Robot manipulators are highly coupled nonlinear systems, therefore real system and mathematical model of dynamics used for control system design are not same. Hence, fine-tuning of controller is always needed. For better tuning fast simulation speed is desired. Since, Matlab incorporates LAPACK to increase the speed and complexity of matrix computation, dynamics, forward and inverse kinematics ...

Parallel robots exhibit good performance in terms of rigidity, accuracy, and dynamic characteristics. However, parallel robots have complex configurations and their dynamic model is highly nonlinear, and conventional PID controllers are not sufficiently robust for their motion control. In this paper, we have investigated the intelligent control of a hydraulically driven parallel robot based on ...

In this paper, we propose a data-based auto-tuning method for industrial PID controllers, which does not rely on a model of the plant. The method is inspired by the Virtual Reference Feedback Tuning approach for data-based controller tuning, but it is taylored to the framework of PID controller design. The method is entirely developed in a deterministic, continuous time setting, where the assum...

In various industrial systems, parameters variation is one of the major problems faced by control engineers now days. To overcome the problem of parameter variations, this paper proposes the hybridization of MIT rule based online tuning of classical PID controllers with Differential Evolution algorithm. The hybridization of two techniques results in the offline as well as online tuning of PID c...

In this paper, determining the optimal proportionalintegral-derivative (PID) controller gains of an single-area load frequency control (LFC) system using genetic algorithm (GA) is presented. The LFC is notoriously difficult to control optimally using conventionally tuning a PID controller because the system parameters are constantly changing. It is for this reason the GA as tuning strategy was ...

Robot manipulators are highly coupled nonlinear systems, therefore real system and mathematical model of dynamics used for control system design are not same. Hence, fine-tuning of controller is always needed. For better tuning fast simulation speed is desired. Since, Matlab incorporates LAPACK to increase the speed and complexity of matrix computation, dynamics, forward and inverse kinematics ...

Most of the proportional-integral-derivative (PID) controller tuning methods reported in literature are based on the approximate plant models (FOPDT or SOPDT models) derived from the step response of the plant. In this paper, a new method of designing PID controllers using ‘impulse response’ instead of ‘step response’ of the plant is presented. Treating the impulse response of the plant as a st...

This paper attempts to tune any controller without the knowledge of mathematical model for the system to be controlled. For that purpose, the optimization algorithm of MATLAB / Nonlinear Control Design Blockset (NCD) is adapted for On-line tuning for controller parameters. To present the methodology, a PID controller is verified with the physical plant using the engine speed control System wher...

This paper is concerned with real-time optimal-tuning PID control design for industrial hydraulic systems. Several optimal-tuning controller design techniques are used for a hydraulic position control system. After analysis on the system, a nonlinear dynamical model is derived. A nonlinear PID control scheme with inverse of dead zone is introduced to overcome the dead zone in this hydraulic sys...

Many areas in power systems require solving one or more nonlinear optimization problems. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. The proposed method utilizes the Particle Swarm Optimization (PSO) algor...