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Sliding mode control , variable structure control, and suction control fall within the same class of control
techniques and are somewhat synonymous. The control law in this class is generally a switching controller.
A variety of switching criteria may be employed. Sliding mode control may be treated as an adaptive control
technique. Because the switching surface is not fixed, its variability is somewhat analogous to an adaptation
criterion. Specifically, the error of the plant response is zero when the control falls on the sliding surface.

H∞ (H-infinity) control is an optimal control approach, which is different from the LQG method. This
frequency-domain technique assumes a linear plant with constant parameters, which may be modeled by a
transfer function (matrix in the general case). The underlying design problem is to select a suitable
controller that will result in the required performance of the system. In other words, the closed-loop
transfer matrix must be properly “shaped” through an appropriate choice of the controller. Specifically, the
controller that minimizes the “H∞ norm” of the closed-loop transfer matrix, which is the maximum value
of the largest singular value of this matrix, is used.

For complex multi-robot systems having various and stringent operating requirements, distributed and
networked control is appropriate. It may consist of many programmable logic controllers and a supervisory
controller, which will supervise, manage, coordinate and control the overall system. In hierarchical control,
the distribution of control is provided both geographically and functionally. The management decisions,
supervisory control, and coordination between robots may be provided by the supervisory controller, which
is at the highest level of the hierarchy. The next lower level may generate control settings (or reference
inputs) for each control region (subsystem). Finally, setpoints and reference signals are inputs to the direct
controllers of the robots. In master-slave distributed control, only downloading of information is available.

5.2. Intelligent control
In intelligent control, an “intelligent” method of decision-making is used to make the control decision (i.e.,
to generate the control action). Soft computing, consisting of neural networks, fuzzy systems, evolutionary
computing, and even probabilistic methods, has been popularly used in intelligent control. The topic of soft
computing has already been addressed under the general theme of the present paper and is not repeated
here. However, it is adequate to mention that, since learning control is used in robotic control, any
approach of machine learning such as deep learning and deep neural networks, as discussed earlier in the
paper, maybe incorporated into intelligent control of robots.

6. OPPORTUNITIES OF ROBOTICS
The commercial applications of Intelligent Robotics (with AI) include: autonomous agents such as self-
driving vehicles (encompassing aerial, ground-based, and underwater vehicles), which are indeed mobile
robots; assistive devices (active and adaptive prostheses, wearables, and hand-held smart devices); advisory
systems (or, expert systems, which are used in such areas as medical, legal, business, service, and social);
monitoring/security systems (they are applicable in such areas as machine fault detection, prediction and
diagnosis; and for human health monitoring, in telemedicine, homecare, etc.; video analysis; cyber security;
human-machine interaction (including natural language processing, facial expression detection, speech
recognition, communication, and intelligent connectivity; industrial application (including manufacturing
and the assessment of production quality, cost, and efficiency); consumer, service, and entertainment
sectors (retail, domestic, social, etc.); agriculture (growing, fertilizing, weed removal, and harvesting); smart
buildings (heating ventilation, and air conditioning - HVAC; smart metering, safety, smart appliances,
automated lighting, and achieving energy efficiency); education (“intelligent” learning management system
or LMS, collaboration among students and with teachers - this approach may be quite beneficial in the
current epidemic situation of Covid-19); and energy and environment (distribution, exploration,