Global Saturated Regulator with Variable Gains for Robot Manipulators

In this paper, we propose a set of saturated controllers with variable gains to solve the regulation problem for robot manipulators in joint space. These control schemes deliver torques inside the prescribed limits of servomotors. The gamma of variable gains is formed by continuous, smooth, and differentiable functions of the joint position error and velocity of the manipulator. A strict Lyapunov function is proposed to demonstrate globally asymptotic stability of the closed-loop equilibrium point. Finally, the functionality and performance of the proposal are illustrated via simulation results and comparative analysis against Proportional-Derivative (PD) control scheme on a two-degrees-freedom direct-drive robot manipulator

Fractional-order sliding mode controller for the two-link robot arm

In this paper, the author proposes a sliding mode controller with the fractional-order for the two-link robot arm. Firstly, the model and dynamic equations of the two-link robot arm are presented. Based on these equations, the author builds the controller for each joint of the robot. The controller is a sliding mode controller with its order is not an integer value. The task of the controller is to adjust the torques acted on the joints in order for the angular coordinates of each link to coincide with the desired values. The effectiveness of the proposed control system is demonstrated through Matlab-Simulink software. The robot model and controller are built to investigate the system quality. The results show that the quality of the control system is very high: there is not the chattering phenomenon of torques, the response angles of each link quickly reach the desired values, and the static error equal to zero

Sliding mode control-based system for the two-link robot arm

In this research, the author presents the model of the two-link robot arm and its dynamic equations. Based on these dynamic equations, the author builds the sliding mode controller for each joint of the robot. The tasks of the controllers are controlling the Torque in each Joint of the robot in order that the angle coordinates of each link coincide with the desired values. The proposed algorithm and robot model are built on Matlab-Simulink to investigate the system quality. The results show that the quality of the control system is very high: the response angles of each link quickly reach the desired values, and the static error equal to zero

Advances in PID Control

Since the foundation and up to the current state-of-the-art in control engineering, the problems of PID control steadily attract great attention of numerous researchers and remain inexhaustible source of new ideas for process of control system design and industrial applications. PID control effectiveness is usually caused by the nature of dynamical processes, conditioned that the majority of the industrial dynamical processes are well described by simple dynamic model of the first or second order. The efficacy of PID controllers vastly falls in case of complicated dynamics, nonlinearities, and varying parameters of the plant. This gives a pulse to further researches in the field of PID control. Consequently, the problems of advanced PID control system design methodologies, rules of adaptive PID control, self-tuning procedures, and particularly robustness and transient performance for nonlinear systems, still remain as the areas of the lively interests for many scientists and researchers at the present time. The recent research results presented in this book provide new ideas for improved performance of PID control applications

Intelligent robust control of redundant smart robotic arm Pt I: Soft computing KB optimizer - deep machine learning IT

Redundant robotic arm models as a control object discussed. Background of computational intelligence IT based on soft computing optimizer of knowledge base in smart robotic manipulators introduced. Soft computing optimizer is the toolkit of deep machine learning SW platform with optimal fuzzy neural network structure. The methods for development and design technology of intelligent control systems based on the soft computing optimizer presented in this Part 1 allow one to implement the principle of design an optimal intelligent control systems with a maximum reliability and controllability level of a complex control object under conditions of uncertainty in the source data, and in the presence of stochastic noises of various physical and statistical characters. The knowledge bases formed with the application of a soft computing optimizer produce robust control laws for the schedule of time dependent coefficient gains of conventional PID controllers for a wide range of external perturbations and are maximally insensitive to random variations of the structure of control object. The robustness of control laws is achieved by application a vector fitness function for genetic algorithm, whose one component describes the physical principle of minimum production of generalized entropy both in the control object and the control system, and the other components describe conventional control objective functionals such as minimum control error, etc. The application of soft computing technologies (Part I) for the development a robust intelligent control system that solving the problem of precision positioning redundant (3DOF and 7 DOF) manipulators considered. Application of quantum soft computing in robust intelligent control of smart manipulators in Part II described

Robust Learning Control for Shipborne Manipulator With Fuzzy Neural Network

The shipborne manipulator plays an important role in autonomous collaboration between marine vehicles. In real applications, a conventional proportional-derivative (PD) controller is not suitable for the shipborne manipulator to conduct safe and accurate operations under ocean conditions, due to its bad tracing performance. This paper presents a real-time and adaptive control approach for the shipborne manipulator to achieve position control. This novel control approach consists of a conventional PD controller and fuzzy neural network (FNN), which work in parallel to realize PD+FNN control. Qualitative and quantitative tests of simulations and real experiments show that the proposed PD+FNN controller achieves better performance in comparison with the conventional PD controller, in the presence of uncertainty and disturbance. The presented PD+FNN eliminates the requirements for precise tuning of the conventional PD controller under different ocean conditions, as well as an accurate dynamics model of the shipborne manipulator. In addition, it effectively implements a sliding mode control (SMC) theory-based learning algorithm, for fast and robust control, which does not require matrix inversions or partial derivatives. Furthermore, simulation and experimental results show that the angle compensation deviation of the shipborne manipulator can be improved in the range of ±1°

Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems

In this article, a novel self-evolving data cloud-based proportional integral derivative (PID) (SEDCPID) like controller is proposed for uncertain nonlinear systems. The proposed SEDCPID controller is constructed by using fuzzy rules with nonparametric data cloud-based antecedence and PID-like consequence. The antecedent data clouds adopt the relative data density to represent the fuzzy firing strength of input variables instead of the explicit design of the membership functions in the classical sense. The proposed SEDCPID controller has the advantages of evolving structure and adapting parameter concurrently in an online manner. The density and distance information of data clouds are proposed to achieve the addition and deletion of data clouds and also a stable recursive method is proposed to update the parameters of the PID-like subcontrollers for the fast convergence performance. Based on the Lyapunov stability theory, the stability of the proposed controller is proven and the proof shows the tracking errors converge to a small neighborhood. Numerical and experimental results illustrate the effectiveness of the proposed controller in handling the uncertain nonlinear dynamic systems

Direct force control of upper-limb exoskeleton based on fuzzy adaptive algorithm

In order to synchronize human and machine positions and minimize human-machine interaction forces in exoskeleton control, we present a two-degree-of-freedom (2-DOF) upper-limb exoskeleton model with power enhancement and direct force control strategy based on fuzzy adaptive algorithm. The conventional PD controller is widely used in exoskeleton control because it is model independent and its gains can be easily tuned. However, the speed of movement of the operator and the mass of external load are uncertain in practice; hence, the parameters of a conventional PD controller have to be adjusted according to the velocity of the motion and external loads to ensure the effectiveness of trajectory tracking. Additionally, there is a lag in the response time when the operator starts to move or changes direction suddenly. Therefore, this study proposes the use of an adaptive controller combining the fuzzy set techniques and PD controller to improve trajectory tracking. Robustness testing of the fuzzy PD controller for the external load uncertainty and motion velocity change are also investigated. The simulation results clearly indicate the superior performance of the fuzzy adaptive PD controller over the conventional one for tracking performance with external load uncertainty and motion velocity variance