52results about How to "Reduce modeling difficulty" patented technology

The invention discloses a water-wind-light complementary strategy optimization method and system based on a large-scale water-wind-light cluster, and belongs to the technical field of power system optimization scheduling. The method comprises the steps of performing generalization processing on a water-wind-light complementary power system optimization object of a large-scale water-wind-light cluster through a power grid aggregation model and power supply cluster division to obtain a generalized optimization object; establishing constraint conditions of the water-wind-light complementary power system, wherein the constraint conditions comprise power transmission channel constraint, wind-light output constraint and water-electricity constraint; constructing a complementary strategy optimization model of the water-wind-light complementary power system by taking maximization of the dry-period supply conservation capacity of the cascade water-wind-light system and maximization of the water-wind-light annual energy output as objective functions and combining constraint conditions; and solving the complementary strategy optimization model of the water-wind-light complementary power system to obtain a complementary strategy of the water-wind-light complementary power system. According to the method, the optimization modeling accuracy and the calculation efficiency of the large-scale system are improved, and the multi-target optimization of the water-wind-light complementary power generation based on the large-scale water-wind-light cluster is realized.

The embodiment of the invention discloses a discrete voice coding method, electronic equipment and a storage medium, and the method comprises the steps: constructing a voice input and frequency spectrum output VAE, enabling the input voice to have speaker tone disturbance, and enabling the VAE structure to be used for multi-task learning; vector quantization is carried out on the VAE, and a VQ-VAE is initialized, so that a quantization layer is added to continue to reduce an information bottleneck; and additionally training a vocoder, and carrying out waveform reconstruction by starting from the code obtained in the VQ-VAE.

The invention discloses a data-driven floating type wind turbine generator model-free control method and system, and the method comprises the steps: obtaining the operation state and external environment parameters of a wind turbine generator, and constructing sample data; inputting the sample data into a pre-trained controller, and outputting a control instruction to control the wind turbine generator; the specific process of obtaining the controller comprises the steps that the simulation operation state / historical operation state and external environment parameters of the wind turbine generator are obtained, and an initial controller is constructed based on the simulation operation state / historical operation state and the external environment parameters; determining a control target, a wind turbine generator operating environment, an intelligent agent, a state space, an action space, a constraint condition and a reward function in the initial controller; and performing continuous interactive learning training on the intelligent agent and the operation environment of the wind turbine generator through a deep reinforcement learning algorithm to obtain a trained controller. The method has the advantages of reducing the modeling difficulty, dynamically correcting the control strategy, improving the power generation efficiency and the like.

The invention relates to the technical field of tire simulation design, in particular to a tire pattern longitudinal groove edge optimization design method and system and a computer program product. The method comprises the following steps: extracting actual grounding pressure data of a tire; establishing a pattern block geometric plane model, and applying proper boundary conditions; according to the condition of non-uniform grounding pressure distribution, the shape of the contact surface of the pattern block is adjusted to realize uniform distribution; the grounding pressure distribution of the edge of the pattern block is optimized by minimizing a grounding pressure skewness value; and finally, calculating and adjusting the outer contour curve of the tire to realize final optimization design. According to the method, the road holding force, the controllability and the driving stability of the tire can be remarkably improved, the service life is prolonged, the modeling and calculation time is relatively low, and the design and development cost is remarkably reduced.

The invention relates to a mechanical property prediction method for a woven composite material structure, and the method comprises the steps: obtaining a microscopic CT image of the woven composite material structure; carrying out image preprocessing on the CT image; segmenting a fiber bundle in the preprocessed image to obtain a segmented fiber bundle image; extracting a fiber bundle trend center line from the segmented fiber bundle image; establishing a macroscopic geometric model of the woven composite material structure, and performing unit grid division on the geometric model; establishing a relation between a fiber bundle trend center line and a geometric model unit; calculating mechanical property parameters of each unit one by one; and calculating the macromechanical response of the composite material structure. According to the method for predicting the mechanical property of the woven composite material structure, the grid division independent of the microstructure characteristics is adopted, so that the modeling efficiency can be improved, the modeling difficulty can be reduced, and the method is easy to implement.

