General Control Research Articles

A Generic Multi-Frequency Repetitive Control Scheme for Power Converters

A Generic Multi-Frequency Repetitive Control (GMFRC) scheme, is proposed to perfectly compensate for all kinds of periodic signals including multi-frequency signals, where multiple selective harmonic repetitive controllers in parallel are added into a stable instantaneous feedback control loop to achieve overall satisfactory performance. The GMFRC scheme provides a general framework for housing various repetitive control and even resonant control schemes in power electronic conversion and other extensive engineering applications. A universal approach to the analysis and synthesis of the digital GMFRC scheme has been developed, which includes the stability criteria and control gain rules. More important, the digital GMFRC controller is transformed into a general fractional-order one for exact compensation of multi-frequency signals and other fractional-order periodic signals in practice. An application example of a 5kVA three-phase PWM inverter under a fractional-order GMFRC scheme is provided. Experimental results demonstrated that the proposed GMFRC scheme can force the inverter to produce required mono-frequency or multi-frequency voltages with high accuracy, fast response and good robustness.

Design and development of MTCA.4-based generic control and data acquisition module for CSNS-II and SAPS-TP

A new generation of domestically generic control and data acquisition AMC module has been developed to meet the requirements of the upgrade and renovation of the RCS LLRF system in the CSNS-II project, as well as the horizontal testing needs of the SAPS-TP. This module complies with the MicroTCA.4 standard and utilizes the Xilinx ZYNQ 7045 SOC as the core data processing chip. It can simultaneously run FPGA algorithms and the EPICS IOC remote control application program, serving as an autonomous control node. The module is equipped with 8 channels of 125 MSPS 16-bit ADC and 2 channels of 16-bit DAC with a maximum sampling rate of up to 500 MSPS. It also features standard expansion interface, front optical panel fiber interface, external clock interface, front panel digital IO interface, and more, catering to the needs of multiple systems in accelerator facilities such as radio-frequency, beam diagnostics, and control system. Additionally, the module can function independently for smaller-scale experimental and testing systems, such as the LLRF system for horizontal testing of SAPS-TP. To guarantee the seamless integration and proper operation of the AMC module within the MicroTCA.4 ecosystem, the MMC mezzanine card has been developed and thoroughly tested. In order to further reduce costs for small-scale systems, we optimized the software of the MMC so that the AMC board can run without the MCH module and realize self-health monitoring. For common applications, we have developed a complete software architecture and demo programs, including FPGA firmware, embedded Linux operating system, kernel driver modules, and EPICS IOC demo application programs. Detailed measurements have been conducted on hardware performance such as SNR, SFDR, channel isolation, as well as software performance including AXI DMA data bandwidth and system resources utilization, etc. The measurement results indicate that the module's performance indicators meet the design requirements.

Linear Program-Based Policies for Restless Bandits: Necessary and Sufficient Conditions for (Exponentially Fast) Asymptotic Optimality

We provide a framework to analyze control policies for the restless Markovian bandit model under both finite and infinite time horizons. We show that when the population of arms goes to infinity, the value of the optimal control policy converges to the solution of a linear program (LP). We provide necessary and sufficient conditions for a generic control policy to be (i) asymptotically optimal, (ii) asymptotically optimal with square root convergence rate, and (iii) asymptotically optimal with exponential rate. We then construct the LP-index policy that is asymptotically optimal with square root convergence rate on all models and with exponential rate if the model is nondegenerate in finite horizon and satisfies a uniform global attractor property in infinite horizon. We next define the LP-update policy, which is essentially a repeated LP-index policy that solves a new LP at each decision epoch. We conclude by providing numerical experiments to compare the efficiency of different LP-based policies.Funding: This work was supported by Agence Nationale de la Recherche [Grant ANR-19-CE23-0015].

