172 results about "Equilibrium control" patented technology

The invention discloses an intelligent cooperative switching control system and method for multi-path valve runners, and the method comprises the steps: collecting all-condition operation data of a multi-path valve system, extracting valve switching characteristic parameters, and building a runner mapping relation between paths; a cascade action chain containing time sequence dependence is constructed based on the mapping relation and the switching characteristics, and the action sequence and the trigger condition of each drift diameter are determined; by analyzing the pressure distribution and the flow velocity change in the cascade action chain, predicting the water hammer strength and designing a reverse pressure wave parameter, and generating a water hammer counteracting control instruction; high-low pressure area distribution in the flow field is identified, a flow field complementary cooperation scheme is constructed, and a pressure balance control strategy is generated; analyzing a pressure wave propagation path to determine a superposition elimination point, extracting pressure pulse resonance characteristics and generating time sequence control parameters; and the sequential control parameters are matched with the cascade action chain, synchronous control nodes are determined through delay compensation, and a time-sharing control sequence is generated to achieve intelligent cooperative switching of the multi-drift-diameter valve flow channels.

The invention discloses a quadruped robot motion control method and system based on reinforcement learning, and the method can simulate and analyze the motion behavior of a quadruped robot in a virtual environment through building a simulation model of the quadruped robot, remarkably reduces the research and development cost, remarkably shortens the research and development period, converts a balance control problem into a Markov decision process, defines a state, an action and a reward function, and improves the reliability of the quadruped robot. A more intelligent and adaptive balance strategy can be realized, a deterministic strategy gradient algorithm and a simulation model are adopted for interactive training, an efficient balance control strategy can be obtained, robot behaviors are adjusted in real time to keep balance, a walking task is decomposed into two sub-tasks of foothold selection and gait generation by utilizing a hierarchical control idea, and the walking efficiency is improved. Through reinforcement learning, walking flexibility and stability of the robot on irregular terrains are improved, the irregular terrains are built on a simulation platform for experimental verification, robot performance can be safely and repeatedly evaluated, and a strategy is further optimized.

The invention discloses an intelligent heat supply two-network balance control method and system based on fuzzy reinforcement learning, and the method comprises the steps: obtaining real-time operation parameters which comprise the return water temperature, the heat load and the environment temperature; based on the real-time operation parameters, preliminarily generating a preliminary adjusting quantity of the opening degree of the valve; operation data are analyzed in real time, fuzzy rule parameters are adjusted according to feedback of the reinforcement learning module, and the fuzzy rule parameters comprise priority weights and membership functions of return water temperature deviation; and according to the preliminary adjustment effect, a deep learning algorithm is utilized to construct a state-action space equation, an energy consumption design reward function is considered, the priority weight is further optimized and fed back to a dynamic optimization module of a fuzzy rule, and secondary adjustment of the valve opening degree is output to an execution mechanism. The problems that a traditional fuzzy control method cannot adapt to complex working condition changes in the operation of the heat supply two-network, the adjusting strategy lags behind, the adjusting effect is not accurate enough, and the adjusting efficiency and energy consumption are affected are solved.

A balance control method for a wheel-legged robot is provided. The robot includes a moving wheel, n links, and n rotating joints, the moving wheel being connected to a first link through a first rotating joint of the n rotating joints, and the n links being connected in series through n−1 rotating joints other than the first rotating joint, n being a positive integer greater than 1. The method includes: acquiring a state quantity of the wheel-legged robot at a first moment; determining dynamics model parameters according to a dynamics equation of the wheel-legged robot and the state quantity at the first moment; establishing a sliding surface according to the state quantity at the first moment; calculating rotation torques of the n rotating joints according to the sliding surface and the dynamics model parameters; and controlling the rotating joints according to the rotation torques of the n rotating joints.

