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Page 71 Li et al. Intell Robot 2021;1(1):58-83 I http://dx.doi.org/10.20517/ir.2021.08

Figure 10. The hunting process with one target and six AUVs. : the target; 1- 6: the AUVs [86] .

controllerbytakingadvantageofvitalcharacteristicsofhumanintelligence,suchasfuzzylogic,neuralnetwork,
etc. bio-inspired intelligent control mechanism is based on biological systems. Using this biologically inspired
system is targeting to improve the control performance by the implementation of natural biological systems in
the control design.

4.1. Tracking control
The tracking control of robots or motors has been studied for many years. Sliding mode control is robust
to variable changes, however this method suffers chattering issues, which is a critical factor that needs to be
considered when designing the control strategy. The linearization control method is easy to implement, how-
ever, it suffers from a large velocity jump when a large tracking error occurs at the initial stage. Backstepping
control is easy to design, however, when a large tracking error occurs, this method becomes impractical as the
speed jump will result in a large velocity surge, which can damage the hardware of the system. Neural network
and fuzzy logic control are capable of resolving the large velocity jump at the initial stage, however, both neu-
ral network and fuzzy logic control are hard to practice. The neural network-based control methods require
online learning, which is expensive and computationally complicate, the fuzzy logic control requires human
experience to make the robot perform well, both of these control methods are rather expensive to practice.

The bio-inspired backstepping control, which is based on the backstepping technique, aims to eliminate the
speed jump in conventional design when a large initial tracking error occurs. The general control design for
the unmanned robot with the implementation of bio-inspired neural dynamics can be described in Figure 11.
The motion planner plans the desired posture , then the desired trajectory along with the feedback of the
current posture propagates through a transformation matrix to convert the tracking error from the inertial
frame into body fixed frame. Then, the path tracker, which contains the bio-inspired backstepping controller
uses the tracking error and desired velocity to generate a velocity command, which then along with the ob-
served velocity propagate through torque controller to generate torque command, which drives the robot
to generate a velocity and reach its desired posture by propagating the velocity that is generated from robot
dynamics to robot kinematics.

The applications of bio-inspired backstepping control are mainly divided into three different platforms: mobile
robots, surface robots, and underwater robots. Therefore, this section illustrates the efficiency, effectiveness,
and applications of the bio-inspired backstepping control into these three different platforms.