The present invention discloses a method for fine-grained control of bridge construction progress based on a digital twin multidimensional model, comprising the following steps: analyzing the composition of the bridge from the perspective of physical entities, establishing a physical entity decomposition structure of the bridge facility in combination with the fine-grained control objectives; constructing a digital twin geometric model of the bridge; further constructing a digital twin physical model of the bridge; further constructing a digital twin behavior model of the bridge; further constructing a digital twin rule model of the bridge; and using the digital twin multidimensional model of the bridge to fine-grained control of the construction progress. The present invention proposes a method for establishing a digital twin model for bridge construction progress control from four dimensions: geometry, physics, behavior, and rules, and innovatively proposes a method for calculating the number of days of construction progress delay based on "time" and "number of components" in the dimension of the rule model, which effectively improves the degree of refinement of the whole process control of the bridge construction progress.

The invention discloses a model-free robust control method and system for a pantograph with external disturbance. Collecting dynamic data in the running process of the train pantograph; establishing a train pantograph data model with external disturbance; estimating external disturbance; optimizing and updating pseudo partial derivative information in the train pantograph data model; designing a model-free robust control scheme for the train pantograph with external disturbance; optimizing and updating an error control gain and a disturbance compensation control gain in the control scheme; a model-free robust control scheme is adopted to calculate and obtain a control input signal, and the train pantograph is controlled until a control task is finished; the control method and system provided by the invention are not only suitable for the train pantograph with external disturbance, solve the disturbance compensation problem, reduce the modeling difficulty and reduce the computing resource consumption, but also have the application prospect of being popularized to other single-input single-output systems.

The invention discloses a prediction method for thermal residual stress of a quasi-continuous mesh composite material, and belongs to the field of novel composite material configuration design and model establishment. The method comprises the following steps: 1) establishing a geometric model of a convex polyhedron reinforced phase; 2) establishing a geometric model of the net-shaped structure; 3) setting the volume fraction of the convex polyhedron reinforced phases, and calculating the number of the convex polyhedron reinforced phases accommodated by the network plane; 4) calculating to obtain the position of a convex polyhedron reinforced phase; 5) inserting the established convex polyhedron reinforced phase geometric models in the planes of all the reticular structure geometric models to obtain a quasi-continuous reticular composite material geometric model; 6) performing mesh division on the continuous mesh composite material geometric model, and applying a cooling boundary condition; and inputting material attributes, performing numerical simulation on thermal mismatch behaviors in the cooling process, and obtaining the thermal residual stress distribution condition of the quasi-continuous mesh composite material. Through the method, the thermal residual stress distribution of the quasi-continuous composite material with a complex structure can be effectively predicted.

The invention provides a divergent rotary diffuser based on a parametric modeling method and a centrifugal compressor. The divergent rotary diffuser comprises an impeller section, a transition vaneless section, a diffuser blade section, a rotary section and a volute section. In the air inlet direction of the diffuser, the transition vaneless section is installed at the rear end of the diffuser impeller section in the radial direction of an outlet, the transition vaneless section is in clearance fit with a diffuser impeller, and the wheel disc side and the wheel cover side of the transition vaneless section are equal in width. After the diffuser blade section is installed on the transition vaneless section, an inlet of the diffuser blade section and an outlet of the transition vaneless section are the same in width. The wheel disc side of the diffuser blade section and the wheel disc side of the transition vaneless section are in the same radial direction, and an expansion angle exists between the wheel cover side of the diffuser blade section and the wheel cover side of the transition vaneless section. After the rotation section is installed on the transition vaneless section, the width of an inlet of the rotation section is the same as that of an outlet of the transition vaneless section. According to the technical scheme, radial installation of the centrifugal compressor diffuser can be reduced, and the modeling difficulty is further reduced by adopting a parametric modeling method.

The invention provides a model and scene fusion rendering method and device, equipment and a storage medium. The method comprises the following steps: displaying a three-dimensional scene; displaying a skeleton model representing a skeleton structure of the three-dimensional model in the three-dimensional scene; in response to an adjustment confirmation operation for the skeleton model, sending a fused image generation request to a server to obtain a target fused image, the target fused image being obtained by fusing the model effect picture and the rendering effect picture of the three-dimensional scene; the model effect picture is obtained by performing background fusion on a three-dimensional model corresponding to the skeleton model and a rendering effect picture of the three-dimensional scene by using a large language model.