An analytical differential kinematics-based method for controlling tendon-driven continuum robots

Generic and high-performance feedback control is still challenging for tendon-driven continuum robots. Conventional model-based controllers, based on the piecewise constant curvature (PCC) assumption, explicitly require the arc parameters (bending angle and direction angle) to link the task (in Cartesian coordinates) and actuation spaces. However, the approaches' effectiveness remains to be explored when robot shapes deviate from circular arcs. This paper proposed a hybrid scheme for novel kinematic control of continuum robots. The error led by the slack state has been avoided through tension supervision, while analytical differential kinematics is further developed to avoid the explicit call of arc parameters by importing Cylindrical coordinates into task space and applying accurate piecewise linear approximation. Comparison between a conventional PCC-based controller and the proposed controller has been done by implementing them in a twin-pivot joint-based continuum section. An overall tip positioning accuracy of ±0.35mm has been reached, and a result of root-mean-square-error (RMSE): 0.3mm and Max error: 0.97mm has been observed when running two predefined path tracking. Further, in order to evaluate the versatility of the proposed controller, a dual-revolute joint-based and a 3D-printed continuum section were used to test for path tracking to prove the effectiveness of the controller on a wide range of continuum robotic systems.

Development and Validation of a Real-Time PCR for the Molecular Identification of Thaumatotibia leucotreta (Lepidoptera: Tortricidae: Olethreutinae) Intercepted in Trade

The false codling moth, Thaumatotibia leucotreta (Meyrick), is a polyphagous pest indigenous to most of sub-Saharan Africa. This species is considered to be quarantined throughout much of the world, in part due to its extensive host range and significant damage caused by larval feeding. We developed a specific real-time PCR assay that allows for rapid and reliable identification of T. leucotreta. More than 150 target specimens were sequenced using an Illumina whole-genome shotgun approach to identify the most suitable loci for assay development. A hydrolysis probe that binds to a segment of the internal transcribed spacer 2 (ITS2) region was designed and used with a generic internal control targeting 18S rDNA. The assay was examined for cross-reactivity by testing additional Thaumatotibia species and representatives of related Olethreutine leafroller genera such as Cryptophlebia, Cydia, and Grapholita, which are often encountered in the same geographic region and on the same hosts as T. leucotreta. We compared our newly developed test to previously published TaqMan real-time PCR, SYBR Green real-time PCR, and loop-mediated isothermal amplification (LAMP) tests. Our newly developed real-time PCR assay outperformed all three tests in terms of analytical specificity, with 100% accuracy. These results will help to further improve diagnostic standards for molecular identification of T. leucotreta. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Controller Design of a Wire-Driven Flexible Robot

Robot tasks and functions are advanced by IoT, remote working, and new human lifestyle requirements in tedious or accurate occupations such as surgery, product inspections, or agriculture harvesting. Numerous robots are technologically advanced by scientists for diverse tasks. Flexible robots are developed based on stated applications since they can adjust their geometry to the working situations. The present study introduces a wire-driven flexible robot stimulated by animal trunks. It can make a motion in planar and space based on the construction of that. Initially, a kinematic model was developed to predict end-effector trajectory, and then a dynamic model was established to compute the required tension of tendon wires based on bending beam theory. Models are simulated by MATLAB/SIMULINK Software and implemented by a generic controller. Various input functions are applied to the model, and outputs show good stability and accuracy. Based on these results, in future work, a WDFR will be developed and controlled by the current presented PID controller.

Adaptive control concepts using radial basis functions and a Kalman filter for embedded control applications

Look-up tables or semi-physical models are used in engine control application to enhance the transient control performance. However, the calibration of these look-up tables and/or semi-physical models is highly data dependent, requires significant calibration effort, and does not account for effects such as hardware aging, drifts, fuel variations, etc. To address these challenges, this paper presents a generic adaptive control concept for real-time engine control applications. The boundary conditions for the operation on a state-of-the-art engine control unit (ECU) are considered and Radial Basis Function (RBF)-based neural networks are identified for the approximation of non-linear models. To ensure optimal convergence and un-susceptibility to signal noise, a Kalman filter (KF) is exploited for training the RBF network and a modified approximation is derived in this work. The derived KF approximation shows ∼30% better training performance in comparison to the approximation without variable noise consideration. The developed control concept is validated on an engine test bench and a demonstrator vehicle using a Rapid Control Prototyping (RCP) system. The potential of the developed control concept to ensure optimal control response and robustness has been demonstrated.