The invention provides an unmanned aerial vehicle flight attitude self-adaptive imbalance control method, device and equipment, and aims to accurately obtain an ideal control track and recognize a beneficial imbalance mode to generate a controllable imbalance strategy library by constructing a reverse track analysis and controllable imbalance strategy generation mechanism. A dynamic balance reference establishment and track reconstruction technology is adopted to construct an unbalance-recovery control track, and dynamic response characteristics are accurately obtained; an unbalance recovery space-time manifold construction and topology analysis technology is introduced, a core unbalance control feature set is extracted through space-time folding transformation, and unbalance control key parameters are determined; establishing an unbalance-recovery mapping rule and self-adaptive parameter fusion mechanism, balancing the control effect and the safety requirement, and generating a disturbance control parameter and a self-adaptive mapping parameter; a real-time control output regulation mechanism is constructed to generate a flight control signal in combination with reverse unbalance combined control and a multi-axis cooperative control mechanism; finally, a self-adaptive unbalance control system is formed, and precise unbalance control over the attitude of the unmanned aerial vehicle is achieved.

The invention provides a gate valve pressure balance control method and device, electronic equipment and a storage medium, and relates to the technical field of gate valve pressure control. The method comprises the steps of monitoring a gate valve control signal and a pressure difference between two sides of a gate valve; calculating a gate valve control signal change rate according to the gate valve control signal, and setting a control signal amplitude threshold value and a change rate threshold value; according to the gate valve control signal change rate, the control signal amplitude threshold value and the change rate threshold value, the gate valve switching action is predicted; based on the predicted gate valve switching action, according to the pressure difference of the two sides of the gate valve, the number of balance channels needing to be opened when the pressure balance is achieved on the two sides of the gate valve is judged; the controller is controlled in advance to open the corresponding number of balance channels to balance the pressure on the two sides of the gate valve. According to the gate valve pressure balance control method, the problem that in the prior art, the response speed is insufficient in the aspect of gate valve pressure balance control is solved, and the effects of remarkably improving the pressure balance response speed and effectively restraining hydraulic impact and vibration are achieved.

The invention relates to the technical field of robots, in particular to a robot stability and balance control method based on an embedded depth fusion algorithm, and the method comprises the following steps: S1, sensor data collection: configuring a plurality of sensors to collect the motion state and environment information of a robot in real time; s2, dynamic scene understanding: performing real-time analysis on the acquired sensor data, and identifying potential obstacles and changes in the environment; s3, cooperative control based on behavior prediction: predicting future behaviors of a plurality of robots, and dynamically adjusting a cooperative strategy; and S4, real-time feedback adjustment: comparing the current state of the robot with a preset stable state through a multi-level feedback mechanism, and intelligently adjusting a control strategy based on real-time environment change and task requirements. According to the invention, the adaptability and task execution efficiency of the system are improved, and more intelligent and flexible cooperative work can be realized in a complex scene.

The invention discloses a surface-mounted permanent magnet synchronous motor sensorless high-performance sliding-mode control method, and the method comprises the steps: designing an improved self-adaptive terminal sliding-mode switching function for the problem of high-frequency buffeting of a sliding-mode observer caused by the fixed gain design of the switching function; and dynamically adjusting the gain of the switching function according to the sum of the stator current error and the integral thereof under the two-phase static coordinate system so as to suppress high-frequency buffeting and improve the estimation precision. In order to further improve the performance of the position sensorless control system, a reaching law in a sliding mode controller is optimized, a novel composite gain self-adaptive sliding mode reaching law is designed, the reaching law combines a self-adaptive gain term, a nonlinear exponential term and a linear term, the system state is accelerated to approach when being far away from a sliding mode surface, buffeting is reduced when the system state approaches, and the control precision of the position sensorless control system is improved. Therefore, the dynamic response speed and buffeting suppression capability of the control system are balanced.

