Design of Circuitry and Programming of a Climbing Robot for MIRoC 2014 Competition

The climbing robot created for this project is aimed to join the Malaysian International Robot Competition (MIRoC) 2014. Arduino microcontroller was chosen to control the movement of a climbing robot. Designing and testing of the actuator system, circuitry and programming must be done in order to produce a robot with the most efficient algorithm of movement. Servo motors are used for the actuator system of the robot. The robot faces problem when climbing when there is too much friction between the gripper of the robot and the rope to be climbed. Thus, the gripping force produced by the robot must be considered and compared with the weight of the robot. The angle of the gripper at the moment the robot is climbing also plays an important role in this. Of course, the study of kinematics with regards of the mechanisms of the robot can be really helpful in making sure the smooth movement of the robot when climbing

Estimator Torsi Beban Sistem Servo Modular MS150 DC Berbasis Jaringan Syaraf Tiruan

Di era sekarang, motor DC telah dipakai dalam skala yang luas mulai dari sistem yang memerlukan sensitivitas rendah hingga tinggi. Untuk sistem bersensitivitas tinggi, dibutuhkan tingkat presisi yang terjaga dengan maksimal walaupun terdapat gangguan berupa torsi beban pada shaft motor. Tingkat kesulitan yang tinggi dan mahalnya sensor untuk mengukur secara langsung mengakibatkan diperlukannya estimasi untuk mengetahui besarnya torsi beban pada shaft motor. Untuk mengatasi permasalahan ini, dilakukanlah penelitian mengenai estimator torsi beban pada motor DC menggunakan Jaringan Syaraf Tiruan (JST). Hasil dari penelitian ini didapatkan rancangan model JST terbaik untuk estimasi torsi beban dengan arsitektur 3 node masukan, antara lain tegangan masukan, arus,dan kecepatan sudut, 9 hidden node, dan 1 node keluaran, yakni torsi beban itu sendiri. Dari hasil pelatihan dan pengujian model JST didapatkan nRMSE sebesar 3.75% dan 4.038% berturut-turut

Design of Circuitry and Programming of a Climbing Robot for MIRoC 2014 Competition

The climbing robot created for this project is aimed to join the Malaysian International Robot Competition (MIRoC) 2014. Arduino microcontroller was chosen to control the movement of a climbing robot. Designing and testing of the actuator system, circuitry and programming must be done in order to produce a robot with the most efficient algorithm of movement. Servo motors are used for the actuator system of the robot. The robot faces problem when climbing when there is too much friction between the gripper of the robot and the rope to be climbed. Thus, the gripping force produced by the robot must be considered and compared with the weight of the robot. The angle of the gripper at the moment the robot is climbing also plays an important role in this. Of course, the study of kinematics with regards of the mechanisms of the robot can be really helpful in making sure the smooth movement of the robot when climbing

Adaptive RBF network control for robot manipulators

TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed network includes a hidden layer with one node, two inputs and a single output. In comparison with other model-free estimators such as multilayer neural networks and fuzzy systems, the proposed estimator is simpler, less computational and more effective. The weights of the RBF network are tuned online using an adaptation law derived by stability analysis. Despite the majority of previous control approaches which are the torque-based control, the proposed control design is the voltage-based control. Simulations and comparisons with a robust neural network control approach show the efficiency of the proposed control approach applied on the articulated robot manipulator driven by permanent magnet DC motors

Discrete time robust control of robot manipulators in the task space using adaptive fuzzy estimator

This paper presents a discrete-time robust control for electrically driven robot manipulators in the task space. A novel discrete-time model-free control law is proposed by employing an adaptive fuzzy estimator for the compensation of the uncertainty including model uncertainty, external disturbances and discretization error. Parameters of the fuzzy estimator are adapted to minimize the estimation error using a gradient descent algorithm. The proposed discrete control is robust against all uncertainties as verified by stability analysis. The proposed robust control law is simulated on a SCARA robot driven by permanent magnet dc motors. Simulation results show the effectiveness of the control approach

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°

Neural Adaptive Backstepping Control of a Robotic Manipulator With Prescribed Performance Constraint

IEEE This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom manipulator driven by an electrohydraulic actuator. To restrict the system output in a prescribed performance constraint, a weighted performance function is designed to guarantee the dynamic and steady tracking errors of joint angle in a required accuracy. Then, a radial-basis-function NN is constructed to train the unknown model dynamics of a manipulator by traditional backstepping control (TBC) and obtain the preliminary estimated model, which can replace the preknown dynamics in the backstepping iteration. Furthermore, an adaptive estimation law is adopted to self-tune every trained-node weight, and the estimated model is online optimized to enhance the robustness of the NN controller. The effectiveness of the proposed control is verified by comparative simulation and experimental results with Proportional-integral-derivative and TBC methods

A Robust Adaptive Control using Fuzzy Neural Network for Robot Manipulators with Dead-Zone

In this paper, a robust-adaptive-fuzzy-neural-network controller (RAFNNs) bases on dead zone compensator for industrial robot manipulators (RM) is proposed to dead the unknown model and external disturbance. Here, the unknown dynamics of the robot system is deal by using fuzzy neural network to approximate the unknown dynamics. The online training laws and estimation of the dead-zone are determined by Lyapunov stability theory and the approximation theory. In this proposal, the robust sliding-mode-control (SMC) is constructed to optimize parameter vectors, solve the approximation error and higher order terms. Therefore, the stability, robustness, and desired tracking performance of RAFNNs for RM are guaranteed. The simulations and experiments performed on three-link RM are provided in comparison with neural-network (NNs) and proportional-integral-derivative (PID) to demonstrate the robustness and effectiveness of the RAFNNs

Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator

This article proposes a new incremental model predictive control (IMPC) strategy, which allows for constrained control of a robot manipulator, while the resulting incremental model is derived without a concrete mathematical system model. First, to reduce dependence on the nominal model of robot manipulators, the continuous-time nonlinear system model is approximated by an incremental system using the time-delay estimation (TDE). Then, based on the incremental system, the tracking IMPC is designed in the framework of MPC without terminal ingredients. Thus, compared with existing MPC methods, the nominal mathematical model is not required. Moreover, we investigate reachable reference trajectories and confirm the local input-to-state stability (ISS) of IMPC, considering the bounded TDE error as the disturbance of the incremental system. For reachable reference trajectories, the local ISS of IMPC is analyzed using the continuity of the value function, and the cumulative error bound is not overconservative. Finally, several real-time experiments are conducted to verify the effectiveness of IMPC. Experimental results show that the system can achieve optimal control performance while guaranteeing that input and state constraints are not violated