A trajectory tracking control system for paddle boat in intelligent aquaculture.

Trajectory tracking plays a notable role in unmanned surface vehicles (USV), especially for the emerging intelligent aquaculture, as the level of integration, high-efficiency, and low-labor-intensity of such USV is determined by trajectory tracking. Here, we report a generic trajectory tracking control system for a paddle boat by establishing a three-degree-of-freedom kinematics model, which could precisely characterize the relationship between velocities, forces and moments of the paddle boat. A Pixhawk 4 as the core controller of the hardware system could be integrated with the other hardware submodules and could complete the wireless data transmission, monitoring and remote control functions. Meanwhile, we establish a fuzzy rule table, consider the advantages of line-of-sight (LOS) guidance and fuzzy adaptive proportional-integral-differential (PID) algorithm, combine the two parts and apply them as the key algorithm in the trajectory tracking of the paddle boat. Demonstrations include trajectory tracking effect at different velocities, turning effect at left-turn moment, and trajectory tracking effect at different turning angles. The results show that the paddle boat is able to travel under the trajectory formed by following the planned waypoints within the error allowed, which is called effective trajectory tracking. And can offer an alternative pathway toward achieving effective trajectory tracking control in advanced intelligent aquaculture USV for smartly and wirelessly operated pond drug spraying.

Studies on crystallization process for pharmaceutical compounds using ANN modeling and model based control

Solvent selection and Controlling of operating parameters play a crucial role in batch cooling crystallization process. Choosing a best solvent for crystallization process involves more experimentation and time. To overcome this problem, an Artificial Neural Network (ANN) model technique is used to predict the carbamazepine form Ⅲ solubility by considering the thermodynamic properties of different solvents i.e. critical temperature, critical pressure, temperature, molecular weight, and acentric factor. The ANN model was trained and evaluated for solubility at various input data sets using experimental solubility data available in the literature. The ANN model with 20 hidden neurons has given the R2 value of 0.9943 which shows that the developed ANN model can be used for the selection of best solvent for batch crystallization process. Further, to determine the optimal cooling profile of batch cooling crystallization process, a multi-objective optimization problem is formulated by considering objectives as minimizing the coefficient of variation (CV) and maximizing the Number mean size (NMS) of crystals subjected to population balance equations using “method of moments” technique. Two types of temperature strategies i.e., piece-wise constant and piece-wise linear are developed and solved using NSGA-Ⅱ dynamic optimization procedure. The optimal NMS value attained through piece-wise linear strategy was 197.1 µm. This value has been increased by 28.3 µm from the nominal case (without optimization) and the coefficient of variation has decreased from 0.951 to 0.76. Further, optimal NMS value attained through piece-wise constant strategy was 205 µm. The value has been increased by 36.2 µm and the coefficient of variation has decreased from 0.951 to 0.73. This proves that the crystal attributes can be improved by optimal cooling temperature profile obtained by multi-objective optimization framework. For implementing the optimal cooling profile an advanced model-based control, i.e., Generic Model Control (GMC) was developed. It was observed that the GMC controller has the good tracking profile with no offset with/without disturbances and small value of root mean square error (RMSE) of 0.0016 using piece-wise constant as set point temperature. Using piece-wise linear as set point temperature, the RMSE value was 0.0018. In particular, it is advantageous to operate the batch cooling crystallization process with piece-wise linear strategy for set point trajectory tracking problems.

Inverted Sedimentation of Active Particles in Unbiased ac Fields.

Gaining control over the motion of active particles is crucial for applications ranging from targeted cargo delivery to nanomedicine. While much progress has been made recently to control active motion based on external forces, flows, or gradients in concentration or light intensity, which all have a well-defined direction or bias, little is known about how to steer active particles in situations where no permanent bias can be realized. Here, we show that ac fields with a vanishing time average provide an alternative route to steering active particles. We exemplify this route for inertial active particles in a gravitational field, observing that a substantial fraction of them persistently travels in the upward direction upon switching on the ac field, resulting in an inverted sedimentation profile at the top wall of a confining container. Our results offer a generic control principle that could be used in the future to steer active motion, direct collective behaviors, and purify mixtures.

