897 results about "Virtual control" patented technology

The invention provides a real-time transmission method and system based on internet-of-things sensing data in a digital twinborn scene, and the method comprises the steps: collecting and processing vibration spectrum data, pressure gradient change data and temperature distribution data of equipment in an industrial production line, generating a preprocessing data package, carrying out the analysis to obtain an equipment state fingerprint feature and an environment working condition label, and carrying out the real-time transmission of the environment working condition label. A target virtual control parameter and a target error compensation amount are generated by using a dynamic parameter correction loop of the digital twin, an actual material flow state is generated in combination with material flow rate data, an optimization strategy sequence of multi-device collaborative operation is generated by using a distributed computing architecture, and the optimization strategy sequence is converted into a track compensation parameter of an end effector in the device; in combination with the target error compensation amount, data closed-loop transmission is carried out; according to the invention, millisecond-level time domain matching of vibration, pressure and temperature data and material flow velocity is realized through a dynamic timestamp alignment mechanism of 5G edge computing nodes, and the problem of synchronization error accumulation of traditional internet-of-things sensing data is solved.

In some embodiments, an electronic device enhances interactions with virtual objects in a three-dimensional environment. In some embodiments, an electronic device enhances interactions with selectable user interface elements. In some embodiments, an electronic device enhances interactions with slider user interface elements. In some embodiments, an electronic device moves virtual objects in a three-dimensional environment and facilitates accessing actions associated with virtual objects.

The invention relates to the technical field of factory management. The intelligent factory management method based on digital twinning comprises the following steps: performing dynamic identification and analysis processing on a multi-source heterogeneous communication protocol of factory equipment to obtain a standardized data stream; constructing a three-dimensional digital twinborn model comprising an equipment state and a technological process; generating a virtual control instruction according to an analog control strategy in the three-dimensional digital twin model; sandbox security verification processing is carried out on the virtual control instruction, and a compliance instruction set is screened out; converting the compliance instruction set into a protocol format supported by the target equipment, generating a reverse control instruction and issuing the reverse control instruction to the physical equipment; performing closed-loop feedback optimization processing according to the difference between the execution result of the physical equipment and the prediction result of the three-dimensional digital twinborn model, so as to solve the problem that the digital twinborn model lacks real-time sensor data injection and dynamic behavior constraint embedding, ensure that the model can reflect the real state of the equipment, and improve the real-time performance of the equipment. And the reliability and the execution security of the virtual control instruction are improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a low-power-consumption star flash control module applied to an unmanned aerial vehicle and a dummy pilot control method. The method comprises the following steps: acquiring an original control instruction of a real flying hand, and converting the original control instruction into an encrypted pulse signal containing a dynamic key; fusing a frequency spectrum camouflage signal simulating the environment background of a dummy pilot into the encrypted pulse signal, and then transmitting the encrypted pulse signal to the dummy pilot in a self-adaptive frequency hopping mode; synchronously analyzing the dynamic key at a dummy flying hand end and stripping the frequency spectrum camouflage signal to generate a virtual control track matched with the real operation behavior characteristics; and reversely modulating an actual response signal of the unmanned aerial vehicle based on the virtual control track, so that the unmanned aerial vehicle executes an action and forms dynamic error compensation with the virtual control track. According to the invention, a real flying hand can be effectively protected from electronic reconnaissance positioning and attack of an enemy, and the concealment is improved.

The invention discloses a data-based predefined time heterogeneous multi-agent formation collision avoidance method. The method comprises the following steps: establishing a nonlinear heterogeneous multi-agent system; designing a bimodal artificial potential field function to perform dynamic obstacle avoidance and prevent regional escape; establishing a self-adaptive robust controller used for generating an obstacle avoidance safety motion trail of the root leader; designing a self-adaptive formation zooming mechanism of the leader, and constructing a predefined time affine observer of the follower based on the self-adaptive formation zooming mechanism; designing a unified obstacle function; constructing a virtual control law for processing tracking errors based on the unified obstacle function; designing a neural network estimator; designing controllers of the leader and the follower according to the virtual control law; and forming a collision avoidance decision of the heterogeneous multi-agent formation based on a self-adaptive robust controller, a predefined time affine observer, a neural network estimator and controllers of the leader and the follower. According to the method, safe, efficient and robust cooperative control of the formation in a complex environment is realized, and the safety and task execution efficiency of the heterogeneous multi-agent formation in a limited and unknown environment are remarkably improved.

