A Self-learning Nonlinear Variable Gain Proportional Derivative (PD) Controller in Robot Manipulators

This paper proposes a nonlinear variable gain Proportional-Derivative (PD) controller that exhibits self-constructing and self-learning capabilities. In this method, the conventional linear PD controller is augmented with a nonlinear variable PD gain control signal using a dynamic structural network. The dynamic structural network known as Growing Multi-Experts etwork grows in time by placing hidden nodes in regions of the state space visited by the system during operation. This results in a network that is "economic" in terms of network sileo The proposed approach enhances the adaptability of conventional PD controller while preserving its' linear structure. Based on the simulation study on variable load and friction compensation, the fast adaptation is shown to be able to compensate the non-linearity and the uncertainty in the robotic system

A New Computed Torque Control System with an Uncertain RBF Neural Network Controller for a 7-DOF Robot

A novel percutaneous puncture robot system is proposed in the paper. Increasing the surgical equipment precision to reduce the patient\u27s pain and the doctor\u27s operation difficulty to treat smaller tumors can increase the success rate of surgery. To attain this goal, an optimized Computed Torque Law (CTL) using a radial basis function (RBF) neural network controller (RCTL) is proposed to improve the direction and position accuracy. BRF neural network with an uncertain term (URBF) which is able to compensate the system error caused by the imprecision of the model is added in the RCTL system. At first, a 7-DOF robotic system is established. It consists of robotic arm and actuator control channels. Now, the RBF compensator is added to the CTL to adjust the robot arm to reduce the position and direction errors. The angle and velocity errors of the robot arm are compensated using the RBF controller. According to the Lyapunov theory, the accuracy of torque control system depends on path tracking errors, inertia of robot, dynamic parameters and disturbance of each joint. Compared to general CTL approaches, the precision of a 7-DOF robot could be improved by adjusting the RBF parameters

Symmetric RBF classifier for nonlinear detection in multiple-antenna aided systems

In this paper, we propose a powerful symmetric radial basis function (RBF) classifier for nonlinear detection in the so-called “overloaded” multiple-antenna-aided communication systems. By exploiting the inherent symmetry property of the optimal Bayesian detector, the proposed symmetric RBF classifier is capable of approaching the optimal classification performance using noisy training data. The classifier construction process is robust to the choice of the RBF width and is computationally efficient. The proposed solution is capable of providing a signal-to-noise ratio (SNR) gain in excess of 8 dB against the powerful linear minimum bit error rate (BER) benchmark, when supporting four users with the aid of two receive antennas or seven users with four receive antenna elements. Index Terms—Classification, multiple-antenna system, orthogonal forward selection, radial basis function (RBF), symmetry

An Adaptive Nonlinear Control for Gyro Stabilized Platform Based on Neural Networks and Disturbance Observer

In order to improve the tracking performance of gyro stabilized platform with disturbances and uncertainties, an adaptive nonlinear control based on neural networks and reduced-order disturbance observer for disturbance compensation is developed. First the reduced-order disturbance observer estimates the disturbance directly. The error of the estimated disturbance caused by parameter variation and measurement noise is then approximated by neural networks. The phase compensation is also introduced to the proposed control law for the desired sinusoidal tracking. The stability of the proposed scheme is analyzed by the Lyapunov criterion. Experimental results show the validity of the proposed control approach

Modeling and control of hard disk drive in mobile applications

Master'sMASTER OF ENGINEERIN

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

Fault Tolerant Control of Electronic Throttles with Friction Changes

To enhance the reliability of the electronic throttle and consequently the vehicles driven by the internal combustion engines, a fault tolerant control strategy is developed in this paper. The proposed method employs a full-order terminal sliding mode control in conjunction with an adaptive radial basis function network to estimate change rate of the fault. Fault tolerant control to abrupt and incipient changes in the throttle viscous friction torque coefficient and the throttle coulomb friction torque coefficient is achieved. Whilst the throttle position is driven to track the reference signal, the post-fault dynamics are guaranteed to converge to the equilibrium point in finite time, and the control is smooth without chattering. A nonlinear Simulink model of an electronic throttle is developed with real physical parameters and is used for evaluation of the developed method. A significant change of the throttle friction torque is simulated, and the fault tolerant control system keeps system stability and tracking the reference signal in the presence of the fault

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles has accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. The design and implementation of an optimized control strategy is a complex challenge. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. Real-time strategies incorporate methods such as drive cycle prediction algorithms, parameter feedback, driving pattern recognition algorithms, etc. The goal of this work is to use a previously defined strategy which has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS) [1], which uses an equivalence factor to define the control strategy. The equivalence factor essentially defines the torque split between the electric motor and internal combustion engine. Consequently, the equivalence factor greatly affects fuel economy. An equivalence factor that is optimal (with respect to fuel economy) for a single drive cycle can be found offline – with a priori knowledge of the drive cycle. The RBF ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data are used to train the RBF ANN, each set contains characteristics from a different drive cycle. Each drive cycle is characterized by 9 parameters. For each drive cycle, the optimal equivalence factor is determined and included in the training data. The performance of the RBF ANN is evaluated against the fuel economy obtained with the optimal equivalence factor from the ECMS. For the majority of drive cycles examined, the RBF ANN implementation is shown to produce fuel economy values that are within +/- 2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF ANN is that it does not require a priori drive cycle knowledge and is able to be implemented real time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF ANN could be improved to produce better results across a greater array of driving conditions