Hiroshi Kinjo

Vibration control of load for rotary crane system using neural network with GA-based training

A neuro-controller for vibration control of load in a rotary crane system is proposed involving the rotation about the vertical axis only. As in a nonholonomic system, the vibration control method using a static continuous state feedback cannot stabilize the load swing. It is necessary to design a time-varying feedback controller or a discontinuous feedback controller. We propose a simple three-layered neural network as a controller (NC) with genetic algorithm-based (GA-based) training in order to control load swing suppression for the rotary crane system. The NC is trained by a real-coded GA, which substantially simplifies the design of the controller. It appeared that a control scheme with performance comparable to conventional methods can be obtained by a relatively simple approach.

Extending the Modeling Framework for Wind Generation Systems: RLS-Based Paradigm for Performance Under High Turbulence Inflow

Strong growth figures prove that wind is now a mainstream option for new power generation. All the successful megawatt-class wind technology developments to date are results of evolutionary design efforts based on the premise that control can significantly improve energy capture and reduce dynamic loads. The main challenge is wind stochasticity that impacts both power quality and drive train fatigue life for a wind generating system. In the proposed paradigm, control is exercised through a self-tuning regulator (STR) that incorporates a recursive least-squares algorithm to predict the process parameters and update the states. In above rated regimes, the control strategy incorporating a pitch regulatory system aims to regulate turbine power and maintain stable, closed-loop behavior in the presence of turbulent wind inflow. The control scheme is formulated based on a detailed performability model; the wind speed is generated by a stochastic model, while the drivetrain is modeled as a multiinertia system linked by a nonideal (KS ne infin) shaft described by nonlinear equations. Computer simulations reveal that achieving the two objectives of maximizing energy extraction and load reduction by the STR becomes more attractive relative to the classical PID controller design.

Load Swing Suppression in Jib Crane Systems Using a Genetic Algorithm-trained Neuro-controller

A neuro-controller for load swing suppression in a jib crane system involving only rotation about the vertical axis is proposed. The controller is trained by a genetic algorithm, substantially simplifying the design of the controller. As such a system is nonholonomic, the conventional control problem is difficult to solve, requiring knowledge of complex control theory. Using a simple three-layered neural network as a controller genetic algorithm-based training, it is demonstrated that a control scheme with performance comparable to conventional methods can be obtained by a relatively simple approach.

On the Continuous Control of the Acrobot via Computational Intelligence

The focus of this work is the continuous control of the Acrobot under limited-torque condition. By utilizing neural network (NN) and genetic algorithm (GA), a global controller is constructed in order to handle both swing-up and balancing control stages of the Acrobot without the need of different control strategies for the two processes. Based on given control timings, two different evaluation functions are introduced, one being continuous evaluation and the other multi-point based evaluation. In order to improve the system performance, an enhanced GA is proposed which recovers the diversity of population when it tends to be lost by applying an adaptive mutation operator based on a convergence index that reflects the diversity of population in GA. To verify the system performance, numerical simulations are implemented with different timing constraints. Comparisons between the proposed GA with the conventional method as well as between the two evaluation schemes are also provided. Simulation results show that the proposed GA has good performance and the neurocontrol system is able to control the Acrobot effectively by either one of the two evaluation schemes.

A switch controller design for the acrobot using neural network and genetic algorithm

This paper presents an intelligent control method for the Acrobot with applications of neural network (NN) and genetic algorithm (GA). A switch controller is proposed where a neurocontroller (NC) optimized by GA is used for the swing-up stage and a linear quadratic regulator (LQR) is applied to the balancing stage. In order to analyze the characteristics of the proposed control system, we investigate the performance of the controller with different swing-up timing constraints. Simulation results show that the method has advantages in that it can provide smooth control process and allow us to flexibly define the swing-up time in advance.

Searching performance of a real-coded genetic algorithm using biased probability distribution functions and mutation

One excellent crossover method for the real-coded genetic algorithm (RGA) is the unimodal normal distribution crossover method (UNDX). The UNDX is superior to the blend crossover method (BLX). The UNDX uses Gaussian distribution functions based on the main and sub searching lines. In this article, we present a method of improving the searching performance of the RGA. We propose the use of biased probability distribution functions (BPDFs) based on the main and sub searching lines in the crossover process. The crossover with BPDFs frequently produces offspring that are close to the best individuals in the current generation, and it is highly likely that these offspring will offer the best solution to the problem. Furthermore, we propose a mutation that has a constant and extended range that is wider than that of the UNDX. Simulations show the efficiency of the proposed method.

Force and position control of robot manipulator using neurocontroller with GA based training

In this paper, we propose a force and position controller for a robot manipulator using a neurocontroller (NC) with genetic algorithm (GA) based training. It is very difficult to design the controller which applies both force and position control to the robot manipulator. We use a simple three layered neural network as the controller, and the training method of the NC is GA based. Inputs to the NC are errors of the position and force. Furthermore, we input the integral information of the position error to the NC because it eliminates the steady-state position error. Simulation shows that the proposed NC has better performance for both position and force control than the conventional neural network, for the robot manipulator.

Particle swarm optimization with genetic recombination: a hybrid evolutionary algorithm

This article presents a hybrid evolutionary algorithm (HEA) based on particle swarm optimization (PSO) and a real-coded genetic algorithm (GA). In the HEA, PSO is used to update the solution, and a genetic recombination operator is added to produce offspring individuals based on the parents, which are selected in proportion to their relative fitness. Through the recombination, new offspring enter the population, and individuals with poor fitness are eliminated. The performance of the proposed hybrid algorithm is compared with those of the original PSO and GA, and the impact of the recombination probability on the performance of the HEA is also analyzed. Various simulations of multivariable functions and neural network optimizations are carried out, showing that the proposed approach gives a superior performance to the canonical means, as well as a good balance between exploration and exploitation.

Neurocontroller with a genetic algorithm for nonholonomic systems: flying robot and four-wheel vehicle examples

This article considers intelligent control for a class of nonholonomic systems using a neurocontroller (NC) and a genetic algorithm (GA). First, we introduce the design of the NC with use of the GA, and then we apply the NC to control two typical examples of nonholonomic systems: a hopping robot in the flight phase and a four-wheel vehicle. In order to verify the effectiveness of the control system, the performance of the NC is investigated and also compared to that of the so-called direct gradient descent control (DGDC) approach, which is able to utilize a GA with the same examples in the comparison. Simulations show that the NC could achieve a competitive performance and control the nonholonomic systems effectively. Furthermore, the use of the NN and GA provide a straightforward solution for the problem without the need of the chained form conversion.

Maximum Wind Power Capture by Sensorless Rotor Position and Wind Velocity Estimation from Flux Linkage and Sliding Observer

In recent developments wind power has been gaining rapid usage as an alternative source of electrical power and there is need to formulate optimized control schemes for power generation. This paper presents a sensorless maximum power point tracking control methodology for a wind power generation system. For the sensorless vector control a sliding mode observer is utilized in the estimation of the rotor speed while the rotor position is estimated based on the flux linkage. The Powell method is introduced to improve the efficiency of the permanent magnet synchronous generator (PMSG) d-axis current optimization. To ensure robustness of the proposed paradigm to parameter variations, the windmill loss coefficients determining the optimal rotor speed are identified online. Results of simulations confirm the effectiveness of the proposed method.