Reduced-order model based dynamic tracking for soft manipulators: Data-driven LPV modeling, control design and experimental results

We propose a generic nonlinear reduced-order tracking control method for elastic soft robots. To this end, a new linear parameter varying (LPV) control framework is developed using the data collected from the soft robots. Specifically, for LPV modeling we first derive a nonlinear robot model, which is large-scale by nature, using finite element methods (FEM). Then, a proper orthogonal decomposition (POD) algorithm is used to generate a set of linearized reduced-order models, representing the local behaviors of the soft robot at different operating points within the workspace. Via a unified POD projector, not only the order of these linearized models can be significantly reduced but also their mechanical structure and stability properties can be preserved for LPV modeling and control design. Next, using radial basis function (RBF) networks, we propose an iterative training method to build the LPV robot model by interpolating a subset of selected linearized models with a specified interpolation error. For LPV control design, the equivalent-input-disturbance (EID) concept is exploited to develop a dynamic tracking control scheme, which is composed of three core components: feedforward control, disturbance-estimator control and feedback control. The feedforward control is designed to account for the effects of the trajectory reference and the time-varying affine term, issued from the FEM-based model linearization. The disturbance-estimator control is obtained from a generalized proportional integral LPV observer, which also provides the estimated reduced-order states for feedback control. The observer-based feedback control design is reformulated as a convex optimization problem under linear matrix inequality (LMI) constraints. The globally uniformly ℓ∞ stability of the closed-loop LPV robot model is guaranteed by means of Lyapunov stability theory. Experimental tests are conducted with a soft Trunk robot, inspired by the elephant trunk, under several scenarios with small and large deformations to show the effectiveness of the proposed LPV tracking control framework. A comparative study is also performed with a recent linear EID-based controller and an iterative learning controller to emphasize the interests this nonlinear control method for soft elastic robots. This paper is complemented with a series of demonstration videos: https://bit.ly/3D8C4Vd.

A PV variability tolerant generic multifunctional control strategy for battery energy storage systems in solar PV plants

This paper proposes a multifunctional control strategy for battery energy storage systems (BESSs) in solar photovoltaic (PV) plants to avoid the unacceptable PV-power ramp-rate caused by PV variability. In addition to the PV variability tolerant ramp-rate control, the proposed multifunctional BESS control strategy is capable of maintaining a user-specified charging profile to store excess PV power while providing backup power support and charge maintenance, by accommodating diverse control functions time to time. Four control functions, namely, Rest (RST), Surplus Energy Storage (STR), Back-up Power Support (SPT) and Charge Maintenance (CMT) functions, are proposed in this article. Besides, extendibility of the proposed multifunctional strategy is presented by adding two action strategies – one with the STR function and another with the SPT function. Validation of the proposed control strategy is conducted by using real historical data from a utility-scale solar PV plant located in Queensland, Australia. Numerical analyses show that the strategy can successfully keep the PV ramp-rate within specified limit along with the user specified charge profile. Further, exploiting the real-field data, the proposed strategy is demonstrated through a real-time co-simulation study conducted in a MATLAB interfaced RSCAD-RTDS platform that can be used as Digital Twin (DT) for the PV plant.

Parameter Tuning for WECC Generic Photovoltaic System Models Using Latin Hypercube Sampling and Pareto Optimality

With attention increasing towards worldwide ecological conservation, the energy policies of various countries are moving towards renewable energy systems, primarily with solar and wind as the primary power sources. The Western Electricity Coordinating Council (WECC) has developed a generic model as a simulation framework for a typical solar wind generation system to analyze the response of the actual equipment. In this paper, we propose a parameter tuning process for a generic model controller that combines Latin hypercube sampling and Pareto optimization to enable a generic model to effectively match the output of an actual inverter device. Different operating scenarios were considered to match the dynamic characteristics of the inverter device using the multi-objective particle swarm algorithm (MOPSO) along with Pareto optimization. The effectiveness of the proposed parameter tuning process was verified on a user-defined test system and, later, the IEEE 39 bus system with a large solar power facility.

