the existing passive vibration reduction method, can be utilized to effectively reduce the vibration for random disturbance of a fluctuating system.

State-Space Modeling and Control of Grid-Tied Power Converters With Capacitive/Battery Energy Storage and Grid-Supportive Services

Advances in the fields of renewable generation, electric vehicles, and energy storage systems push forward the research on ac–dc and dc–ac grid-tied power converters. However, the variabilities of power converters create new challenges in modeling and control. Existing state-space models fail to accurately describe various types of grid-tied converters (GTCs), particularly those with grid-supportive services, which are increasingly required by upcoming grid codes. As such, this article first proposes to classify GTCs into four basic types according to their ac and dc characteristics. Subsequently, corresponding detailed state-space models of GTCs are introduced, which serve as a useful tool for stability analysis. On top of that, this article further proposes control implementations of grid-supportive services related to active and reactive power control, including droop, inertia, and oscillation damping. Finally, simulation and experimental results demonstrate the effectiveness of the proposed models and grid-supportive services.

Tuning of PIDD2 controllers for oscillatory systems with time delays

Proportional–integral–derivative (PID) control is a durable control technology that has been widely applied in the process control industry. However, PID controllers cannot achieve satisfactory performance for oscillatory systems with long time delays; thus, high-order controllers like the proportional–integral–double derivative (PIDD2) can be adopted to enhance the control performance. In this paper, we propose a tuning formula for the PIDD2 controller for oscillatory systems with time delays and its practical implementation via an observer bandwidth-based state-space PIDD2. Simulation results show that the state-space PIDD2 controller tuned from the proposed formula trades-off among robustness, time domain performance, and measurement noise attenuation and can arrive at a better control effect than PID for oscillatory systems.

State space analysis and control transfer function of a flyback converter

Flyback converters are used in automotive power supply applications for their advantage of galvanic isolation and the low number of components on a small PCB area. This paper is dedicated to state space analysis of flyback converter with parasitic elements. The Euler method also uses state-space matrices of the proposed flyback converter with nonideal flyback converter with a Bode plot of both open-loop and closed-loop systems. The advantage of this approach is to determine the stability of the open-loop system from a mathematical model and to estimate suitable PI tuning values for the closed-loop system. Transfer function and Bode plots have been obtained from state space matrices in MATLAB.