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This paper is concerned with the control of an underactuated three-dimensional tower crane system using a recurrent neural network (RNN) which is evolved by an evolutionary algorithm. In order to improve the performance in evolving the RNN, a hybrid evolutionary algorithm (HEA) which utilizes the operators of a constricted particle swarm optimization (PSO) and a binary-coded genetic algorithm (GA) is proposed. Simulation results show that the proposed HEA has superior performance in a comparison with the canonical algorithms and that the control system works effectively.

Active noise control has attracted much research attention due to its several advantages over passive noise control. This paper introduces two model-based noise canceling techniques, that is, using the Moving Average (MA) model and a feedforward Neural Network (NN) to estimate the signal. The Least Mean Square (LMS) algorithm is used to minimize the error in the MA model while a backpropagation algorithm is employed to optimize the NN. Due to its advantages of good robustness and nonlinear processing, the NN is considered to be suitable for nonlinear signals. In order to reduce computational cost, the backpropagation algorithm in the NN is applied once at each time step with only one iteration. To examine the methods, two real-world problems are considered, one being engine noise and the other road traffic noise. A comparison between the two methods is carried out. Results indicate that both the MA and NN processors are effective in reducing the noises and that the NN based approach is superior over the MA model, especially for low frequency band.

It is well known that the class of nonholonomic systems cannot be asymptotically stabilized by continuous static state feedback controls. It has been reported that the so-called direct gradient descent control (DGDC) is able to stabilize nonholonomic systems. This article attempts to improve the performance of the DGDC by using neural network (NN) and particle swarm optimization (PSO). A control method is proposed by embedding an NN into the control law derived by the original DGDC. PSO is employed in order to search globally the parameters of the controller without requiring a redesign process of the parameters. To verify the method, the control problems of two typical nonholonomic systems, one being a wheeled mobile robot and the other a rotary crane system, are considered under constraints applied to the systems. Comparative performance tests are carried out, showing that the proposed approach outperforms the original method and a neurocontroller. Also, simulations show that the proposed method is able to control the systems effectively under the given constraints without the need of the redesign process. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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.

This paper presents an online adaptive control method for nonlinear systems using evolutionary algorithms (EAs). A general framework of constructing an EA-based online control system that processes problems continuously is first introduced with focus on handling external disturbance adaptively. In order to verify the method, a neural network controller and genetic algorithm, which have emerged as the most popular and successful techniques in computational intelligence, are employed as an example of the method. Two nonholonomic systems are considered, one being a wheeled mobile robot and the other rotary crane system. To emphasize the advantage of the proposed approach, strong and sudden disturbances are used in these tests. Simulations show that the method works effectively to control the perturbed systems to the desired states. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Recently, computational intelligence has been applied extensively in control engineering, especially for systems that cannot easily be controlled by conventional means. In this article, attention is paid to the control of a three-DOF planar underactuated manipulator, also known as the three-link gymnastic robot, by utilizing a neural network (NN) and a genetic algorithm (GA). In an attempt to make the problem more analogous to human gymnastics, constraints are applied to the joint angles. With different swing-up timings, the performance of the proposed controller is investigated and control simulations are performed. Numerical simulations show that the neurocontroller is able to control the system effectively within the constraints and given timings.

This paper focuses on two control methods which are constructed based on neurocontroller (NC) and genetic algorithm (GA) for the Acrobot. A switching controller is first introduced where an NC opti...

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.

This paper addresses the control problem of an underactuated rotary crane system by using a recurrent neural network (RNN) and a particle filter (PF) based state estimation. The RNN is used as a state feedback controller which is designed by a constricted particle swarm optimization (PSO). As the study also considers the problem with assuming that the velocities of the system are not obtained, PF is utilized to estimate the latent states. Simulations show that the RNN could provide a superior evolutionary performance and less computational cost compared to a feed forward NN and that the PF is effective in estimating the unobserved states.

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.