The invention relates to the technical field of robot control, and provides a robot body self-balancing control method applied to a quadruped robot, the robot body self-balancing control method comprises the following steps: S1, detecting a slope angle and attitude offset data of the quadruped robot in real time, the attitude offset data comprising roll angle offset and pitch angle offset; s2, transmitting the slope angle and attitude offset data to a main control system; s3, the main control system executes partition compensation control according to the attitude offset data: when roll angle offset exists, a left and right leg height compensation instruction is generated, and the robot is controlled to stretch out and draw back a left leg and a right leg; when the pitch angle deviation exists, a front and rear leg height compensation instruction is generated, and the robot is controlled to stretch out and draw back a front side leg and a rear side leg; and S4, performing partition compensation control through iteration to enable the fuselage to return to the horizontal position again and maintain the balance state.

The invention discloses a double-foot wheel-legged robot pose control method and device based on hierarchical cooperative control, a medium and a product, and relates to the field of robot control, and the method comprises the steps that a hierarchical cooperative control framework is constructed; the layered cooperative control framework comprises a balance controller and a whole body attitude controller; the balance controller determines a reference wheel joint driving torque based on a simplified inverted pendulum dynamic model according to the pose information of the upper mass center of the biped wheel-leg robot; the whole-body attitude controller performs optimization control on all joints of the biped wheel-leg robot by adopting an incremental model prediction control algorithm combined with a reference wheel joint driving moment based on a whole-body dynamic model; state information of the biped wheel-legged robot is obtained; according to the state information, a driving torque instruction of each joint is obtained based on a hierarchical cooperative control framework; and driving the double-foot wheel-leg robot to move according to the driving torque instruction of each joint. The precision, robustness and real-time performance of robot motion control can be improved.

The invention relates to the field of wind power generation, in particular to a performance degradation prediction method based on an independent variable pitch system of a wind turbine, which comprises the following steps of: 1, establishing a performance degradation model by using a Wiener process by taking a pitch angle control error as a core degradation characterization quantity, and analyzing a degradation rule of key components of the variable pitch system in real time; 2, defining a state vector containing a control error and a change speed thereof, introducing deterministic drift and random fluctuation brought by degradation, and constructing a state-space equation; 3, designing a target function, and dynamically adjusting a weight coefficient according to a degradation state; and 4, implementing model prediction control, adjusting a control strategy in real time according to typical working conditions, balancing the control precision and prolonging the service life of parts. By comprehensively analyzing the complex relationship between the external load and the degradation of the variable pitch system, a more accurate performance degradation model is established by using the Wiener process, and the influence of the external load on the degradation law of key components is fully considered.

The invention provides a compensation balance system and method of an electrodynamic transducer, and aims to solve the following problems in the prior art: a traditional electrodynamic sound source transducer needs to realize large-volume displacement in a low-frequency application scene, but the flexibility of an elastic suspension support of the traditional electrodynamic sound source transducer is insufficient to bear hydrostatic pressure; meanwhile, traditional passive and active compensation modes have challenges in the aspects of delay and stability. In order to solve the problems, the compensation balance system and method of the electrodynamic transducer mainly comprise an underwater acoustic transducer, a low-pressure middle cabin and a high-pressure gas cylinder, and a system self-checking function, a transducer piston position balance control function and a transducer fuzzy self-adaptive control function are integrated. The defects in the prior art are effectively overcome, and balance between the inner cavity of the transducer and the external hydrostatic pressure can be rapidly achieved. In addition, the control process is further simplified by determining the target control quantity, and the overall performance of the system is improved.

The invention discloses an air cushion belt dynamic balance control system and a management and control method, and belongs to the field of air cushion belt conveyors. Original signals of a sensor are obtained, a Fluent noise filtering algorithm is used for eliminating airflow interference, synchronous tension and air pressure data are aligned through timestamps, a reliability model is used for filling temperature field missing values, and standardized data streams are formed; based on the standardized data flow, analyzing an air pressure gradient by means of a fluid mechanics equation, extracting abnormal vibration modal characteristics of the carrier roller, calculating a fluctuation coefficient in combination with tension simulation, and obtaining a multi-dimensional parameter set containing dynamic distribution of a pressure field and a belt deviation angle; inputting the multi-dimensional parameter set into the digital twin, updating the fatigue damage parameters of the belt through Fluent simulation driving, rendering the visualization result of the flow field of the air chamber in real time, and outputting a simulation result containing the dynamic cloud picture of the pressure field of the air chamber and the fatigue damage distribution of the belt; and calculating the pressure balance degree of the air chamber according to a simulation result.