The invention provides a quadrotor unmanned aerial vehicle suspension swing suppression flight control method based on a DDPG. The method comprises the steps that a kinetic model of a quadrotor unmanned aerial vehicle suspension system is established; performing input shaping processing on an expected reference track signal by using a ZVD input shaper; the unmanned aerial vehicle suspension system is decoupled into position control of a position loop and attitude control of an attitude loop by adopting a cascade trajectory tracking control strategy, and a fuzzy PID controller of the position loop is designed based on a fuzzy theory; the shaped track signal is input into a fuzzy PID controller, and parameters of the PID controller are adjusted on line; performing attitude settlement on the virtual control quantity output by the fuzzy PID controller, and inputting the virtual control quantity into an active disturbance rejection controller; roll, pitch, and yaw torque control quantities are generated based on the virtual control quantity of the attitude loop and the control quantity generated by the DDPG-based tilt angle controller. Under a complex external interference environment, a preset track is tracked with high precision, load swing is effectively suppressed, reliable execution of a transportation task is successfully guaranteed, and meanwhile, the control precision and the anti-interference capability of the system are remarkably improved.

The invention relates to the technical field of industrial distributed control system architecture, and discloses a dual-redundancy design method for a virtual controller VDPU, which comprises the following steps of: deployment of a four-network-port redundant network architecture: dividing four physical network ports into two groups of independent channels, and simultaneously configuring a network link automatic switching mechanism, the working states of the four network ports are monitored in real time, then double isolation of a physical layer and a protocol layer is achieved, and an independent heartbeat transmission channel is established; main / standby heartbeat monitoring and switching control: the main / standby controller periodically interacts state information packets through a special heartbeat channel, and establishes a three-level fault determination mechanism at the same time to perform seamless main / standby switching; a multi-mode data synchronization mechanism is implemented, namely initialization synchronization, increment synchronization during operation and exception recovery synchronization are started, separation of heartbeat signals and data synchronization can be realized through two groups of independent channels, and the fault-tolerant capability and the switching efficiency of the system are improved, so that the operation stability of the system is guaranteed.

Systems, interfaces, and methods for implementing the systems and interfaces include a dynamic environment generation subsystem that changes objects and subobjects based on locations of the motion sensors and / or the nature, time and / or location of sensed motion and include selection attractive movement as the selection protocol, where a selection object is used to discriminate between selectable objects and attract a target object toward the selection objects, where the direction and speed of the motion controls, discriminates, attracts, and activates the selected objects.

The self-adaptive switching control method for the unmanned surface vehicle comprises the following steps: establishing a mathematical model of the unmanned surface vehicle, and determining a control target of the unmanned surface vehicle; designing a virtual control law and a first Lyapunov alternative function for controlling the unmanned surface vehicle based on a backstepping method; designing a second Lyapunov alternative function, establishing an adaptive neural network controller model and a robust controller model, and designing control input of the unmanned surface vehicle switching control system; designing updating laws of an adaptive neural network controller and a robust controller, and designing a third Lyapunov function to perform stability analysis on the system; and testing and simulating the unmanned surface vehicle system in a simulation environment by using the adjusted control strategy. According to the control method, on the premise of ensuring the stability of the system, the path tracking error of the unmanned surface vehicle is reduced, and the energy consumption of the controller is reduced.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The invention relates to the technical field of mechanical arm control, in particular to a self-adaptive fuzzy fixed time control method based on an output-limited mechanical arm, and the method comprises the following steps: S1, constructing a mechanical arm dynamic model; s2, setting constraint conditions of an output state of the mechanical arm dynamic model; s3, defining a tracking error and a virtual control law of the mechanical arm dynamic model; s4, on the basis of output state limitation and fixed time control, an actual control law is designed to serve as input of the mechanical arm dynamic model, and an adaptive law is constructed; and S5, a three-level logarithmic obstacle Lyapunov function is constructed, the three-level logarithmic obstacle Lyapunov function is combined with the virtual control law, the actual control law and the self-adaptive law step by step and substituted into the virtual control law, the actual control law and the self-adaptive law, and then it is guaranteed that the output state of the mechanical arm dynamic model does not exceed the preset safety boundary according to the actual fixed time control lemma. According to the invention, high-precision trajectory tracking can be realized on the premise of ensuring safe operation of the mechanical arm.