Data-driven analysis for disturbance amplification in car-following behavior of automated vehicles

This paper presents a data-driven framework to quantitatively analyze the disturbance amplification behavior of automated vehicles in car-following (CF). The data-driven framework can be applied to unknown CF controllers based on the concept of empirical frequency response function (FRF). Specifically, a well-known signal processing method, Welch's method, together with a short time Fourier transformation is developed to extract the empirical transfer functions from vehicle trajectories. The method is first developed assuming a generic linear controller with time-invariant CF control features (e.g., control gains) and later extended to capture time-variant features. The proposed methods are evaluated for estimation consistencies via synthetic data-based simulations. The evaluation includes the performances of the linear approximation accuracy for a linear time-invariant controller, a nonlinear controller, and a linear time-variant controller. Results indicate that our framework can provide reasonably consistent results as theoretical ones in terms of disturbance amplification. Further it can perform better than a linear theoretical analysis of disturbance amplification, particularly when nonlinearity in CF behavior is present. The methods are applied to existing field data collected from vehicles with adaptive cruise control (ACC) on the market. Findings reveal that all tested vehicles tend to amplify disturbances, particularly in low frequency (< 0.5 Hz). Further, the results demonstrate that these ACC vehicles exhibit time-varying features in terms of disturbance amplification ratio depending on the leading vehicle trajectories.

The MicroTCA.4 Fast Control and Processing Board for Generic Control and Data Acquisition Applications in HEP Experiments

The MicroTCA.4 Fast Control and Processing board (u4FCP) is an FPGA-based double-width MicroTCA.4 compatible Advanced Mezzanine Card (AMC) for generic control and data acquisition applications in High-Energy Physics (HEP) experiments. Built around the Xilinx Kintex UltraScale+ FPGA, the u4FCP provides users with a platform which has access to on-board FPGA Mezzanine Card (FMC) sockets with an array of configurable I/O and high-speed links up to 100 Gbps. In addition, the rear transition module (RTM) allows separation of processing and I/O functionality with the Kintex -7 FPGA and more FMC sockets. The increased processing capacity and area on the RTM allow a significant increase in the amount of FPGA logic and I/O connections. The on-going R&D effort carried out on applications in HEP experiments is presented.

Interrogating the Function of Bicistronic Translational Control Elements to Improve Consistency of Gene Expression.

Context independent gene expression is required for genetic circuits to maintain consistent and predicable behavior. Previous efforts to develop context independent translation have leveraged the helicase activity of translating ribosomes via bicistronic design translational control elements (BCDs) located within an efficiently translated leader peptide. We have developed a series of bicistronic translational control elements with strengths that span several orders of magnitude, maintain consistent expression levels across diverse sequence contexts, and are agnostic to common ligation sequences used in modular cloning systems. We have used this series of BCDs to investigate several features of this design, including the spacing of the start and stop codons, the nucleotide identity upstream of the start codon, and factors affecting translation of the leader peptide. To demonstrate the flexibility of this architecture and their value as a generic modular expression control cassette for synthetic biology, we have developed a set of robust BCDs for use in several Rhodococcus species.

Energy consumption versus strength in MEΧ 3D printing of polylactic acid

The cost-effectiveness and the environmental impact of Additive Manufacturing (AM) are nowadays two of the hottest process-related industrial and research topics. Energy efficiency is a strong claim, and so is the demand for durable and functional 3D-printed workpieces. These contradictory aspects usually require flexibility and compromises. Especially for Material Extrusion (MEX) 3D printing, the plurality of the control parameters makes such optimizations complicated. This research explores the effect of seven generic and machine-independent control factors (e.g., Raster Deposition Angle; Orientation Angle; Layer Thickness; Infill Density; Nozzle Temperature; Bed Temperature, and Printing Speed) on energy consumption of Polylactic Acid over the compressive response of MEX 3D printed specimens. To make it possible, a three-level L27 orthogonal array was compiled. Each experimental run included five specimen replicas (after the ASTM D695-02a standard) summing up 135 experiments. The fabrication time and the energy consumption were determined by the stopwatch method, whereas the compressive strength, elasticity modulus, and toughness were derived with compressive tests. The Taguchi analysis ranked the impact of each control parameter on each response metric. The printing speed and the layer thickness were the most influential control parameters on energy consumption. Furthermore, the infill density and the orientation angle were found as the most dominant factors in the compressive strength. Finally, Quadratic Regression Model (QRM) equations for each response metric over the seven control parameters were compiled and validated. Hereto, the best settlement between energy efficiency and mechanical strength is now possible, an option with great technological and industrial merit.

