44results about How to "Overcome time-consuming and labor-intensive tuning problems" patented technology

The invention discloses a parameter self-tuning method based on system error for an MIMO partial format model-free controller. A system error set is used as the input of a BP neutral network. The BP neutral network performs forward calculation, and the to-be-tuned parameters of an MIMO partial format model-free controller, such as penalty factor and step length factor, are output through an outputlayer. A control input vector of a controlled object is calculated by using a control algorithm of the MIMO partial format model-free controller. With the minimum value of a system error function asthe objective, system error back propagation calculation is carried out on the gradient information set of each to-be-tuned parameter through a gradient descent method according to the control input.The hidden layer weight coefficient and the output layer weight coefficient of the BP neutral network are updated online and in real time. Therefore, self-tuning of the parameters of the controller based on system error is realized. The parameter self-tuning method based on system error for an MIMO partial format model-free controller presented by the invention can overcome the difficulty of online controller parameter tuning, and has a good control effect on an MIMO system.

The invention discloses a parameter self-tuning MISO (Multiple Input and Single Output) different-factor tight-format model-free control method. In view of the limitation of the existing MISO tight-format model-free control method adopting the same factor structure, that is, the limitation of only adopting penalty factors with the same value and step-size factors with the same value for differentcontrol inputs in a control input vector at a k moment, an MISO tight-format model-free control method by adopting a different factor structure is provided, penalty factors with different values and/or step-size factors with different values can be adopted for different control inputs in the control input vector at the k moment, and the control problem of different characteristics of control channels existing in a complex object such as a strong nonlinear MISO system can be solved. A parameter self-tuning method is also provided to effectively overcome the problem that penalty factors and step-size factors need to consume time and labor for tuning. Compared with the existing control method, the method of the invention is higher in control accuracy, better in stability and wider in applicability.

The invention discloses a system error-based parameter self-setting method of an MIMO full-format model-free controller. According to the invention, a system error set is used as input of a BP neuralnetwork, and the BP neural network performs forward calculation and outputs a penalty factor, a step factor and other MIMO full-format no-model controller to-be-set parameters through an output layer.The calculation is performed by adopting a control algorithm of the MIMO full-format model-free controller, and then a control input vector for a controlled object is obtained through calculation. The value of a system error function is minimized as a target, and the gradient descent method is adopted. The gradient information sets of all to-be-set parameters are respectively set according to thecontrol input and the reverse propagation calculation of the system error is performed. The hidden-layer weight coefficient of the BP neural network is updated on line in real time, and then the hidden-layer weight coefficient is outputted for realizing the parameter self-setting of the controller based on the system error. According to the invention, the self-setting method of the MIMO full-format model-free controller is proposed based on the system error. The problem of the parameter online setting of the controller can be effectively solved. The good control effect of the MIMO system is achieved.

The invention discloses a parameter self-tuning method of an SISO full-format model free controller based on partial derivative information. The partial derivative information is used as an input of aBP neural network, the BP neural network performs forward calculation and outputs a penalty factor, a step length factor and other parameters to be set of a controller through an output layer, a control algorithm of the controller is used to calculate and obtain control input for a controlled object, the gradient information of the control input for each parameter to be set is calculated, with aminimum value of a system error function as a target, a gradient descent method is used, combined with gradient information, the system error backpropagation calculation is carried out, a hidden layerweight coefficient and an output layer weight coefficient of the BP neural network are updated in real time in an online mode, and the gradient information is stored as the partial derivative information to be an input of the BP neural network of a next time. The invention provides the parameter self-tuning method of an SISO full-format model free controller based on partial derivative information, the tuning problem of the controller parameters can be effectively overcome, and a good control effect is achieved.

The invention discloses a parameter self-tuning method based on partial derivative information for an MIMO partial format model-free controller. A partial derivative information set is used as the input of a BP neutral network. The BP neutral network performs forward calculation, and the to-be-tuned parameters of an MIMO partial format model-free controller, such as penalty factor and step lengthfactor, are output through an output layer. A control input vector of a controlled object is calculated by using a control algorithm of the MIMO partial format model-free controller. With the minimumvalue of a system error function as the objective, system error back propagation calculation is carried out on the gradient information set of each to-be-tuned parameter through a gradient descent method according to the control input. The hidden layer weight coefficient and the output layer weight coefficient of the BP neutral network are updated online and in real time. Therefore, self-tuning ofthe parameters of the controller based on partial derivative information is realized. The parameter self-tuning method based on partial derivative information for an MIMO partial format model-free controller presented by the invention can overcome the difficulty of online controller parameter tuning, and has a good control effect on an MIMO system.