The invention discloses a fully-mechanized coal mining equipment group sensing-control integrated twinning device and method, and relates to the technical field of coal mine intelligent mining digital twinning. The method comprises a sensing information driving mode, a physical instruction driving mode and a dual-driving mode. When the sensing information driving mode lacks physical instruction information, real-time sensing data of the hydraulic support are collected, sensing-control integration is achieved, and the shape is consistent; the physical instruction driving mode is used for speculating a current posture through a speed parameter training model and an optimal force training model when sensing information is lost, so that virtual-real synchronization is realized; and the dual-drive mode is used for realizing real-time consistent action and regulation of the virtual monitoring model and the virtual control model according to an adaptive control strategy when the physical instruction transmission path and the sensing information transmission path are both intact. According to the method, high-fidelity actions of the virtual control model and the virtual monitoring model can be driven through the physical entity, and virtual reconstruction of the working space is completed in the virtual space.

The invention relates to a control method and system for a flexible joint robot under input delay, and the method comprises the steps: carrying out the modeling according to the motion state of the flexible joint robot, and introducing the input delay into a constructed model; according to the physical characteristics of the flexible joint robot, the constructed model is converted into a state equation; introducing a specified performance function with preset time into the state equation obtained by conversion; constructing an error conversion function, and converting a tracking error constraint problem in the control period of the flexible joint robot into a bounded problem by utilizing the error conversion function and a specified performance function; through an auxiliary system and coordinate transformation, the influence of input delay is eliminated, and a compensated tracking error signal is obtained; and defining a performance index function including an error index and a control input index, designing a virtual controller by taking the minimization of the performance index function as a target, and obtaining unknown parameters in the virtual controller by using a neural network.

The invention discloses an intelligent regulation and control method for a power battery positive electrode binder production process, and the method comprises the steps: synchronously collecting multi-dimensional process parameters such as temperature, viscosity and the like and performance indexes such as particle size distribution, bonding strength and the like, carrying out the normalization, denoising and time sequence alignment processing, and fusing hydrodynamic simulation and historical data to construct an initial parameter coupling model; identifying a parameter influence weight through an attention mechanism neural network, generating a decoupling factor matrix to reconstruct a parameter space, and establishing a virtual control channel; executing constrained gradient descent multi-objective optimization in the channel, generating a regulation and control instruction, and reversely mapping the regulation and control instruction into an equipment executable parameter; according to the method, performance indexes and optimization targets after regulation and control are continuously compared, model parameters are updated, retraining is triggered when deviation exceeds a threshold value, regulation and control precision and process stability are guaranteed, and the real-time performance of parameter regulation and control, the collaborative optimization capability and the quality consistency of the production process are remarkably improved.

The invention provides an exoskeleton robot self-adaptive control method and related equipment, and relates to the technical field of exoskeleton robots. The method comprises the following steps: acquiring motion information and position information of each movable joint in the exoskeleton robot; inputting the motion information and the position information into a pre-trained angle prediction model to obtain a target angle of each movable joint; the difference value between the target angle and the actual angle output by the exoskeleton controller serves as an angle error; constructing a virtual control quantity based on the angle error; the virtual control quantity is combined with motion parameters of the exoskeleton robot to be input into a disturbance observer, and estimated disturbance is generated; the angle error, the virtual control quantity and the estimated disturbance are input into an adaptive neural network controller, and the control torque of each movable joint is obtained; and based on the control torque of each movable joint, generating a control signal of each movable joint. The method and the device have good control precision.

The invention provides a stipulated performance optimal backstepping control method and system for an unmanned ship formation, and relates to the technical field of unmanned ship optimal backstepping control, and the method comprises the steps: building a dynamic model of multiple unmanned ships, and compensating the external interference and unknown nonlinear terms of the dynamic model through a disturbance observer and a neural network identifier; an optimal virtual controller is constructed, a derivative of optimal virtual control is calculated by introducing a first-order integral filter, and then an optimal actual controller is constructed; designing a monotonic tube boundary function by utilizing the characteristics of a hyperbolic cosecant function; formation control is carried out through a virtual controller and an actual controller by using a continuous control law and a monotone control boundary function, and the position of the following unmanned surface vehicle is controlled to be synchronous with the expected trajectory of the leading unmanned surface vehicle under the condition of external disturbance; according to the method, the disturbance observation estimator is used for time-varying external disturbance, a neural network is used for approximating unknown parameters of a dynamic model, and a monotone pipe boundary function is used for enabling formation errors to evolve in a specified area.

