Design of Fuzzy Controller for Robot Manipulators Using Bacterial Foraging Optimization Algorithm

Trial and error method can be used to find a suitable design of a fuzzy controller. However, there are many options including fuzzy rules, Membership Functions (MFs) and scaling factors to achieve a desired performance. An optimiza-tion algorithm facilitates this process and finds an optimal design to provide a desired performance. This paper presents a novel application of the Bacterial Foraging Optimization algorithm (BFO) to design a fuzzy controller for tracking control of a robot manipulator driven by permanent magnet DC motors. We use efficiently the BFO algorithm to form the rule base and MFs. The BFO algorithm is compared with a Particle Swarm Optimization algorithm (PSO). Performance of the controller in the joint space and in the Cartesian space is evaluated. Simulation results show superiority of the BFO algorithm to the PSO algorithm.

Design of Radial Basis Function Network Using Adaptive Particle Swarm Optimization and Orthogonal Least Squares

This paper presents a two-level learning method for designing an optimal Radial Basis Function Network (RBFN) using Adaptive Velocity Update Relaxation Particle Swarm Optimization algorithm (AVURPSO) and Orthogonal Least Squares algorithm (OLS) called as OLS-AVURPSO method. The novelty is to develop an AVURPSO algorithm to form the hybrid OLS-AVURPSO method for designing an optimal RBFN. The proposed method at the upper level finds the global optimum of the spread factor parameter using AVURPSO while at the lower level automatically constructs the RBFN using OLS algorithm. Simulation results confirm that the RBFN is superior to Multilayered Perceptron Network (MLPN) in terms of network size and computing time. To demonstrate the effectiveness of proposed OLS-AVURPSO in the design of RBFN, the Mackey-Glass Chaotic Time-Series as an example is modeled by both MLPN and RBFN.

A Precise Robust Fuzzy Control of Robots Using Voltage Control Strategy

Fuzzy control of robot manipulators with a decentralized structure is facing a serious challenge. The state-space model of a robotic system including the robot manipulator and motors is in non-companion form, multivariable, highly nonlinear, and heavily coupled with a variable input gain matrix. Considering the problem, causes and solutions, we use voltage control strategy and convergence analysis to design a novel precise robust fuzzy control （PRFC） approach for electrically driven robot manipulators. The proposed fuzzy controller is Mamdani type and has a decentralized structure with guaranteed stability. In order to obtain a precise response, we regulate a fuzzy rule which governs the origin of the tracking space. The proposed design is verified by stability analysis. Simulations illustrate the superiority of the PRFC over a proprotional derivative like （PD-like） fuzzy controller applied on a selective compliant assembly robot arm （SCARA） driven by permanent magnet DC motors.

Type-2 Fuzzy Control for a Flexible-joint Robot Using Voltage Control Strategy

ype-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller （IT2FLC） to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization （PSO）. A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller （T1FLC） is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.

Modeling and Robust Discrete LQ Repetitive Control of Electrically Driven Robots

Discrete linear quadratic control has been efciently applied to linear systems as an optimal control.However,a robotic system is highly nonlinear,heavily coupled and uncertain.To overcome the problem,the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks.This modeling motivates us to develop an optimal repetitive control.The contribution of this paper is twofold.For the frst time,it presents discrete linear quadratic repetitive control for electrically driven robots using the mentioned model.The proposed control approach is based on the voltage control strategy.Second,uncertainty is efectively compensated by employing a robust time-delay controller.The uncertainty can include parametric uncertainty,unmodeled dynamics and external disturbances.To highlight its ability in overcoming the uncertainty,the dynamic equation of an articulated robot is introduced and used for the simulation,modeling and control purposes.Stability analysis verifes the proposed control approach and simulation results show its efectiveness.

