Adaptive neural network tracking control method based on dynamic gain
A neural network and dynamic gain technology, applied in adaptive control, general control system, control/adjustment system, etc., can solve the problems of inflexible controller design and high energy consumption of the controller, so as to achieve flexible design and reduce energy consumption Effect
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specific Embodiment approach 1
[0019] Embodiment 1: The specific process of an adaptive neural network tracking control method based on dynamic gain in this embodiment is as follows:
[0020] Step 1. According to the state variable x of the actual nonlinear control system (such as robotic arm trajectory tracking control, spacecraft attitude tracking control, motor servo control, etc.) 1 and x 2 , output signal y and control signal u, establish a two-dimensional state space model of an uncertain nonlinear strict feedback system with unknown nonlinear control direction function, so that the system output y(t) can track the given system target within a small error range signal y d (t);
[0021] Step 2. Define the extended state variable x 3 =u, establish a three-dimensional nonlinear system state space model with extended state variables, and define the error variable z 1 ,z 2 ,z 3 ;
[0022] Step 3. Use the error variable z in Step 2 1 ,z 2 ,z 3 Design the Lyapunov function V;
[0023] Step 4. Use ...
specific Embodiment approach 2
[0026] Embodiment 2: The difference between this embodiment and Embodiment 1 is that in step 1, the state variables of the actual nonlinear control system (such as robot arm trajectory tracking control, spacecraft attitude tracking control, motor servo control, etc.) x 1 and x 2 , output signal y and control signal u, establish a two-dimensional state space model of an uncertain nonlinear strict feedback system with unknown nonlinear control direction function, so that the system output y(t) can track the given system target within a small error range signal y d (t); the specific process is:
[0027] The two-dimensional state-space model of an uncertain nonlinear strict feedback system with unknown nonlinear control direction function is established as:
[0028]
[0029] where x 1 (t) and x 2 (t) represents the state variable of system (1), f 1 (x 1 (t), t) and f 2 (x 1 (t), x 2 (t), t) is the unknown nonlinear function of the system, ψ 1 (x 1 (t)) and ψ 2 (x ...
specific Embodiment approach 3
[0034] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that the state variable x 1 and x 2 When bounded, f 1 , f 2 , ψ 1 , ψ 2 is bounded, f 1 , f 2 , ψ 1 , ψ 2 The first derivative of each with respect to time is bounded, and ψ 1 ≠0, ψ 2 ≠0;
[0035] the f 1 , f 2 , ψ 1 , ψ 2 respectively represent f 1 (x 1 (t), t), f 2 (x 1 (t), x 2 (t), t), ψ 1 (x 1 (t)) and ψ 2 (x 1 (t), x 2 (t)).
[0036] Other steps and parameters are the same as in the first or second embodiment.
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