Improved Tracking Differentiator and Flexible Joint Parameter Identification Algorithm

What is it about?

The assembly error of flexible joints and the change of joint stiffness during movement make the actual value of joint parameters inconsistent with the given value, which affects the joint control accuracy. In order to suppress the influence of parameters error, a parameters identification method for flexible joint combined offline identification and online compensation is proposed. Firstly, the offline identification model of inertia, mass and damping and the online identification model of joint stiffness are established respectively. Then, a hybrid tracking differentiator based on improved Sigmoid function is designed to track the differential signals of joint motion parameters, and Lyapunov function is designed to prove its convergence. The adaptive differential evolution is used as the identification algorithm, and the improved adaptive crossover, mutation factor and Metropolis acceptance criterion are designed to improve the convergence speed. Finally, a feedforward control structure based on identification is designed to compensate the model deviation. Simulation and experimental shows that the improved differentiator can effectively improve the tracking speed and derivation accuracy of the signals. Compared with other algorithms, the proposed identification method has faster convergence speed and higher identification accuracy, and feedforward compensation control can effectively correct model parameters and improve control accuracy.

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Photo by Sufyan on Unsplash

Photo by Sufyan on Unsplash

Why is it important?

The main innovations and conclusions of this paper include the following points: (1) A flexible joint parameter identification method combining offline identification and online compensation is proposed. When off-line, the optimal trajectory is used as the identification data to identify relatively fixed parameters such as inertia and friction coefficient and to correct the initial model; when online, the actual output is used as the identification data to identify the stiffness parameters and to compensate for the model errors generated during the variable speed motion in real-time. The advantage of this design is that it can reduce the calculation of online identification, and make the controller have the ability of real-time identification and real-time compensation. (2) The improved Sigmoid function has the ability to adjust the concave and convex properties of the function near the origin, and does not reduce the convergence speed away from the origin, which helps to improve the convergence stability while maintaining the tracking speed. Compared with other existing tracking differentiators, ISTD can achieve the purpose of changing the convergence trend of the system state only by adjusting the exponential parameter c, which enables it to better balance the contradiction between the high convergence speed and the high convergence stability of the differential, and does not require redesigning the tracking function. (3) The improved differential evolution algorithm adopts improved adaptive mutation and crossover factors, and introduces Metropolis acceptance criterion in the selection process. Compared with the traditional differential evolution algorithm, the new adaptive mutation and crossover factors can improve the convergence speed while ensuring the global search ability, while Metropolis acceptance criterion improves the ability of the algorithm to jump out of local optimum, which is beneficial to improve the convergence accuracy.