The invention relates to a vehicle adaptive model prediction control method. The method comprises the following steps: collecting vehicle dynamic parameters, and constructing a two-degree-of-freedom vehicle dynamic model according to the dynamic parameters; constructing a first matrix used for describing an input and output relation of the linear state space equation; determining a model order of a vehicle model corresponding to the two-degree-of-freedom vehicle dynamics model through an akaike information criterion, and identifying parameters of the first matrix through a recursive least square method of introducing a forgetting factor to obtain the vehicle model; self-adaptively adjusting forgetting factors in the vehicle model through fuzzy logic and identification residual errors to obtain an optimized vehicle model; and establishing a vehicle controller based on the optimized vehicle model to control a vehicle control system in real time. According to the method, effective tracking of the expected state of the vehicle under different working conditions can be realized, the identification speed and the modeling precision are improved, and the modeling complexity is reduced.

The invention discloses a prediction method for thermal residual stress of a discontinuous particle reinforced composite material, and belongs to the field of novel composite material modeling. The method specifically comprises the following steps: 1) constructing an initial convex polyhedron reinforced phase particle model; 2) establishing a composite material basic model; 3) performing volume scaling on the initial convex polyhedron reinforced phase particle model to obtain N target convex polyhedron reinforced phase particle models; 4) performing spatial rotation and translation on each target convex polyhedron reinforced phase particle model, and then combining the target convex polyhedron reinforced phase particle model with the composite material basic model to obtain a discontinuous particle reinforced composite material model; and 5) carrying out grid division, applying cooling boundary conditions, inputting thermodynamic attributes of corresponding materials, and carrying out numerical simulation on thermal mismatch behaviors in the cooling process of the discontinuous particle reinforced composite material. The method has the advantages of low modeling difficulty and few control parameters, and can effectively predict the influence of the reinforced phase particle shape on the thermal residual stress distribution of the composite material.

The invention belongs to the field of engines and computational fluid mechanics, and discloses an engine combustion chamber fuel injection point source loading method based on a flame surface model. According to the engine combustion chamber fuel injection point source loading method based on the flame surface model, the point source is arranged at the center point of the fuel injection hole under the condition that the numerical simulation precision of the combustion chamber is not reduced according to the rapid, efficient and accurate numerical simulation requirements of fuel injection, mixing and combustion processes in the aerospace engine combustion chamber; a mass source item, a momentum source item, an energy source item and a mixed fraction source item are added in a control equation set, so that the generation difficulty of a complex grid is reduced, the physical characteristics of the fuel injection process are well represented, a combustion chamber numerical simulation CFD program is slightly changed, the operability is high, and the method is suitable for large-scale popularization and application. And the modeling difficulty and the grid number of the combustion chamber grid with the complex engineering configuration are reduced, the engine evaluation design efficiency is greatly improved, and the engineering practical value is achieved.

The application discloses a 3D printing ocular prosthesis platform, which is prepared through the following steps: S1, designing the shape of the ocular prosthesis platform and dividing the shape into regions; S2, selecting a porous structure for the ocular prosthesis platform in each region and determining the voxel size according to the connecting point parameters of the selected porous structure; S3, voxelizing and fitting each region to form a voxelized interface; S4, filling the porous structure selected in step S2 into each corresponding region to complete the modeling of the porous structure in each region; S5, connecting the connecting points on adjacent voxelized interfaces to each other and converting the connecting lines into solid rod models to complete the modeling of the interfaces; S6, merging the models obtained in steps S4 and S5 to obtain a final model of the ocular prosthesis platform; S7, 3D printing to obtain a bioceramic blank; and S8, high-temperature sintering of the blank to obtain a 3D printed ocular prosthesis platform after porcelain forming. The application can obtain a new 3D printed ocular prosthesis platform, which provides technical support for ocular prosthesis repair.

The invention discloses a simplified analysis method for a complex radiator group structure in an electric reactor, and belongs to the field of electric reactors. The method comprises the following steps that S1, the number of cooling fins adopted by a radiator set is reduced to obtain a simplified radiator set, a reactor three-dimensional model is established, material attribute parameters of all parts are set in finite element analysis software, and the structures of all the parts are subdivided; s2, determining loss distribution on each component, and determining a shell heat dissipation boundary condition of the reactor; s3, applying an equivalent convective heat transfer coefficient of the cooling fins on the cooling fins in combination with the simplification degree of the reactor radiator group; and S4, solving the temperature fluid field control equation to obtain the temperature fluid field of the reactor. According to the method, the reactor is fully modeled after the number of the radiating fins in the radiator group is reduced, so that the difficulty of fully modeling is reduced, the simulation efficiency can be improved on the basis of guaranteeing the simulation precision, and the method is feasible.