Discrete-time indirect adaptive fuzzy control for robot manipulators

Purpose–The purpose of this paper is to design a discrete indirect adaptive fuzzy controller for a robotic manipulator.This paper addresses how to overcome the approximation error of the fuzzy system and uncertainties for asymptotic tracking control of robotic manipulators.The uncertainties include parametric uncertainty,un-modeled dynamics,discretization error and external disturbances.Design/methodology/approach–The proposed controller is model-free and voltage-based in the form of discrete-time Mamdani fuzzy controller.The parameters of fuzzy controller are adaptively tuned for asymptotic tracking of a desired trajectory.A robust control term is used to compensate the approximation error of the fuzzy system.An adaptive mechanism is derived based on the stability analysis.Findings–The proposed model-free discrete control is robust against all uncertainties associated with the robot manipulator and actuators.The approximation error of the fuzzy system is well compensated to achieve asymptotic tracking of the desired trajectories.Stability analysis and simulation results show its efficiency in the tracking control.Originality/value–A novel discrete indirect adaptive fuzzy controller is designed for electrically driven robot manipulators using the voltage control strategy.The novelty of this paper is compensating the approximation error of the fuzzy system and discretizing error for asymptotic tracking of the desired trajectory.

Voltage control strategy for an uncertain mobile robot

Purpose–The uncertainty and nonlinearity are the challenging problems for the control of a nonholonomic wheeled mobile robot.To overcome these problems,many valuable methods have been proposed by using two control loops namely the kinematic control and the torque control so far.In majority of the proposed approaches the dynamics of actuators is omitted for simplicity in the control design.This drawback degrades the control performance in high-velocity tracking control.On the other hand,to guarantee stability and overcome uncertainties,the control methods become computationally extensive and may be impractical due to using all states.The purpose of this paper is to design a simple controller with guaranteed stability for overcoming the nonlinearity,uncertainty and actuator dynamics.Design/methodology/approach–The control design includes two control loops,the kinematic control loop and the novel dynamic control loop.The dynamic control loop uses the voltage control strategy instead of the torque control strategy.Feedbacks of the robot orientation,robot position,robot linear and angular velocity,and motor currents are given to the control system.Findings–To improve the precision,the dynamics of motors are taken into account.The most important advantages of the proposed control law is that it is free from the robot dynamics,thereby the controller is simple,fast response and robust with ignorable tracking error.The control approach is verified by stability analysis.Simulation results show the effectiveness of the proposed control applied on an uncertain nonholonomic wheeled mobile robot driven by permanent magnet dc motors.A comparison with an adaptive sliding-mode dynamic control approach confirms the superiority of the proposed approach in terms of precision,simplicity of design and computations.Originality/value–The originality of the paper is to present a new control design for an uncertain nonholonomic wheeled mobile robot by using voltage control strategy in replace of the torque control strategy.In addition,a novel state-space model of electrically driven nonholonomic wheeled mobile robot in the workspace is presented.

Guaranteed-stability adaptive fuzzy control of a hydraulic elevator

Purpose-A hydraulic elevator including the hydraulic actuator and cabin is highly nonlinear with many parameters and variables.Its state-space model is in non-companion form and uncertain due to the parametric errors,flexibility of the ropes,friction and external load disturbances.A model-based control cannot perform well while a precise model is not available and all state variables cannot be measured.To overcome the problems,this paper aims to develop a direct adaptive fuzzy control(DAFC)for the hydraulic elevator.Design/methodology/approach-The controller is an adaptive PD-like Mamdani type fuzzy controller using position error and velocity error as inputs.The design is based on the stability analysis.Findings-The proposed control can overcome uncertainties,guarantee stability,provide a good tracking performance and operate as active vibration suppression by tracking a smooth trajectory.The controller is not involved in the nonlinearity,uncertainty and vibration of the system due to being free from model.Its performance is superior to a PD-like fuzzy controller due to being adaptive as illustrated by simulations.Originality/value-The proposed DAFC is applied for the first time on the hydraulic elevator.Compared to classic adaptive fuzzy,it does not require all system states.In addition,it is not limited to the systems,which have the state-space model in companion form and constant input gain,thus is much less computational and easier to implement.