The invention discloses a large-span truss hoisting and splitting system and method based on digital twinning, and belongs to the technical field of building construction, and the system comprises a physical entity unit, a data acquisition and transmission unit, a digital twinning model unit, a function service and data analysis unit and an application and interaction unit. According to the invention, through real-time sensing of a physical entity state, multi-source data fusion transmission, high-precision digital twinning modeling, intelligent simulation analysis and decision optimization, and fusion of multi-modal man-machine interaction, a virtual control system which is in whole-course synchronous mapping and bidirectional interaction with a physical entity is constructed; according to the method, real-time visual monitoring, trend prediction and risk early warning of the hoisting and splitting process can be achieved, scheme preview and dynamic optimization are supported, virtual-real fusion operation guidance is achieved through the AR technology, and the safety, precision and efficiency of hoisting and splitting operation of the large-span truss are improved.

The invention discloses a ship formation autonomous obstacle avoidance control method based on offset intervention guidance. The method comprises the steps of obtaining a nonlinear mathematical model of a three-degree-of-freedom ASV; acquiring a smooth reference path for USV ship formation according to the virtual ship L1VS, and acquiring a ship obstacle avoidance virtual reference path according to the obstacle avoidance virtual ship VS in combination with a DWA algorithm; designing a kinematics virtual control law of the USV ship formation based on the constructed offset intervention guidance mechanism; on the basis of the constructed dynamic event triggering mechanism, a robust neural damping technology is combined, and a robust kinematics error is obtained according to the kinematics error of the USV ship formation; and designing an adaptive robust command filtering controller according to the robust kinematics error so as to realize autonomous obstacle avoidance control of the ship formation. The problems that according to a traditional ship formation control method, when a collision avoidance task in an unknown water area is executed in a complex marine environment, information of moving and fixed obstacles cannot be obtained in time, and communication load balancing and obstacle avoidance control precision cannot be effectively achieved are solved.

The invention discloses a finite-time active-disturbance-rejection control method for a multi-unmanned-aerial-vehicle hanging transportation system. According to the finite-time active-disturbance-rejection control method, a finite-time super-spiral sliding-mode observer is used for carrying out observation compensation on load errors and lumped disturbance of the load errors, a finite-time sliding-mode controller is constructed, total tension for controlling load movement is determined, and an optimal tension distribution scheme is solved. External disturbance is introduced, a load is taken as a virtual leader, a leader following cluster model is constructed, a finite time reduced-order proportional differential observer and a finite time controller are designed, formation finite time control is carried out, and an expected trajectory of each unmanned aerial vehicle is determined; an actuator fault model and a virtual control quantity are introduced, an unmanned aerial vehicle dynamics model is constructed, and a lumped disturbance reconstruction state error system is introduced; and designing a finite-time reduced-order generalized parameter estimation observer and a finite-time nonsingular terminal integral sliding mode fault-tolerant controller, carrying out unmanned aerial vehicle position loop control and attitude loop control, and realizing finite-time active-disturbance-rejection control of the multi-unmanned aerial vehicle hanging transportation system.

The invention discloses a unified management system and method for a virtual controller, and the system comprises a controller registration module which is used for initializing a controller registration array, checking whether the number of network IO registration reaches an upper limit or not, traversing the controller registration array, searching vacancies, and recording new instance information; the instance mapping module is used for mapping the successfully registered controller indexes into a controller information array and updating the registration number; the multi-thread security module is used for realizing concurrent security access in a registration process through a mutual exclusion lock; and the communication management module is used for creating a read management module, a write management module and an alarm management module, processing different types of data and carrying out heartbeat monitoring and data forwarding through a port. The technical problems that when a plurality of virtual controllers work cooperatively, control tasks of the virtual controllers are difficult to coordinate efficiently, so that the system performance is reduced, and even control conflicts or data loss may occur are solved.

The invention relates to a non-linear multi-unmanned vehicle system self-triggering time-varying formation control method, which comprises the following steps: constructing a second-order dynamic model for a non-linear multi-unmanned vehicle system, and describing communication between unmanned systems through a directed topological structure; a backstepping method is adopted, a virtual controller is introduced, and a leader-follower formation error system under the triggering condition is constructed; designing influence weights of communication resources and control performance, and respectively constructing an optimization model based on virtual input and formation distributed position errors and an optimization model based on real input and tracking formation speed errors; solving an approximate solution of a corresponding Hamiltonian-Jacobi-Bellman equation through a simplified reinforcement learning method, and sequentially obtaining an optimal virtual control protocol and an optimal reality control protocol; similarly, determining a self-triggering prediction condition; and finally, under the condition of self-triggering prediction, the multi-unmanned vehicle system not only realizes time-varying formation, but also balances communication resources and control performance.

