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Page 54 Harib et al. Intell Robot 2022;2(1):37-71 https://dx.doi.org/10.20517/ir.2021.19

[109]
Huan et al. examine the issue of building robot hand controllers that are device-dependent. Their
argument for a controller like this is that it would isolate low-level control issues from high-level
capabilities. They employ a BP algorithm with a single hidden layer comprised of four neurons to achieve
this goal. The inputs are determined by the object’s size, while the outputs are determined by the grab
modes. In this way, they have demonstrated how to build a p-g table using simulation. Another BP
[110]
architecture was used by Wang and Yeh to control a robot model which simulates PUMA560. A network
to simulate the plant and a controller network make up their self-adaptive neural controller (SANC). The
plant model is trained either off-line with mathematical model outputs or on-line with plant outputs
through excitations. The control network is modified by working in series with the plant network during
the “controlling and adapting” phase. The control network is also trained off-line in a “memorizing phase”
with data from the adapting phase in a random way, which is another element of this training. This trait,
according to the authors, aids in overcoming the temporal instability that is inherent with BP. Their
numerical findings show that the SANC technique produces good trajectory-tracking accuracy.

Up to the early 2000s, the main goal of robotic manipulators designs was to minimize vibration and achieve
good position accuracy, which led to maximizing stiffness. This high stiffness is achieved by using heavy
material and a bulky design. As a result, it is demonstrated that heavy rigid manipulators are wasteful in
terms of power consumption and operational speed. It is necessary to reduce the weight of the arms and
increase their speed of action in order to boost industrial output. As a result of their light weight, low cost,
bigger work volume, improved mobility, higher operational speed, power economy, and a wider range of
applications, flexible-joint manipulators have gotten much attention. Figure 7 shows a representation of a
flexible joint manipulator model.

Controlling such systems, however, still challenges significant nonlinearities, such as coupling caused by the
manipulator’s flexibility, changing operating conditions, structured and unstructured dynamical
uncertainties, and external disturbances. Complex dynamics regulate flexible-joint manipulators [111-114] . This
emphasizes the need to examine alternate control techniques for these types of manipulator systems in
order to meet their increasingly stringent design criteria. Many control laws for flexible joints have been
presented in those studies [115-118] to solely address (structured) parametric uncertainties. The proposed
controllers need a complete a priori knowledge of the system dynamics. Several adaptive control
systems [119-121] have been proposed to alleviate this necessity. The majority of these control strategies use
singular perturbation theory to extend adaptive control theory established for rigid bodies to flexible
ones [122-125] .

Based on all the above reasons, computational intelligence techniques, such as ANNs and fuzzy logic
controllers, have been credited in a variety of applications as powerful controllers of the types of systems
that may be subjected to structured and unstructured uncertainties [126,127] . As a result, there have been
advancements in the field of intelligent control [128,129] . Various neural network models have been used to
operate flexible-joint manipulators, and the results have been adequate . Chaoui et al. [131,132] developed a
[130]
control strategy inspired by sliding mode control that uses a feedforward-NN to learn the system dynamics.
[133]
Hui et al. proposed a time-delay neuro-fuzzy network. The joint velocity signals were estimated using a
linear observer in this system, which avoided the need to measure them directly. Subudhi and Morris
[134]
proposed a hybrid architecture that included a NN for controlling the slow dynamic subsystem and an H
∞
for controlling the rapid dynamic subsystem. Despite its effectiveness, NN-based control systems are still
unable to incorporate any humanlike experience already obtained about the dynamics of the system in
question, which is regarded as one of the soft computing approaches’ primary flaws.