The invention discloses an automatic shipment balance control method, which belongs to the technical field of automatic shipment control, and comprises the following steps: scanning a cabin of a berthing empty ship, establishing a cabin three-dimensional model, constructing a wharf coordinate system and a ship coordinate system, and obtaining ship type data; planning a shipping strategy according to the ship type data, setting shipping turns and blanking point coordinates and target material height of each turn, and determining safe operation parameters of the chute tube; according to the coordinates of the blanking point of the current turn and the safe operation parameters of the chute tube, the outlet of the chute tube is controlled to be aligned with the coordinates of the blanking point of the current turn based on the wharf coordinate system by adjusting the motion parameters of the ship loader, and blanking is conducted when it is monitored that the material height of the current turn reaches the target material height; controlling the outlet of the chute tube to transfer to the blanking point coordinate of the next round; refreshing the cabin three-dimensional model in the loading process, obtaining the posture of the ship body, and updating the coordinates of the blanking point; and stopping the shipping operation until the shipping volume reaches the standard. The method can improve the automatic shipment balance control precision.

The embodiments disclose a financial transaction platform comprising a Stability Verification Engine coupled to a Transaction Input and configured to calculate stability functions using a Lyapunov Stability Engine to evaluate whether a proposed transaction converges toward equilibrium within a Stability Valley defined by threshold values in a Stability Function Database, a Deterministic Consensus Protocol coupled to the Stability Verification Engine and configured to process validated transactions through pre-commit verification, mathematical proof generation, and finality attestation to ensure irreversible acceptance, a Cascade Prevention System coupled to the Deterministic Consensus Protocol and configured to apply progressive intervention levels including enhanced verification, fee adjustment, transaction size limits, processing delays, and emergency stabilization when abnormal conditions are detected, and a Transaction Output coupled to the Cascade Prevention System and configured to forward transactions confirmed as stable and finalized to downstream predictive management and interface node systems.

The invention relates to a balance control algorithm for gravity center trajectory planning and real-time tracking of a humanoid robot, in particular to the technical field of robot control, and realizes dynamic balance control through a closed-loop process of state sensing, online gravity center trajectory re-planning, hierarchical control law calculation and joint torque output and execution. A layered dynamic modeling and control framework is constructed, a variable-height inverted pendulum model is combined with model prediction control, and a gravity center and zero moment point track feasible in dynamics is generated online; virtual restoring force is generated through a gravity center impedance controller, a task priority inverse dynamics solver is used, multi-task execution is coordinated on the premise that balance tasks are guaranteed to be absolutely preferential through a null-space projection technology, theoretical torque is converted into current instructions to drive joints to move, and through efficient collaboration and real-time updating of dynamic models of all levels, the balance tasks are optimized. According to the invention, deep collaboration of various technical features is realized, and the problems of poor adaptability, slow response and poor balance capability of a traditional method in a dynamic environment are effectively solved.

The invention relates to the technical field of robot attitude balance control, and provides a robot attitude balance control method and system. Before the feet of the robot make contact with the supporting face or at the initial stage of contact, preset micro-force excitation is actively applied to the supporting face for detection, and mechanical response information and foot displacement information between the feet and the supporting face are collected in real time; based on the information, the system can estimate the local physical characteristics of the supporting surface in real time, and dynamically control and adjust the balance control strategy of the robot according to the estimated local physical characteristics. Through active detection and foresight adjustment, the characteristic change of the supporting surface can be identified and adapted before or at the initial stage of instability, so that the control conflict and vicious circle possibly brought by passive compensation are avoided.