The application discloses a lithium ion battery internal short circuit fault simulation method, comprising the following steps: S1, establishing a lithium ion battery single particle lumped parameter estimation model, and estimating the dynamics and transfer attribute parameters of a lithium ion battery to be simulated by using a control equation; S2, constructing an electrochemical-thermal coupling 3D model and setting a control equation thereof; S3, substituting the dynamics and transfer attribute parameters of the simulated lithium ion battery into the constructed electrochemical-thermal coupling 3D model, simulating the electrothermal behavior of the lithium ion battery under different fault types based on the control equation, and realizing internal short circuit fault simulation. The application solves the defects of the existing fault simulation scheme in simulation accuracy, calculation rapidity and application universality, establishes an internal short circuit fault electrochemical-thermal coupling 3D model, and provides simulation data basis for an internal short circuit fault detection method.

The present invention discloses a current prediction control method and system for a co-phase power supply device based on error differentiation; the method comprises collecting the input voltage and input current of the co-phase power supply device and referring to the current data; establishing a current control system prediction data model and optimizing and updating its pseudo partial derivative matrix; designing a model-free adaptive prediction control scheme based on error differentiation and optimizing and updating its error control gain matrix; processing a control input signal and then modulating it through PWM to generate a disconnection signal for controlling the co-phase power supply device until the control task is completed; the control method and system provided by the present invention are not only suitable for the current control of the co-phase power supply device, but also reduce the difficulty of modeling and improve the control accuracy, and also have the application prospect of being extended to other single-input and single-output systems.

The present application belongs to the field of multi-scale modeling of composite materials, and is a RVE modeling method for composite materials with random particle distribution considering periodic boundary conditions. The method first establishes a basic analysis model of composite materials, generates a number of randomly distributed particle coordinates in the RVE region of the RVE basic model of composite materials through random commands, and then respectively judges whether the particles are in the RVE region, whether the particles overlap, whether the particles overlap with the model boundary, and the volume percentage, so as to determine the coordinates of each particle and its derivative particles that meet the generation requirements, and then generates a txt format file, generates a finite element model through finite element analysis software and performs finite element analysis, so as to calculate the effective mechanical properties of the composite material. It is possible to generate completely random particles and their derivative particles that meet the needs of finite element analysis, and at the same time, it is not necessary to avoid contact with the RVE boundary, and the periodic boundary conditions are convenient to apply, so that the effective mechanical properties of composite materials with random particle distribution can be accurately predicted.

The application discloses a heterogeneous energy collaborative prediction and scheduling method considering source-load low-carbon characteristics complementation and matching, relates to the technical field of power system source-load prediction and low-carbon scheduling, and comprises the following steps: based on Laguerre polynomials, pseudo-inverse learning and integrated learning, robust local mean decomposition (RLMD) and weighted permutation entropy (WPE), a wind power (WP), photovoltaic power (PVP) and power load (PL) prediction model is constructed, which takes into account the prediction accuracy and stability; a HECM mathematical model is constructed, the HECM mathematical model framework comprises an integrated flexible operation mode of carbon capture power plant (IFCCPP), a wind farm (WF), a photovoltaic power station (PP), a concentrated solar power (CSP), a load active demand response (LADR), a ladder carbon trading mechanism (LCTM), an objective function and related constraints; in the source-load prediction, the two targets of accuracy and stability are considered simultaneously, a compromise solution is selected in the Pareto frontier through the MORUN algorithm, and the wind power, photovoltaic power and power load prediction results are more suitable for the power system scheduling scene with high robustness requirements.

The present invention provides a method for correcting the deviation of a performance model of a civil aviation engine, which is used to reduce the deviation between the performance model and the real engine state caused by the change of the working condition of the aviation engine. By establishing a correction decision module, an MBGD algorithm intelligent optimization module and a Kalman filter module, and constructing the matching relationship of the three modules, an intelligent optimization mechanism is formed. The method for correcting the deviation of a performance model of a civil aviation engine described in the present invention solves the problem of model deviation between the onboard model of the aviation engine and the real state of the engine due to the change of working condition, etc., reduces the modeling difficulty of the aviation engine system, improves the accuracy of the onboard model of the aviation engine, and is conducive to ensuring the accuracy and safety of the model-based control method of the aviation engine.