A Generic Multicell Network Control for Three-Phase Grid-Connected Inverters

The inherent resonance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> filters threatens the inverter stability seriously. Moreover, the strong <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dq$</tex-math></inline-formula> -axis cross-coupling of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -filtered grid-connected inverters in a synchronous frame goes against the independent regulation of active ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P$</tex-math></inline-formula> ) and reactive ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> ) power that is required in various applications. Besides, the imbalance and harmonics of the grid voltage will deteriorate the injected grid current, so the disturbance suppression performance of grid-connected inverters is important. Furthermore, excellent steady-state and dynamic performance is essential for grid-connected inverters. To solve the above four issues simultaneously, a complex-domain model of grid-connected inverters is established, and a generic multicell network control (GMNC) structure is proposed in this article. Based on the proposed GMNC structure, a number of control schemes are obtained, and the quantitative relationship between inverter performance and control parameters can be given directly and precisely for each scheme. Therefore, it is convenient and simple for engineers to select proper control schemes depending on the practical conditions and to design all the control parameters for an expected steady-state, dynamic, and antidisturbance performance without the resonance and the coupling issues. Finally, the effectiveness of the proposed control is verified by the experiments.

A Generic Voltage Control for Grid-Forming Converters With Improved Power Loop Dynamics

Grid-forming converters (GFMCs) provide voltage support and other grid services, such as inertia and droop, in more-electronics power systems. The GFMCs with small droop coefficients or connected to the grid through a small impedance may have a fast power loop, which leads to serious conflicts with the inner ac voltage control loop, resulting in power oscillations or even instability. To address this problem, an extremely fast ac voltage loop should be expected for GFMCs. On top of that, GFMCs must operate well in both stand-alone and grid-tied conditions. Through detailed analysis, this article first reveals that the grid-tied operation of a dual-loop controlled GFMC with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> output filter features a much slower voltage control loop than that of the stand-alone mode. To improve the dynamics of GFMCs, we propose a generic voltage control scheme with a high-pass filter in the current feedback loop. The generic voltage controller has fast voltage tracking performances under both grid-tied and stand-alone operations, thereby enabling GFMCs to achieve better power regulation and grid service. The improvements in both voltage tracking and power control are verified via experiments.

Analysis and Optimal Modulation for 2/3-Level DAB Converters to Minimize Current Stress With Five-Level Control

A two-to-three-level dual-active-bridge (DAB) dc–dc converter has high potential to become a promising solution for medium-voltage high-power applications. However, its switching characteristics, modeling, and optimal modulation schemes have not been comprehensively explored yet. In order to avoid voltage and current distortions and ensure reliable operation, this article analyzes the operating constraints for the neutral-point-clamped (NPC) DAB converters. Based on the obtained operating constraints, the transferred power and current stress models under a five-level control scheme, which is considered as one of the most advantageous control strategies for the NPC DAB converters, are obtained with an equivalent-wave modeling method. Generic control strategies based on analytical solutions have not been fully explored for the multilevel DAB converters due to the increased number of control variables. With the above, this article proposes a minimum-current-stress control strategy employing analytical solutions in the entire power range, which are obtained by combining the Karush–Kuhn–Tucker conditions and numerical-solution analysis. With the proposed modulation scheme, the control variables can be automatically modified online, instead of relying on the offline calculation of the numerical control variables. Therefore, the control complexity can be reduced significantly, especially when the operating parameters change in a wide range. Finally, experimental tests verify the effectiveness of the analysis.