The invention discloses a self-balancing control method applied to a heteroaxial two-wheeled vehicle, the vehicle carries a scissor-type double CMG module, the module comprises two CMG units, and the method comprises hardware decoupling: using the scissor-type double CMG to counteract course disturbance torque generated when the CMG works on a physical level; role separation: redefining a CMG system and a steering system; feedforward coordination: during turning, not depending on the CMG always generating a restoring moment to maintain a 0-degree inclination angle, calculating a steady-state inclination angle (phi lean) required at the speed and the steering angle through a dynamic model feedforward; and track planning: by coordinating track planning, it is ensured that the inclination action of the CMG and the steering action of the steering are perfectly synchronized in time, and balance is dynamically maintained. The problem of CMG saturation is solved, high-precision decoupling control is achieved, turning is smooth and safe, full-speed-domain coverage is achieved, and efficiency and energy saving are achieved.

The invention relates to a multi-objective optimization control method for an intelligent networked electric vehicle based on data driving and an application thereof. The method comprises the following steps: extracting a vehicle model for a single intelligent networked electric vehicle and obtaining a state representation of the vehicle model in a vehicle queue; constructing a data-driven prediction model based on a subspace identification method and historical data, and constructing a linear input and output mapping relation; constructing a multi-objective optimization function, and executing optimization control based on a multi-objective optimization result; the method is applied to a controller of an intelligent network-connected electric vehicle in a vehicle queue, the controller collects acceleration and braking operation data to track torque and outputs control quantity for executing optimization control, and queue control is achieved. According to the method, the problem of control performance degradation caused by modeling errors or system complexity is effectively solved; a multi-objective optimization problem is introduced, so that the practicability and robustness of the system under a complex working condition are enhanced; the control performance is balanced while the prediction precision is kept, and the operation safety, the energy saving performance and the cooperation efficiency of the intelligent network connection electric vehicle queue are effectively improved.

The invention relates to an electromagnetic bearing rotor system unbalance control method based on a DDPG algorithm, and the method comprises the following steps: building a rotor dynamics equation according to rotor dynamics, and carrying out the decoupling control; constructing a compensation current, extracting an unbalance coefficient in the compensation current, and constructing a self-adaptive identification equation; constructing a DDPG intelligent agent, fusing position errors, system response and control output, and designing a multi-target reward function through a normalized weighted optimization strategy; and a DDPG intelligent agent is used to optimize an unbalance coefficient identification parameter, and a compensation current is dynamically adjusted in combination with the rotating speed of the rotor, so that unbalance force disturbance is counteracted. According to the invention, identification is carried out by combining the deep reinforcement learning algorithm DDPG according to the unbalance coefficient reflected to the two ends of the rotor by the unbalance force borne by the rotor, the identification and unbalance compensation efficiency of the unbalance coefficient is improved, and rapid, stable and efficient compensation of a rotor system is realized.

The invention provides a multi-source cooperative operation and dynamic adjustment method for a power distribution network, and relates to the technical field of power system operation and control, and the method comprises the steps: building a multi-level physical characteristic model based on the collected network topology parameters of the power distribution network and the physical operation parameters of all distributed energy devices; on the basis of the multi-level physical characteristic model, a master-slave collaborative optimization control architecture is utilized to obtain collaborative balance control conditions; based on the master-slave collaborative optimization control architecture, the collaborative balance control condition and the initialized energy management interaction signal, distributed iterative calculation is carried out to obtain an energy management interaction signal and a power control strategy; and based on the real-time operation state data and the power control strategy of the power distribution network at the current moment, performing rolling optimization calculation in a limited time domain by using a model prediction control method to obtain a real-time control instruction for driving each distributed energy device to act. According to the invention, the problem that multi-body operation and dynamic optimization in the power distribution network are difficult to coordinate in the prior art is solved.