The present invention discloses a dynamic modeling and control method for a distributed thrust tilt-wing aircraft. The modeling method comprises the steps of establishing a multi-body dynamic tensor model and designing a smooth trajectory for synchronous tilting of a wing and a canard in a modal conversion stage. The control method adopts a hybrid control framework in which an inner and outer loop are connected in series, comprising an outer loop trajectory tracking control module, an inner loop attitude control module, a logic switching module and a distributed thrust control allocation module. Finally, the control method is simulated and verified by designing a reference trajectory. The structural weight layout design in which the center of mass of the wing and the canard, the center of mass of the fuselage and the total center of mass of the whole aircraft coincide with each other reduces the difficulty of modeling and control. A fine dynamic model of a distributed tilt-wing aircraft in a tensor form is established by using a multi-body dynamics method. Compared with a traditional single-body dynamics modeling method, the model accuracy is improved.

The present invention discloses a model-free control method and system for a single-link robotic arm under cloud-edge-end collaboration; the method comprises: a terminal collects real-time data of the single-link robotic arm, and sends the real-time data to an edge end; the edge end inputs the preprocessed data into a model-free controller that performs adaptive learning of time-varying parameters based on an online Actor-Critic reinforcement learning network, calculates control instructions for the single-link robotic arm, and the terminal controls the movement of the single-link robotic arm according to the control instructions; at the same time, the edge end sends the preprocessed data to the cloud; the cloud end optimizes and updates the parameters of the model-free controller, and transmits the optimized and updated model-free controller parameters to the model-free controller at the edge end; the above steps are repeated until the control task is completed; the present invention is not only applicable to single-link robotic arms, reduces their modeling difficulty, improves control accuracy, and improves computing efficiency, but also has the application prospect of being extended to other single-input and single-output systems.

The present invention proposes a simulation analysis method for the compaction disease of heavy-duty railway track bed based on the ping-pong model, which belongs to the field of nondestructive testing of ground penetrating radar for railway engineering, including: calculating the overall equivalent dielectric constant and the equivalent dielectric constant of the compaction area based on the ping-pong model; using gprMax to simulate the ping-pong model, and calculating the simulated overall equivalent dielectric constant and the equivalent dielectric constant of the compaction area based on the simulation results; calculating the gprMax compaction area equivalent dielectric constant prediction formula according to the overall equivalent dielectric constant, the compaction area equivalent dielectric constant, the simulated overall equivalent dielectric constant and the compaction area equivalent dielectric constant, so as to realize the simulation and analysis of the compaction disease of heavy-duty railway track bed based on the ping-pong model. The present invention proposes a "model-simulation" compaction area equivalent dielectric constant prediction formula, which is based on the "ping-pong model" modeling, and only relies on the parameters of the model itself, without running the simulation, to obtain the predicted value of the compaction degree.

The invention belongs to the field of power system operation. A heating type electric load short-term prediction method based on data-knowledge fusion driving under demand response is characterized by comprising the steps that heating type electric load interaction response knowledge under demand response and other prediction influence factors serve as features, and a data-knowledge fusion driving prediction model is introduced for training; a heating type electric load prediction value which is high in prediction and close to the actual power utilization level is obtained; according to the data-knowledge fusion driving model, heat supply type electric load demand response signals, user response rate, uncertainty and other knowledge, other common prediction influence factors and the like serve as data driving model input characteristics, a data-knowledge fusion driving prediction model is established, and model training is conducted under knowledge and data dual driving. According to the method, the prediction precision can be improved, the prediction result better fits the actual power consumption level, the modeling difficulty is reduced, and the generalization and interpretability of the prediction model are improved.

