Hassan M. Emara

Adaptive Fuzzy Control of the Inverted Pendulum Problem

In this brief, we address adaptive fuzzy control of the inverted-pendulum on a cart problem as an underactuated mechanical system. Many of the schemes presented in the adaptive fuzzy control literature tackle the problem as a second-order system based on feedback linearization. Such schemes render unstable zero dynamics for the cart-pole systems which hinders experimental implementation. The paradigm presented is also based on a feedback linearizing (FBL) scheme, yet it ensures system stabilization. A damping term and an adaptive fuzzy control term are added to guarantee asymptotic stability and to account for disturbances. Experimental results illustrate the success of the proposed controller in stabilization and cart-position tracking of a reference trajectory

Using Ant Colony Optimization algorithm for solving project management problems

Network analysis provides an effective practical system for planning and controlling large projects in construction and many other fields. Ant Colony System is a recent approach used for solving path minimization problems. This paper presents the use of Ant Colony Optimization (ACO) system for solving and calculating both deterministic and probabilistic CPM/PERT networks. The proposed method is investigated for a selected case study in construction management. The results demonstrate that - compared to conventional methods - ACO can produce good optimal and suboptimal solutions.

LMI based design of constrained fuzzy predictive control

Predictive control of nonlinear systems subject to output and input constraints is considered. A fuzzy model is used to predict the future behavior. Two new ideas are proposed here. First, an added constraint on the applied control action is used to ensure the decrease of a quadratic Lyapunov function, and so guarantee Lyapunov exponential stability of the closed-loop system. Second, the feasibility of the finite-horizon optimization problem with the added constraints is ensured based on an off-line solution of a set of LMIs. The novel stability method is compared to the existing methods, such as the techniques based on the end-point constraints (terminal constraint set), and the robust stability techniques based on the small gain theory. The proposed method ensures Lyapunov exponential stability, does not need an auxiliary controller and can be used with any feasible controller parameters. Illustrative examples including the predictive control of a highly nonlinear chemical reactor (CSTR) are discussed.

Parameter identification of induction motor using modified Particle Swarm Optimization algorithm

This paper presents a new technique for induction motor parameter identification. The proposed technique is based on a simple startup test using a standard V/F inverter. The recorded startup currents are compared to that obtained by simulation of an induction motor model. A Modified PSO optimization is used to find out the best model parameter that minimizes the sum square error between the measured and the simulated currents. The performance of the modified PSO is compared with other optimization methods including line search, conventional PSO and genetic algorithms. Simulation results demonstrate the ability of the proposed technique to capture the true values of the machine parameters and the superiority of the results obtained using the modified PSO over other optimization techniques.

Robust robot control enhanced by a hierarchical adaptive fuzzy algorithm

Abstract Robust control of robots including motor dynamics is considered based on a dynamic game approach. The control law is composed of three main components. The first component utilizes feedback linearization to cancel the systems nominal nonlinearity and achieve tracking via pole-placement. The discrepancy between the nominal functions used in feedback linearization and the true ones is compensated for using the second control component that is based on an adaptive hierarchical fuzzy algorithm. The third control component ensures robustness by attenuating the worst-case effect of both the residue due to fuzzy cancellation and the external disturbances. The main contribution of this work is the derivation of the hierarchical adaptive fuzzy algorithm to avoid the rule explosion phenomenon that characterizes traditional fuzzy systems. We also prove that the proposed control algorithm is locally stable. Simulation of a two-link manipulator illustrates a successful performance of our controller and investigates the effect of the design parameters.

Stator fault estimation in induction motors using particle swarm optimization

The use of induction motors is extensive in industry. The working conditions of these motors make them subject to many faults. These faults must be detected in an early stage before they lead to catastrophic failures. This paper presents a scheme for detecting inter-turn faults in the stator windings of induction motors and estimating the fault severity. Detection of incipient inter-turn faults prevents further insulation failure. The proposed algorithm monitors the spectral content of stator currents to detect the fault. After the fault is detected and identified, a particle swarm approach is used to estimate the fault severity. The swarm estimator update is based on the error between the measured data and a complete model of the faulty motor. An experimental setup is used to validate the developed scheme and to implement an online fault detector.

Robust output feedback power system stabilizer design: an LMI approach

Design of output feedback power system stabilizers (PSSs) that guarantee robust pole clustering and robust performance for a wide range of loading conditions is described in this paper. The objectives considered are clustering the closed loop poles in a prescribed region in the s-plane while minimizing an Hinfin performance criterion for the uncertain system. The main difficulty in PSS design is that power systems encounter continuous variations in the load patterns and consequently the nominal model design does not guarantee satisfactory performance at other operating conditions. To cope with such variations, a systematic approach is proposed to present the plant uncertainty in the form of a polytopic model. Based on this model, the synthesis of output feedback PSS leads to a bilinear matrix inequality (BMI) optimization problem. A fast LMI-based procedure for computing an initially feasible controller is presented and an iterative LMI algorithm is suggested to solve the BMI optimization problem. Simulation results on a single machine infinite-bus nonlinear model illustrate the validity of the proposed design procedure.

Particle Swarm Optimized Direct Torque Control of Induction Motors

The flux and torque hysteresis bands are the only adjustable parameters in direct torque control (DTC) of induction motors. Their selection greatly influences the inverter switching loss, motor harmonic loss and motor torque ripples, which are major performance criteria. In this paper, the effects of flux and torque hysteresis bands on these criteria are investigated and optimized via the minimization, by the particle swarms optimization (PSO) technique, of a suitably selected cost function. A DTC control strategy with variable hysteresis bands, which improves the drive performance compared to the classical DTC, is proposed. Online operating artificial neural networks (ANNs) use the offline optimum values obtained by PSO, to modify the hysteresis bands in order to improve the performance. The implementation of the proposed scheme is illustrated by simulation results

Observer based fuzzy-control design using relaxed LMI conditions

This paper proposes two different approaches to design observer based regulators for T-S fuzzy models. The first approach has a discrete-time state feedback counterpart in the literature. Here, we derive appropriate LMI conditions for the continuous-time case. Furthermore, new LMI conditions are derived to facilitate the design of an observer- based regulator. The second approach generalizes the first one by using a new parallel distributed compensator with a modified quadratic Lyapunov function. By including constraints on the input signal, it is shown that the second approach can cope with tighter bounds on the control signal. A typical simulation example is included to illustrate the performance of both design approaches.

Power stabilization of nuclear research reactor via fuzzy controllers

Power stabilization is a critical issue in nuclear reactors. A conventional PD controller is currently used in Egypts second testing research reactor (ETRR-2). This controller is designed neglecting the nonlinear nature of the plant. In this paper, two fuzzy controllers are used instead of the conventional PD. The first fuzzy controller has a static rule set, while the second one has an adaptive rule set. Simulation results show that the adaptive fuzzy controller gives the best integral square error (ISE) index, while the static fuzzy controller still provides a better ISE than the conventional PD controller.