A rigid aircraft predetermined time adaptive neural network attitude stabilization method comprises the following steps: establishing kinematics and dynamics models of a rigid aircraft system, introducing a hysteresis quantizer to reduce buffeting in a quantized signal, and estimating and compensating lumped uncertain items of the system by using a neural network; a virtual controller is designed based on an inversion recursion idea, and the problem of controller singularity caused by derivation of the virtual controller is avoided; on the basis, a preset time adaptive neural network attitude controller is designed. According to the method, under the condition that inertia uncertainty, external interference and quantitative input exist, the rigid aircraft attitude is converged to the neighborhood near the original point within the preset time, and the upper bound of the minimum convergence time can be predetermined by adjusting a simple parameter.

The invention discloses an anti-ocean-current trajectory tracking control method and system for a jet-arm submarine cable laying robot. The method comprises the steps that an anti-ocean-current motion model of the jet-arm submarine cable laying robot is constructed; acquiring a position error and a course tracking error according to a given reference trajectory based on the ocean current resistance motion model, and constructing a preset time controller; according to the preset time, the controller obtains a virtual controller of the jet arm seabed cable laying robot so as to define a longitudinal speed error and an angular speed error; constructing a parameter adaptive law according to the longitudinal velocity error and the angular velocity error; and constructing a disturbance observer based on the virtual controller and the parameter adaptive law to obtain an actual controller for ocean current trajectory tracking of the jet arm submarine cable laying robot. Aiming at the problem that an existing submarine cable laying robot cannot realize accurate trajectory tracking in a complex submarine environment, a more perfect ocean current resistance motion model is established, and a disturbance observer is introduced to estimate disturbance in real time so as to realize accurate control; a preset time theory is adopted, so that the system can be converged to a preset time, and the robustness is better; the disturbance observer is used to more accurately compensate the interference of ocean current of the seabed, and the anti-interference capability of the submarine cable laying robot is improved.

The invention discloses a ship anti-spoofing attack control method based on a self-feedback expansion state observer. The ship anti-spoofing attack control method comprises the steps that a guidance virtual ship and a dynamic virtual ship are set to construct an L2-DVS guidance strategy; constructing a spoofing attack additional signal model of the unmanned ship, and obtaining a ship position signal under the spoofing attack according to the kinematic model of the target unmanned ship; acquiring a longitudinal velocity virtual control law of the target unmanned ship and a spoofing attack compensation law of a yaw angle according to the constructed error model so as to obtain a yaw velocity virtual control law / adaptive law; and constructing a state memory type event triggering mechanism, obtaining a controller / adaptive law of a rudder angle / rotating speed of the target unmanned ship in combination with a self-feedback compensation reduced order expansion state observer, and realizing anti-spoofing attack control of the target unmanned ship according to the yaw velocity virtual control law / adaptive law and the controller / adaptive law of the rudder angle / rotating speed. The problems that the research of the application in the current network attack environment is not complete enough, the unmanned ship is disturbed all the time in the marine environment, the actuator continuously responds to the disturbance, and the excessive wear of the steering engine is greatly increased are solved.

The invention discloses a hydraulic mechanical arm motion control method based on low-pressure volume elastic modulus compensation. The method comprises the steps that a dynamic linearization model of the multi-degree-of-freedom hydraulic mechanical arm containing the effective volume elastic modulus is established, a backstepping method is used for designing a self-adaptive robust controller, an expected joint angle is input into the self-adaptive robust controller, meanwhile, online self-adaptive parameter updating is carried out, and the optimal joint angle is obtained. The self-adaptive robust controller outputs virtual control flow of the multi-degree-of-freedom hydraulic mechanical arm after processing, and valve port control voltage is obtained after static mapping so as to control a hydraulic valve; and the multi-degree-of-freedom hydraulic mechanical arm outputs the actual control flow, the actual joint control torque and the actual joint angle to the self-adaptive robust controller in real time, and motion closed-loop control over the multi-degree-of-freedom hydraulic mechanical arm is achieved. According to the method, the high-precision motion tracking performance can be achieved under the condition that the hydraulic mechanical arm keeps stable low pressure.

