Eiho Uezato

Centralized Design of Decentralized Stabilizing Controllers for Large-Scale Descriptor Systems

Abstract This paper considers a decentralized stabilization problem for large-scale linear descriptor systems composed of a number of interconnected subsystems. The information structure constraint is compatible with the subsystems. The decentralized controller design is carried out in a centralized way. The design problem is reduced to feasibility of a bilinear matrix inequality (BMI). To solve the BMI, the idea of the homotopy method is applied, where the interconnections between subsystems are increased gradually from zeros to the given magnitudes. The case where polytopic perturbations exist in the interconnections is also dealt with

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.

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.

Swing-up control of a 3-DOF acrobot using an evolutionary approach

We present a control method for a 3-DOF acrobot which is a model of a gymnast on a horizontal bar with three links, two active joints, and a passive joint. This robot is a nonholonomic and underactuated system. We propose two control methods for the 3-DOF acrobot. First, swing-up control is performed by genetic programming (GP), and stabilizing control is handled by a linear quadratic regulator (LQR). GP can search widely for the optimum input torques for swing-up so that the acrobot is able to reach a near balancing point. The LQR is then switched on to stabilize the system. In the simulation results, the 3-DOF acrobot could swing up to the desired position, and the proposed method could control the acrobot effectively.

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.

Stabilization of linear time-varying descriptor systems: A linear matrix differential inequality approach

This paper considers stabilization of linear time-varying descriptor systems with continuous and bounded coefficient matrices. First, a necessary and sufficient condition for exponential stability is presented as solvability of a linear matrix differential inequality. Then, the stability condition is utilized to derive a feedback control law for stabilization of the descriptor system. The proposed feedback gain extracts the dynamic component of the descriptor variable. A numerical example is presented.

Adaptive Genetic Algorithm Observer and its Application to a Trailer Truck Control System

In this paper, we present an adaptive observer for nonlinear systems using a genetic algorithm (GA). It is considered that the GA has a superior performance for solution searching of multivariable systems. We utilize the GA searching ability to determine unobtainable variables in the control system. We extended the role of the GA observer to that of a parameter estimator for unknown systems and constructed an adaptive GA observer. The adaptive GA observer has a searching ability to estimate unobtainable variables and parameters simultaneously. In this paper, we use the estimated values of the system variables and parameters to design a neurocontroller (NC) using a GA. We applied the adaptive GA observer and NC design method to the backward movement control of a trailer truck. A simulation shows the effectiveness of the proposed method

State-space H ∞ controller design for descriptor systems

This paper proposes a new linear matrix inequality (LMI) method to design state-space H ∞ controllers for linear time-invariant descriptor systems. Unlike preceding studies, where descriptor-type controllers are first computed and then numerically transformed to state-space controllers, the proposed method carries out the transformation analytically in the parameter domain. We derive a necessary and sufficient LMI condition for the existence of a state-space controller with the same dynamic order of the descriptor system to be controlled, which makes the closed-loop system regular, impulse-free, stable, and guarantees the H ∞ norm bound imposed on the closed-loop transfer function. Furthermore, we present parameterization of all such state-space controllers by variables satisfying the LMI condition and an arbitrary nonsingular matrix. The LMIs utilized in this paper are strict ones, that is, those containing no equality, while LMIs with equality constraints have been extensively used in the analysis and design for descriptor systems. The strict LMIs play key roles in deriving the results of this paper.

Particle Swarm Optimization of a Recurrent Neural Network Control for an Underactuated Rotary Crane with Particle Filter Based State Estimation

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.