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Harib et al. Intell Robot 2022;2(1):37-71 https://dx.doi.org/10.20517/ir.2021.19 Page 55

Figure 7. Flexible-joint manipulator model.

[135]
Chaoui et al. suggested an ANN-based control technique in 2009, which used ANNs’ learning and
approximation skills to estimate the system dynamics. The MRAC is made up of feedforward (ANN ) and
FF
feedback (ANN ) NN-based adaptive controllers. The reference model is built in the same manner as a
FBK
sliding hyperplane in variable structure control, and its output, which may be regarded as a filtered error
signal, is utilized as an error signal to adjust the ANN ’s weights. It comprises a first-order model that
FBK
specifies the required dynamics of the error between the desired and real load locations, as well as between
the motor and load velocity, in order to maintain internal stability. The ANN offers an approximate
FF
inverse model for the positioning system, while the ANN corrects residual errors, assuring the
FBK
manipulator’s internal stability and rapid controller response.

The feedback’s learning rate is dependent on the load inertia, which is a flaw in this construction. To
improve the stability region of the NN-based controllers, a supervisor is proposed to modify the learning
rate of the ANNs. The supervisor also increases the adaptation process’s convergence qualities.

Nowadays, the subject of multiple-arms manipulation highlights some interesting progress in using
intelligent control approaches. Hou et al. used a dual NN to solve a multicriteria optimization problem
[136]
for coordinated manipulation. Li et al. [137,138] are representatives who operate on several mobile manipulators
with communication delays. Some promising approaches, such as LMI and fuzzy-NN controls, were used in
both articles [137,138] , to improve motion/force performances, which were crucial in multilateral teleoperation
applications.