Robotic systems are mechanical systems with actuators, motors and brakes. The main theme in robotics research and applications: the design considerations and strategies in robotic control systems. This book provides the reader with an in-depth knowledge of intelligent controllers design in the field of robotics. The performance of these controllers is verified using simulations in the MATLAB environment.
This book focuses on the presentation and application of intelligent controller approaches using conventional and non-convective control techniques.
The main objectives of the book are:
- To provide a list of intelligent control design methods that can be implemented for robotics engineering applications;
- To present advanced methods of designing intelligent controllers and their stability analysis methods;
- And to validate each intelligent controller, through numerical simulations of robotic systems examples.
The book is organized into three chapters as follows:
The chapter 1 is devoted to the presentation of the parallel robotics and the control of a parallel robot by a classic controller for the tracking of the reference trajectories.
The field of parallel robotics is fascinating because there seems to be an infinity of research topics, of increasing complexity but still surmountable. That’s why, it’s not surprising that thousands of researchers are interested in this field today. More and more manufacturers are adopting parallel architectures for their products: motion simulators, industrial robots, positioners, joysticks, etc.
Parallel robots have shown for decades that they can perform well in terms of speed and acceleration. Parallel kinematic manipulators (PKMs) are defined as follows: “A generalized parallel manipulator is a closed-loop kinematic chain mechanism whose end-effector is linked to the base by several independent kinematic chains”. Among the parallel robots with the strongest acceleration capacities, we find the VELOCE robot.
Chapter 1 deals with the control of parallel robot of four degrees of freedom called VELOCE. Thus, a conventional PID controller is applied on this system to improve the desired performance as stability and accuracy. In this chapter, we demonstrate the strength of this PID controller applied to a parallel kinematic manipulator known for its high non-linearity, time- varying parameters and uncertainties. Finally, the numerical simulations results are given and discussed.
In Chapter 2 , we looked at the nonlinear control of underactuated mechanical systems, these systems having fewer actuators than degrees of freedom. This category of systems has been presented in details and their particular properties such as the non-holonomic of the constraints on their dynamics have been highlighted. Indeed, the dynamics of such systems are not entirely linearizable in the general case. The dynamics of the subsystem made up of non-linearized coordinates, called internal dynamics, are generally not stable. We then present non-minimum phase systems. It is therefore necessary to design a specific control technique for this type of under-actuated system.
We propose to apply the adaptive fuzzy control technique for an under-actuated system called “inertia wheel inverted pendulum”. This control technique takes into account the variation of the robot’s parameters, the non-linearity of the model to be controlled, and measurement errors due to the sensors which negatively influence the following of the inverted pendulum trajectory. A control law which takes these different factors into account would be an adequate solution to this control problem. Several simulation results have been presented and interpreted in order to observe the trajectory followed by the inertia wheel inverted pendulum after this control has been applied.
In chapter 3, we are interested in the control of an exoskeleton-type robot for neuromotor rehabilitation. In fact, rehabilitation is one of the new fields of application of robotics in physical interaction. In this field, attempts are made to design mechanisms that can assist the movements of patients with neuromotor disorders in achieving physical movements. One of the important contributions is to be able to offer systems capable of controlling the mechanical forces distributed along the patient’s limbs during movements.
The main focus in this chapter is the upper-limb robot control which allows the tracking of the reference trajectories of movements desired by the sick patient. To improve the quality of the control of the forces in the implementation of robotic exoskeletons, the main contributions are in the fields of design and robust control by adaptive sliding mode control. This work made it possible to propose a method to assess the quality of the human-exoskeleton interaction in cohandling tasks. Indeed, in order to be able to quantify the contribution of the various proposals in the field of design and control, we had to establish a control allowing to study in a reproducible manner the physical human-robot interaction.