The invention discloses a reinforcement learning-based multi-unmanned ship formation hunting control method, which comprises the following steps of: constructing a multi-unmanned ship motion system influenced by environmental interference, and constructing a hunting formation error variable; constructing a virtual controller of the unmanned ship by adopting a backstepping method based on the encircling formation error variable; constructing a leader ship formation hunting controller based on a reinforcement learning method in combination with a neural network; constructing a following ship formation hunting controller based on an adaptive backstepping method in combination with the Lyapunov theory and a neural network; and according to the leading ship formation hunting controller and the following ship formation hunting controller, formation hunting control of the target ship is realized. The problem that the control performance of the multiple unmanned ships is insufficient due to the interference of complex marine environments such as wind, waves and flow in the process of efficiently executing a hunting task in the formation of the multiple unmanned ships, the cooperative relationship between the chasing unmanned ships cannot be fully reflected, the formation of the multiple unmanned ships is difficult to control, and the hunting efficiency of the multiple unmanned ships is improved is solved. And the control is difficult to realize at the cost as low as possible.

The invention relates to the technical field of TEC semiconductors, and discloses a temperature control method, device and equipment based on a TEC semiconductor, and the method comprises the steps: carrying out the distributed temperature collection and preprocessing of a region to be subjected to temperature control, and obtaining a temperature correlation matrix; performing temperature anomaly detection to obtain a temperature anomaly feature matrix; calculating a temperature influence range of the TEC controller, and mapping the control group to a virtual control network; performing global temperature optimization to obtain a global optimal control variable, and dividing the global optimal control variable into local control instructions according to control groups; a temperature control cost function is constructed, and TEC control parameters are obtained through gradient iteration optimization calculation; and TEC control parameters are input into the temperature dynamic prediction model, robust optimization is carried out in a rolling prediction interval to obtain a local optimal control sequence, and temperature closed-loop control is executed. According to the invention, reasonable distribution of control resources is realized, mutual interference among TEC controllers is reduced, and accuracy and reliability of temperature control are ensured.

Provided in the present invention is a multi-purpose modular operation optimization system for a municipal solid waste incineration process. The system comprises a multi-modal historical data synchronous driving module, an MSWI-process virtual control object module, an MSWI-process loop control module, an MSWI-process monitoring module, an operation parameter assisted decision-making module, a data-acquisition forward isolation module, an operation parameter reverse transmission module, an MSWI-process single-objective / multi-objective operation optimization module, a difficult-to-measure procedure parameter soft-measurement module, a multi-modal data driven procedure parameter prediction module, a vision-driven combustion state identification module, a flame combustion line quantification module and a multi-modal data acquisition module. The multi-purpose modular operation optimization system for a municipal solid waste incineration process provided in the present invention can achieve functions such as laboratory operation-optimization algorithm verification, laboratory procedure-parameter-modeling algorithm simulation and real-time verification, industrial field data acquisition and procedure parameter modeling, and industrial field assisted decision-making and operation optimization, and the system is convenient to use.

The invention discloses an IC generation method of a virtual controller, a product, a medium and equipment, and relates to the technical field of nuclear power simulators, and the method comprises the steps: obtaining a modified project file; compiling the modified project file, and adding a point table generation operation in the compiling process to obtain a point table file; downloading the point table file into a virtual controller of a lower computer; in a virtual controller of the lower computer, performing IC reset operation on the old IC file based on the point table file to obtain a modified IC file; the old IC file is an IC file corresponding to a project file before modification; and storing the modified IC file. According to the IC generation method of the virtual controller, the product, the medium and the equipment, the flexibility and the efficiency of IC file upgrading can be effectively improved.

The invention relates to the technical field of robot control, and discloses a mechanical arm specified performance fault-tolerant control method based on a fuzzy logic system, and the method comprises the steps: building a kinematics and dynamics model and a fault model of an agricultural robot; establishing a relative error dynamic equation between the actual position and the expected position; designing a specified performance function, and constraining the tracking error within an expected range by using the specified performance function; on the basis, designing a virtual control law by using a backstepping method under the condition of meeting error constraints; designing an adaptive control law by adopting a fuzzy logic system based on an uncertain item, unknown environment interference and a fault model in the kinetic model; a specified performance controller is designed based on a virtual control law, fuzzy logic adaptive control and full-state feedback agricultural robot, and specified performance fault-tolerant control of the agricultural robot is realized. The stability proves that the tracking error of the agricultural robot converges to any small neighborhood near zero within fixed time.