An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inp...

Students' beliefs and goals can powerfully influence their learning success. Those who believe intelligence is a fixed entity (entity theorists) tend to emphasize 'performance goals,' leaving them vulnerable to negative feedback and likely to disengage from challenging learning opportunities. In contrast, students who believe intelligence is malleable (incremental theorists) tend to emphasize '...

The speed, accuracy, and adaptability of human movement depends on the brain performing an inverse kinematics transformation — that is, a transformation from visual to joint angle coordinates — based on learning from experience. In human motion control, it is important to learn a feedback controller for the hand position error in the human inverse kinematics solver. This paper proposes a novel ...

The role of feedback in perceptual learning is probed in an orientation discrimination experiment under destabilizing non-stationary conditions, and explored in a neural-network model. Experimentally, perceptual learning was examined with periodic alteration of a strong external noise context. The speed of learning, the performance loss at each change in external noise context (switch cost), an...

Motor error evaluation appears to be a hierarchically organized process subserved by 2 distinct systems: a higher level system within medial-frontal cortex responsible for movement outcome evaluation (high-level error evaluation) and a lower level posterior system(s) responsible for the mediation of within-movement errors (low-level error evaluation). While a growing body of evidence suggests t...

In iterative learning control schemes for linear discrete time systems, conditions to guarantee the monotonic convergence of the tracking error norms are derived. By using the Markov parameters, it is shown in the time-domain that there exists a non-increasing function such that when the properly chosen constant learning gain is multiplied by this function, the convergence of the tracking error...

We describe a four-week series of assignments in an undergraduate AI course at a liberal arts college developing a supervised learning solution to the problem of classifying Twitter accounts as either a person account or a non-person account (e.g. organization or spambot). This problem employs real data in an ongoing research project by the first author, yet is accessible to students with limit...

Intense ultrashort pulse lasers are used for fs resolution pump-probe experiments more and more at large scale facilities, such as free electron lasers (FEL). Measurement of the arrival time of the laser pulses and stabilization to the machine or other sub-systems on the target, is crucial for high time-resolution measurements. In this work we report on a single shot, spectrally resolved, non-c...

This paper represents a novel online self-learning disturbance observer (SLDO) by benefiting from the combination of a type-2 neuro-fuzzy structure (T2NFS), feedback-error learning scheme and sliding mode control (SMC) theory. The SLDO is developed within a framework of feedback-error learning scheme in which a conventional estimation law and a T2NFS work in parallel. In this scheme, the latter...