The invention discloses a partial deviation information based parameter self-setting method of an SISO tight-format model-free controller. Partial deviation information serves as input of a BP neuralnetwork, the BP neural network carries out forward calculation and outputs to-be-set parameters including an output-layer output punishing factor and a step factor, a control algorithm of the controller is used to calculate a control input vector aimed at a controlled object, reverse spreading of system errors is calculated by taking minimizing a value of a system error function as the target, employing a gradient decrease method and controlling input aimed at gradient information of parameters to be set, a hidden-layer weight coefficient and an output-layer weight coefficient of the BP neuralnetwork are updated online in real time,. and the gradient information is stored as partial deviation information and serves as input of the BP neural network in next time. The method can be used toovercome difficulty in parameter setting of the controller, and has a good control effect.

The invention discloses a parameter self-tuning method of an MISO (Multiple Input and Single Output) partial format model-free controller based on partial derivative information, which comprises the steps that a partial derivative information set is used to serve as input of a BP neural network, the BP neural network performs forward calculation and outputs parameters to be tuned such as a penaltyfactor and a step factor of the MISO partial format model-free controller through an output layer, a control input vector in allusion to a controlled object is calculated by adopting a control algorithm of the MISO partial format model-free controller, the minimization of the value of a system error function is taken as an objective, a gradient descent method is adopted to perform system error back propagation calculation respectively in allusion to a gradient information set of each parameter to be tuned by combining the control input, a hidden layer weight coefficient and an output layer weight coefficient of the BP neural network are updated in real time online, and parameter self-tuning of the controller based on the partial derivative information is realized. The parameter self-tuning method provided by the invention of the MISO partial format model-free controller based on the partial derivative information can effectively overcome a problem of online tuning for controller parameters and has a good control effect for the MISO system.

The invention discloses an MIMO different-factor partial-format model-free control method with self-tuning parameters, and aims at the limitation of a prior MIMO partial-format model-free control method adopting a same-factor structure, namely, aiming at the limitation that different control inputs in a control input vector at a k moment can only adopt penalty factors with the same numerical valueand step factors with the same numerical value, an MIMO partial-format model-free control method adopting a different-factor structure is provided, different control inputs in a control input vectorat the k moment can adopt penalty factors with different numerical values and step factors with different numerical values, the control problem of different characteristics of various control channelsin complex objects such as a strong nonlinear MIMO system can be solved. Meanwhile, a method of parameter self-tuning is provided to solve the problem that tuning of the penalty factors and step factors is time consuming and labor consuming. Compared with a prior control method, the method has higher control precision, better stability and wider applicability.

The invention discloses a parameter self-tuning method of a MISO partial format model-free controller based on system errors. The method includes: a system error set is adopted as input of a BP neuralnetwork, the BP neural network performs forward calculation and outputs to-be-tuned parameters of the MISO partial format model-free controller, such as a penalty factor and a step factor, through anoutput layer, a control input vector of a controlled object is obtained through calculation by employing a control algorithm of the MISO partial format model-free controller, the minimization of a value of a system error function is regarded as the target, reverse propagation calculation of the system errors is performed aiming at a gradient information set of each to-be-tuned parameter with thecombination of control input by employing a gradient descent method, weight coefficients of a hidden layer and the output layer of the BP neural network are updated in real time in an online manner, and parameter self-tuning of the controller based on the system errors is realized. According to the parameter self-tuning method of the MISO partial format model-free controller based on the system errors, the online tuning problem of the parameters of the controller can be effectively overcome, and a good control effect of the MISO system is achieved.

The invention discloses a MISO hetero-factor full-format model-free control method with parameter self-tuning. The MISO hetero-factor full-format model-free control method with parameter self-tuning aims at the limitation of an existing MISO full-format model-free control method with a same factor structure, namely, the limitation comprises that penalty factors with a same numerical value and stepsize factors with a same numerical value can only be used for different control inputs in the control input vector at k time, the MISO full-format model-free control method based on a hetero-factor structure is proposed. At the k time, penalty factors with different numerical values and/or step size factors with different numerical values can be used for different control inputs in a control input vector, the control difficult problems of different control channel characteristics in complex objects such as strongly nonlinear MISO systems can be solved, and meanwhile, the method of parameter self-tuning is proposed to effectively solve the difficult problem of time-consuming and laborious tuning of the penalty factors and the step size factors. Compared with an existing control method, theMISO hetero-factor full-format model-free control method with parameter self-tuning has higher control accuracy, better stability and wider applicability.