Patent ID: 11958365
Assignee: JIANGXI UNIVERSITY OF SCIENCE AND TECHNOLOGY
Field: Transport (Mechanical engineering)
Classification: CPC B  G  Y | IPC B  G

Claim 6:
7. The method for dual-motor control on an electric vehicle based on adaptive dynamic programming according to claim 6, wherein an equation for updating the weight of the evaluation network in S325 can be expressed as follows:

Wc(t+1)=Wc(t)+ΔWc(t)  (6), wherein

Wc(t) denotes a weight matrix of the evaluation network at moment t, and ΔWc(t) denotes a weight change value of the evaluation network from moment t to moment t+1; and
equations for updating the weight of the execution network can be expressed as follows:, Δ
       ⁢
       
        
         W
         a
        
        (
        t
        )
       
      
      =
      
       
        -
        η
       
       ·
       
        
         ∂
         
          J
          ⁡
          (
          t
          )
         
        
        
         ∂
         
          u
          ⁡
          (
          t
          )
         
        
       
       ·
       
        
         ∂
         
          u
          ⁡
          (
          t
          )
         
        
        
         ∂
         
          
           W
           a
          
          (
          t
          )
         
        
       
      
     
     ,
     and
    
   
   
    
     (
     7
     )
    
   
  
 

 
  
   
    
     
      
       
        W
        a
       
       (
       
        t
        +
        1
       
       )
      
      =
      
       
        
         W
         a
        
        (
        t
        )
       
       +
       
        Δ
        ⁢
        
         
          W
          a
         
         (
         t
         )
        
       
      
     
     ,
    
   
   
    
     (
     8
     )
    
   
  
 

 wherein
Wa denotes a weight matrix of the execution network, ΔWa(t) denotes a weight change value of the execution network from moment t to moment t+1, J(t) denotes a cost function at moment t, u(t) denotes output of the execution network at moment t, and η(η>0) denotes a learning rate.