The present invention belongs to the field of engines and computational fluid dynamics, and discloses a surface source loading method for simulating fuel injection in aerospace engine combustion chambers. The surface source loading method for simulating fuel injection in aerospace engine combustion chambers proposed by the present invention is aimed at the requirements of fast, efficient and accurate numerical simulation of fuel injection in aerospace engine combustion chambers. Without reducing the accuracy of numerical simulation of the combustion chamber, the nozzle is set as a surface source, and the mass source term, momentum source term, energy source term and mixed fraction source term are added to the control equation group, which reduces the difficulty of complex engineering configuration combustion chamber grid modeling and the number of overall combustion chamber grids, better describes the near-field physical characteristics of the nozzle, makes the flow field converge faster, improves the efficiency of combustion chamber design evaluation, and has good engineering practical value.

The present invention relates to a soft measurement method for a typhoon-proof suspension system driven by a mechanism and data hybrid. The method first identifies the structural characteristics of the typhoon-proof suspension system, and determines the auxiliary variables according to the monitored dominant variables; secondly, based on the selected dominant variables and auxiliary variable types, the sensor type and monitoring position are determined, the monitoring data are collected and data processing is performed based on the multi-source information fusion technology, and a monitoring data history library is established; thirdly, a mechanism model of the typhoon-proof suspension system is established, a data-driven model is established based on a machine learning algorithm, model parameter identification is carried out in combination with the monitoring data, and a soft measurement model of the typhoon-proof suspension system is proposed by integrating the mechanism model and the data-driven model; finally, the soft measurement data is compared with the monitoring data, and the established soft measurement model is corrected online and offline. The present invention realizes the soft measurement of the difficult and unmeasurable data of the typhoon-proof suspension system by constructing a mechanism and data hybrid driven model, and improves the accuracy of the soft measurement data.

The invention discloses a network construction type direct-driven fan impedance modeling method considering a frequency coupling effect. The method comprises the following steps: constructing a main circuit small signal model of a network construction type direct-driven fan grid-connected system including power grid impedance in a frequency domain; based on a multi-harmonic linearization theory, obtaining an analytical expression between modulation coefficient disturbance and grid-connected point voltage, current and inductive current disturbance signals; combining the main circuit small signal model with the analytical expression to obtain an overall impedance matrix of the grid-connected system of the grid-forming type direct-driven wind turbine generator; using the overall impedance matrix to obtain the overall impedance under the disturbance frequency omega p, and subtracting the impedance component corresponding to the frequency omega p in the power grid impedance matrix from the overall impedance to obtain the equivalent impedance of the grid-forming type direct-driven wind turbine generator. According to the method, the modeling difficulty can be effectively reduced, the accurate and reliable equivalent impedance can be obtained, and technical support is provided for accurately analyzing the stability of the grid-connected system.

The application relates to a CAE-based light-emitting grid mist simulation method, which comprises the following steps: constructing a light-emitting grid product model based on three-dimensional data of the light-emitting grid and defining an analysis model; defining material parameters of parts of the light-emitting grid product model; defining outer wall surface simulation parameters of the light-emitting grid product model; defining the heat power of electronic components of the light-emitting grid product model; completing local mesh encryption of each part of the light-emitting grid product model; defining the inner surface of a mask of the light-emitting grid product model as a monitoring target, and defining a physical quantity as film thickness; setting solving parameters and performing calculation to obtain the film thickness of the inner surface of the mask of the light-emitting grid product model. The application can guide the internal structure design of the light-emitting grid and the layout of air holes on the shell.

The present invention provides a slope stability analysis method, characterized by comprising: collecting point cloud data of a slope environment; converting the point cloud data into a set of slope contour lines; constructing a set of slope models using the set of slope contour lines, and performing stability analysis on the set of slope models. The present invention improves the efficiency of stability analysis while ensuring the quality of stability analysis by converting a set of two-dimensional slope contour lines from three-dimensional data. The present invention also provides a slope stability analysis device, equipment and storage medium.

The present invention discloses a model-free robust control method and system for pantographs with external disturbances; dynamic data of the pantograph operation process of a train is collected; a train pantograph data model with external disturbances is established; external disturbances are estimated; pseudo partial derivative information in the train pantograph data model is optimized and updated; a model-free robust control scheme for train pantographs with external disturbances is designed; error control gain and disturbance compensation control gain in the control scheme are optimized and updated; a control input signal is calculated using the model-free robust control scheme to control the train pantograph until the control task is completed; the control method and system provided by the present invention are not only applicable to train pantographs with external disturbances, solve disturbance compensation problems, reduce modeling difficulty, and reduce computing resource consumption, but also have the prospect of being extended to other single-input and single-output systems.