The present application relates to a kind of narrow body vehicle balance control method based on control moment gyro, belong to narrow body vehicle active roll control system field.The dynamics equation of control moment gyro is established, the active roll dynamics model of narrow body vehicle is calculated to obtain the roll angle error and roll angular velocity error of vehicle, then the active roll controller is designed using sliding mode control method to control the roll angle error and roll angular velocity error, and the rotational angular velocity of control moment gyro frame is obtained;Finally, the control moment gyro on narrow body vehicle is adjusted in real time, and vehicle balance is realized.The vehicle roll scheme in the present application uses control moment gyro device, with high control precision, fast response speed, and independent of suspension system, can realize greater balance torque.By controlling the frame angular velocity of control moment gyro, the dynamic balance of narrow body vehicle when steering can be realized, to assist the safe steering of driver.

The invention relates to the field of automatic balance weight forklifts, in particular to an automatic balance weight forklift control method and system based on multi-source data, and the method comprises the steps that the multi-source data of a sensor are obtained; compensating the weighing value of the left pallet fork and the weighing value of the right pallet fork according to the real-time attitude angle of the forklift, and generating an equivalent left weighing value and an equivalent right weighing value under the horizontal reference system; calculating a static gravity center offset component and a dynamic inertia offset component; performing addition or weighted summation on the static gravity center offset component and the dynamic inertia offset component to obtain a dynamic gravity center offset; inputting the real-time center-of-gravity offset into a balance controller, and judging whether the absolute value of the center-of-gravity offset reaches or exceeds a preset stable safety threshold value or not by the balance controller; and if so, triggering a balance control strategy. Through hierarchical feature construction and physical constraint fusion of multi-source data, high-precision and low-delay dynamic gravity center estimation is realized, so that the effect of dynamically adjusting a forklift control strategy is achieved.

The invention discloses an intelligent landing leg automatic balance control system and control method, and belongs to the technical field of landing leg control. The method is used for solving or improving the technical problems that the anti-shake capability is insufficient under the dynamic load and the sensor error affects the balance precision in the existing scheme. Through the technical advantage of independent operation of each module, the high-precision data of the sensing module, the intelligent strategy of the decision control module, the precise action of the execution module, the risk prevention and control of the security module, and the deep cooperation of the data-strategy-action closed loop, control-protection double tracks and scene adaptation optimization among the modules, the safety of the system is improved. The full-process efficient linkage of data acquisition, dynamic control and safety guarantee is realized, and the dynamic load adaptive capacity, the balance control precision and the system reliability are effectively improved.

The present invention discloses a control method and control system for cooling balance in a pipe network, which adopts a method to meet the needs of the first unfavorable cooling user. First, the normal return water temperature range value of the user is preset; then, the return water temperature value and valve opening of each user are monitored; then, the first unfavorable cooling user is obtained, and when the user is monitored to have a maximum valve opening and a return water temperature value not within the preset normal return water temperature range value, it is determined to be the first unfavorable cooling user; the working frequency of the cooling water pump can be adjusted, and the cooling water pump can adjust the water supply again, or only the valve opening can be adjusted to adjust the water supply amount for different users; finally, it is ensured that the return water temperature value of each user is within the preset normal return water temperature value, and the pipe network enters a cooling balance state; this solves the problem in the prior art that a centralized cooling system generally uses a balancing valve for cooling balance control, resulting in poor cooling balance control effect caused by control disorder.

The invention discloses an unmanned bicycle balance control method based on uncertain reasoning learning. According to the method, a lateral inclination angle (inclination angle speed), a handlebar rotation angle (rotation angle speed) and handlebar control torque of an unmanned bicycle body are used as input, increments of moments before and after the lateral inclination angle (inclination angle speed) and the handlebar rotation angle (rotation angle speed) are used as output, and a probability dynamic model (PDM) of a system is constructed by utilizing a GPR (Gaussian process regression) method; performing parameterization processing on the control strategy, and performing subsequent state sequence prediction by using the established PDM; a cost function is constructed through a vehicle body lateral inclination angle (inclination angle speed) and a handlebar rotation angle (rotation angle speed), and finally a control strategy of the system is optimized in combination with a gradient search method. According to the method, a prior dynamic model of the unmanned bicycle does not need to be established through a complicated physical principle, a probability dynamic model is learned from interactive empirical data, the uncertainty of the unmanned bicycle during operation is fully considered, the influence of model deviation on a control strategy is reduced, the robustness and generalization ability of an unmanned bicycle system are improved, and the method is suitable for popularization and application. And the method has very strong engineering significance.

