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Harib et al. Intell Robot 2022;2(1):37-71 https://dx.doi.org/10.20517/ir.2021.19 Page 53

[96]
Figure 6. Block diagram of the ANN-based adaptive control scheme . ANN: Artificial Neural Network.

successfully. Furthermore, the motion and posture errors are kept to a minimum, resulting in a smooth
control signal.

3.3. Applications for robotic manipulators
There has been great interest in universal controllers that mimic the functions of human processes to learn
about the systems they are controlling on-line so that performance improves automatically. NN-based
controllers are derived for robot manipulators in a variety of applications, including position control, force
control, link flexibility stabilization and the management of high-frequency joint and motor dynamics. The
nature of joint torques must be determined for the end effector to follow the required trajectory as quickly
and accurately as feasible, which is a common difficulty for robot manipulators. Both parametric and
structural uncertainties necessitate adaptive control. Parametric uncertainties originate from a lack of
accurate information about the manipulator’s mass characteristics, unknown loads, and load location
uncertainty, among other things. Structural uncertainties are a result of the presence of high-frequency
unmodeled dynamics, resonant modes, and other structural reservations.

The late 1980s and early 1990s were booming years for both NNs and robotic manipulators research. In this
era, the literature survey concerning the application of NNs in robotic manipulators is very rich. Thus, we
direct the readers to some interesting approaches in these studies [97-102] and the references therein.

From 1987 to 1989, Miller et al. [103-107] discuss a broad CMAC learning technique and its application to
robotic manipulators’ dynamic control. The dynamics do not need to be known in this application.
Through input and output measurements, the control scheme learns about the process. The findings show
that when compared to fixed-gain controllers, the CMAC learning control performs better. Also, because
measured and estimated values must be transformed to discrete form, each variable’s resolution and range
must be carefully selected, and the number of memory regions handled by each input state in the CMAC
[108]
architecture is the most important design parameter. In another popular approach, Miller et al. used
CMAC in the real-time control of an industrial robot and other applications. In their network, they utilize
hundreds of thousands of adjustable weights that, in their experience, converge in a few iterations.