PID type iterative learning control method based on neural network
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A technology of iterative learning control and neural network, applied in the field of PID type iterative learning control based on neural network
Active Publication Date: 2019-10-25
HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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[0004] However, there are not many research results on iterative learning problems...
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Embodiment 1
[0079] Consider the following system:
[0080]
[0081] Obviously, this is a non-affine nonlinear system. The expected output trajectory in the iterative learning task is:
[0082] the y d (t)=0.5×(-1) round(t / 10) t∈I[1,100]
[0083] We adopt the iterative learning scheme formula (17) proposed in the present invention to control the system, and now we pre-set the structure of the neural network and the initial values of the parameters. The three neural networks used to estimate the gain of the PID controller all use a hidden layer with three nodes, and the inputs of the three neural networks are:
[0084]
[0085] For all i∈I[1,+∞), t∈I[1,T], we set the radial basis function center of the neural network to be [0 0 0] T , with a width of 100; for all t∈I[1,T], the input of the first iterative neural network is set to z S,1 (t)=[0 0 0] T . The weight of the first iteration is w P,1 (t) = [0.2 0 0] T ,w I,1 (t)=[0 0 0] T ,w D,1 (t) = [0 00] T ; For all t∈I[...
Embodiment 2
[0088] Such as figure 2 is a pick-and-place robot model (refer to non-patent literature 2: Liu N. Learning identification and control for repetitive linear time-varying systems [D]. University of Illinois at Urbana-Champaign, 2014.), for the rotation angle θ of the manipulator, we get the state variable x 1 = θ and And taking the angular velocity as the system output, the following second-order linear mathematical model can be obtained:
[0089]
[0090] Where β=2Nm / rad is the viscous friction coefficient, K t =100 is the gain of the actuator, M(t) is the mass at the bottom of the time-varying manipulator, and L=0.1m is the length of the manipulator. During the pick-and-place process of the robotic arm, the heavy object is grasped in the 5th second and put down in the 10th second. The mass of M(t) also changes from 1kg to 10kg and then back to 1kg.
[0091] Take the sampling time T s =0.01s, the discretization model (19) in the time interval [1,10] can obtain the fo...
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Abstract
The invention proposes a PID-based iterative learning control method based on a neural network, belonging to the field of control science and engineering. For a general nonlinear difference system, the invention gives an iterative learning PID controller, and then dynamic parameters in the PID controller are fitted by using three RBF neural networks. The weights of the neural networks are estimated according to a gradient descent method and a neural network weight update expression is obtained. Since an exact model of a controlled system is complex or unknown, a real value of a system pseudo partial derivative (PPD) in a neural network weight estimation expression cannot be obtained, and an estimated value is used for numerical value substitution. In this way, a nonlinear iterative learning control algorithm that can be run is generated. Finally, an example is given to demonstrate that the numerical simulation of the algorithm in a nonlinear system and a picking and placing robot system is effective. Therefore, this algorithm can be applied to a general (unknown) nonlinear differential system.
Description
technical field [0001] The invention relates to the field of control science and engineering, in particular to a neural network-based PID iterative learning control method. Background technique [0002] Iterative learning is a control method in which the controlled system continuously absorbs previous experience for repeated learning in a limited time interval. It can be understood as: when the controlled system performs the i-th operation, the controller uses the input (output) data and errors of the (i-1) and previous times to make the controlled system Get a better execution effect. According to this method, the controller is constantly corrected, so that the controlled system moves towards the desired trajectory. [0003] In the past three decades, iterative learning control has achieved rich results both in theory and in application. However, these efforts mainly focus on solving iterative learning control problems for linear or affine nonlinear systems of known mode...
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IPC IPC(8): G05B11/42
CPCG05B11/42
Inventor 常明方吴爱国
Owner HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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