The invention relates to the technical field of hoisting equipment control, in particular to a double-beam crane control device for a lifting gas engine unit of a power plant, which comprises the following modules: a load stabilizing module for monitoring the tension difference and the swing amplitude of a hoisting cable in real time and starting an automatic adjusting function when the tension difference and the swing amplitude exceed a set threshold value; the path planning module is used for generating a hoisting path according to the weight and the size of the gas engine unit and adjusting the path when an obstacle or working condition change is detected; the dynamic balance module is used for monitoring the vertical offset and the vibration condition of the gas engine unit, and the alarm prompting module is used for sending out an acousto-optic warning signal when an abnormal condition occurs in the hoisting process; according to the method, intelligent feedback control, self-adaptive path planning and high-precision positioning technologies are introduced, so that the defects in the aspects of load stability, dynamic balance control and complex working condition adaptability in the prior art are overcome, and the safety and high efficiency of the gas engine unit in the hoisting process are ensured.

The invention discloses a building comprehensive management control method and device and a medium, and belongs to the technical field of building automation control, and the method comprises the steps: building a dual-medium coupling model between an air medium and a building structure medium, and carrying out the parameterization; establishing a heat flux balance equation based on a parameterization result; building operation data and environment data are collected and processed; the air medium enthalpy difference change rate and the building structure body temperature change rate are calculated and substituted into the heat flux balance equation, the real-time heat flux difference is obtained, and a coupling state vector is generated; and inputting the coupling state vector into a dynamic balance optimization control equation to execute iterative solution, obtaining a control variable, and generating a control signal to adjust a building operation state. According to the method, an energy transfer rule is accurately described through double-medium coupling modeling, a heat flux balance equation is established to realize dynamic balance control, control variables are divided into air side and building structure body side adjustment components to realize multi-system collaborative optimization, and the control precision and the energy-saving effect are improved.

The invention discloses a current control parameter optimization method and device based on a whale optimization algorithm. The method comprises the following steps: firstly, detecting an unbalanced signal in a current system, wherein the unbalanced signal comprises unbalanced magnetic potential and / or unbalanced voltage; secondly, inputting the unbalanced signal into a whale optimization PID (Proportion Integration Differentiation) controller; (inputting the unbalanced magnetic potential into the whale optimization PID controller); then, the whale optimization PID controller dynamically adjusts a proportionality coefficient, an integral coefficient and a differential coefficient of the PID controller through a whale optimization algorithm based on the imbalance signal so as to optimize control parameters; and finally, according to the optimized control parameters, adjusting the output current of the current source to realize balance control of the current system. Compared with a traditional control method, the method has the advantages that the response speed is higher, the fluctuation range is smaller, operation is convenient, the evaluation process is quantized through a fitness function, and the fitness value of whale optimization PID controller parameters updated every time is smaller.

The invention discloses a Nash equilibrium control method for a data-driven unmanned system under incomplete information. The method comprises the following steps: constructing a distributed dynamic state feedback controller for an unmanned system, and converting a Nash equilibrium solving problem into an output adjustment problem; in the off-line stage, a continuously excited control input sequence is applied to the unmanned system, a corresponding state sequence, a state derivative sequence, a measurement noise sequence and an external signal sequence are collected, and unmanned system data matrixes are constructed respectively; based on the unmanned system data matrix, a Nash equilibrium search problem is converted into a semi-definite programming problem and solved, and a dynamic feedback control gain matrix is obtained; and in the online stage, the controller of each agent dynamically adjusts a decision based on a control signal generated by the dynamic feedback control gain matrix, so that the unmanned system is converged to a Nash equilibrium point. According to the method, an offline and online scheme of data driving and no precise modeling is adopted, robust anti-interference and distributed equilibrium convergence are achieved, and the method is suitable for a complex unmanned collaborative scene.