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Memory, as the basis of learning, determines the storage, update andforgetting of knowledge and further determines the efficiency of learning.Featured with the mechanism of memory, a radial basis function neural networkbased learning control scheme named real-time progressive learning (RTPL) isproposed to learn the unknown dynamics of the system with guaranteed stabilityand closed-loop performance. Instead of the Lyapunov-based weight update law ofconventional neural network learning control (NNLC), which mainly concentrateson stability and control performance, RTPL employs the selective memoryrecursive least squares (SMRLS) algorithm to update the weights of the neuralnetwork and achieves the following merits: 1) improved learning speed withoutfiltering, 2) robustness to hyperparameter setting of neural networks, 3) goodgeneralization ability, i.e., reuse of learned knowledge in different tasks,and 4) guaranteed learning performance under parameter perturbation. Moreover,RTPL realizes continuous accumulation of knowledge as a result of itsreasonably allocated memory while NNLC may gradually forget knowledge that ithas learned. Corresponding theoretical analysis and simulation studiesdemonstrate the effectiveness of RTPL.

Real-Time Progressive Learning: Accumulate Knowledge from Control with Neural-Network-Based Selective Memory