Abstract
To enhance trajectory tracking performance for affine nonlinear systems with parametric uncertainties and improve parameter convergence under interval excitation, this paper proposes a multilateral cooperative adaptive learning mechanism. The initial parameter values are assigned based on available data distribution or predefined bounds when unknown. A composite learning adaptive controller estimates system uncertainties using multilateral learning outputs. Adaptive update laws for unknown parameters and multilateral weights are designed using parameter estimation errors and approximation errors, with a saturation function constraining weight variation rates to suppress oscillations. Experimental results on an inverted pendulum system demonstrate the superiority of the proposed controller over two conventional adaptive controllers.