11 results about "Probable Control" patented technology

The invention provides a range extender control method and device, and the method comprises the steps: obtaining a target user intention and vehicle state data, verifying the feasibility of the target user intention based on the vehicle state data, determining a range extender control strategy according to the vehicle state data and the target user intention if the target user intention is feasible, and carrying out the control of a range extender. In this way, a user can be supported to actively participate in control of the range extender, endurance anxiety caused by passive waiting for regulation and control due to the fact that the control logic of the range extender is not clear is avoided, due to the tendency description that the user actively carries out control of the range extender, a certain expectation can be achieved for a possible control strategy, and the user experience is improved. And the discomfort of a user caused by sudden starting or power attenuation of the range extender is reduced.

An apparatus and method for controlling hardware agents in a control situation having multiple hardware agents. According to various embodiments, a method is described, comprising: determining a potential function via a first neural network; determining a control scenario for the control situation from a plurality of possible control scenarios via a second neural network; determining a common action sequence of the plurality of hardware agents by searching for the optimal value of the potential function determined with respect to possible common action sequences of the determined control scenario; and controlling at least one of the plurality of hardware agents according to the determined common action sequence.

This disclosure describes systems, methods, and apparatus for estimation law modules of an adaptive engine. The estimation law modules estimate estimated model parameter tensors for each control sample within a frame. These estimated model parameter tensors are passed to control law modules and used to determine possible control signals as well as being passed to an estimation portion of a nonlinear model of the system, along with the possible control signals, to estimate estimated system outputs corresponding to each of the possible control signals. A selector module can then select a control as one of the possible control signals or a combination of two or more of the possible control signals, based on comparing the corresponding estimated system outputs to a reference signal, and / or measured system outputs.

An adaptive engine and a method of adaptive control are disclosed. The method comprises receiving an input regressor, where the input regressor comprises a reference signal, a system output measurement, and a control output. The method includes applying one or more estimation laws to the input regressor to estimate two or more sets of estimated model parameter tensors, θ; receiving two or more possible control signals; generating two or more estimated system outputs; and selecting a control signal from a set comprising at least the first possible control signal and the second possible control signal, or a combination of possible control signals blended from two or more of the sets.

This disclosure describes systems, methods, and apparatus for a selector and combiner of an adaptive engine. The adaptive engine can combinations of estimation and control laws to produce a multitude of possible control signals based on inputs such as a reference signal. A nonlinear model of the system can generate estimated system outputs for each of the possible controls signals. The selector and combiner can use the estimated system outputs to determine a best of the possible control signals, or a best combination of the possible control signals, such that the control approaches a desired measured output of the system.

An adaptive engine and adaptive control method. The method comprises receiving an input regressor and applying one or more estimation laws to the input regressor to estimate a plurality of estimated model parameter tensors for a nonlinear model. The method also includes computing an estimation error or a cost function using a system output measurement and an estimated system output for the previous iteration and determining a model order based at least in part on the estimation error or the cost function and receiving two or more possible control signals each using a corresponding control portion of the nonlinear model. In addition, the method includes generating two or more estimated system outputs each using a corresponding estimation portion of the nonlinear model and selecting a preferred control signal from a set comprising at least the two or more possible control signals or a preferred combination of possible control signals.

The invention discloses an equipment control method and device and an interaction system, and relates to the technical field of the Internet of Things, and the method comprises the steps: obtaining interaction information inputted by a user for electronic equipment; determining intention information corresponding to the interaction information; the intention information is sent to the control device, the control device is indicated to determine a target device and a control instruction based on the intention information, the control instruction is sent to the target device to control the target device to execute a preset operation, and the target device belongs to at least one intelligent device. Therefore, the control device acts as an intermediary for communication with different devices, shielding the complexity of different device protocols. Through the mode, the target equipment does not need to provide adaptation for each possible control equipment, and only needs to ensure the compatibility with the control equipment, so that the adaptation workload of each equipment is reduced.

The invention discloses a high-proportion new energy power system inertia response determination method and system based on frequency lowest point constraint and a storage medium. Firstly, the influence of inertia response and primary frequency modulation response on system frequency is deduced, and substitution modes of related control parameters of the inertia response and the primary frequency modulation response are obtained; then, in combination with a typical unit frequency domain control block diagram, establishing a related model in simulink; and finally, traversing possible control parameters in the system to obtain an alternative relationship between inertia response and primary frequency modulation response under the constraint of the lowest point of the frequency, and further determining the inertia response capability required by the system for maintaining safe and stable operation of the frequency.

The application provides a range extender control method and device. The method obtains target user intention and vehicle state data, verifies the feasibility of the target user intention based on the vehicle state data, determines a range extender control strategy according to the vehicle state data and the target user intention if the target user intention is feasible, and controls the range extender. In this way, the user can actively participate in the control of the range extender, thereby avoiding the range anxiety caused by passive waiting for regulation and control due to the unknown control logic of the range extender. Since the user actively performs the tendency description of the range extender control, there is a certain expectation for the possible control strategy, thereby reducing the discomfort of the user caused by the sudden start or power attenuation of the range extender.

The invention provides an energy storage converter control system optimization method based on hierarchical learning, and the method comprises the steps: firstly, randomly generating a group of particles, and enabling each particle to represent a group of possible controller parameters; thirdly, the fitness of each particle is evaluated according to performance indexes of the system, and the particles are divided into a plurality of layers according to fitness values; different learning strategies are applied to the particles of different levels, the particles of the low level pay more attention to exploration of a new parameter space, and the particles of the high level pay more attention to development in a known excellent parameter area. Two particles are randomly selected from a higher level as samples, and learning of other particles is guided, so that the positions of the particles are updated. The process is repeated until the number of iterations is satisfied or a satisfactory solution is found. According to the method, the LPSO algorithm is adopted, the global search capability and the local development capability of the algorithm are remarkably improved through a hierarchical learning strategy, and a solution with higher quality can be found in a complex optimization problem.

The invention provides an in-situ alkalization and convergence enhancement system for ocean negative emission and a control method of the in-situ alkalization and convergence enhancement system. In the system, a central control unit predicts a convergence increasing effect under different operation strategies according to a preset prediction model based on monitoring data to obtain a prediction result, and generates a control instruction according to the prediction result and enclosure volume variation; and a control instruction is sent to the intelligent dosing system and the controllable water body exchange mechanism so as to realize self-adaptive regulation of the medicament dosing acceleration and the water body exchange rate. By means of the technical scheme, the system can select the optimal path capable of maximizing the convergence increasing effect and minimizing the cost or ecological influence from numerous possible control paths through model prediction, and intelligence and foresight of a control strategy are achieved; tidal dynamic balance is achieved on the physical environment adaptation level, the water exchange and enclosure volume is automatically adjusted in the fluctuation and ebb tide process, and the environment applicability is improved.