Discontinuous integral control for mechanical systems

In this paper, we propose an adaptive fuzzy output feedback control method for trajectory tracking control problem for robotic systems. Using Lyapunov method, we first develop a stable adaptive fuzzy state feedback control algorithm by assuming that the systems output and its derivatives are available for feedback control design. The algorithm combines fuzzy systems with robust adaptive controller. The fuzzy system approximates the certainty equivalent (CE)-based optimal controller while robustifying adaptive control term is used to cope with uncertainties that appeared from the effect of external disturbance, fuzzy approximation errors and other modeling errors. Then, an output feedback form of the position-velocity (state feedback) controller is proposed where unknown velocity signal is replaced by the output of model-free linear estimator. We show via asymptotic analysis that the tracking performance of the output feedback design can recover the performance achieved under the state feedback control design. Finally, the proposed method is implemented and evaluated on a 2-DOF robotic system to demonstrate the theoretical development for the real-time applications.

Output feedback control of switched nonlinear systems using multiple Lyapunov functions

Permanent magnet synchronous motors (PMSM) are mainly used in high-performance and high-efficiency motor drives such as used in railways. PMSM has the performance required for traction motors and is more compact, light weighted and efficient than induction motors. This paper presents the detailed modeling of PMSM drive in Simulink environment and also presents the speed control of PMSM using vector control approach and by use of state feedback (SFB) controller. The observer is designed to eliminate the need of sensors. The observer is designed for estimating any one of the state variable if the other state variable is known thus eliminating the need to sense both speed and current. The state feedback (SFB) controller is designed such to provide zero steady state error. For observation purpose a PI controller is also designed and the results of control actions have been compared and observed that SFB controller are more robust than PI controllers bpth in case of normal and over loading. In this paper, we have designed and simulated the mathematical model of PM synchronous motor and designed the state feedback control system using pole placement method and PI controller using bode plot method, for speed control of PMSM. The controller hence designed is found to be satisfactory and robust over wide range of operating condition and parameter variations.

This paper proposes feedback control algorithms suitable for the top level of a hierarchical approach to controlling Flexible Manufacturing Systems (FMS). Preview and state feedback control techniques are employed to regulate the inventory levels near zero. The resulting controller utilizes inventory state feedback and anticipatory demand feedforward control, referred to as preview control. The preview control takes on the form of demand filtering. Special dynamic properties of the demand filter are discussed. We show how future demand information is related to present production and inventory levels. Production constraints are explicitly handled in the development of the feedback controller. We present numerical results for a test case.

“SOME APPLICATIONS OF STATE ESTIMATION AND CONTROL THEORY TO DISTRIBUTED PARAMETER SYSTEMS”

This paper is concerned with the drive-response synchronization for a class of fractional-order bidirectional associative memory neural networks with time delays, as well as in the presence of discontinuous activation functions. The global existence of solution under the framework of Filippov for such networks is firstly obtained based on the fixed-point theorem for condensing map. Then the state feedback and impulsive controllers are, respectively, designed to ensure the Mittag-Leffler synchronization of these neural networks and two new synchronization criteria are obtained, which are expressed in terms of a fractional comparison principle and Razumikhin techniques. Numerical simulations are presented to validate the proposed methodologies.

In this paper, a new iterative learning algorithm is proposed for repetitive nonlinear systems. The control system employs a combination of state feedback and iterative learning control (ILC) in which the coefficients of states are learned similar to ILC methods. The control system is in a closed loop format both in iteration domain (because of ILC) and in time domain (because of feedback control) which improves the robustness of the conventional ILC. The convergence of the control algorithm is also proved. Finally, simulation results for a 5-DOF manipulator have been presented to illustrate that the proposed algorithm is more robust than a first order P type ILC method at the presence of white Gaussian noise as a nonrepeating disturbance.

To achieve proper operation of an online static uninterruptible power supply (UPS) system, feedforward and feedback (FF/FB) controls are introduced. Feedforward control is applied to reduce the low order harmonics in the output voltage due to the ripples included in the input DC voltage to the UPS inverter resulting from the controlled rectifier, while feedback control is applied mainly for stabilizing the output voltage. In this paper, a feedforward technique is selected that was found to give free ripple output, and two types of feedback techiques are compared and the most suitable one is considered. The paper gives the modeling and simulation results of the whole system, that compare the performance without and with FF/FB under different steady state and transient conditions.

This paper describes a novel approach to operate a pseudo direct drive (PDD) permanent-magnet machine with a single sensor on the low-speed rotor (LSR). Due to the magnetic coupling between its two rotors, the PDD machine exhibits torsional oscillation when conventional control techniques are employed. While full state feedback (SFBK) control can effectively suppress torsional oscillation, the need for a position sensor mounted on the high-speed rotor (HSR) for electronic commutation imposes great constraints on the PDD mechanical design. A novel technique has been developed in order to control the PDD machine using a single sensor mounted on the LSR, whereas the position and speed of the HSR required for electronic commutation and SFBK control are estimated with an observer. Both the SFBK controller and observer are optimally designed using the genetic algorithm against a defined set of criteria and constraints. The proposed control technique has been validated by experiments on a prototype PDD system.

This paper is concerned with the output feedback control problem for a class of nonlinear system with time delay. First, a state feedback controller is design and a sufficient condition with exponential stability is obtained. Second, we study the observer design problem. And we proved that the observer designed in this paper ensure the observation error exponential converge to zero. Furthermore, output feedback controller using state feedback control and observer design is studied.

This paper proposes an integral fuzzy control strategy to solve output regulation problem for the micro unmanned helicopter. In order to eliminate the system's bias and guarantee the zero-offset output regulation performance, we firstly take coordinate translation at equilibrium point and introduce an added integral state of output regulation error, and then the resulting augmented system is represented into a Takagi-Sugeno fuzzy model. Next, utilizing parallel distributed compensation (PDC) technique and direct Lyapunov method, an integral state feedback control law for output regulation is established by solving a set of linear matrix inequalities (LMIs). Moreover, the proposed controller design has following merits: i) capability to dealing with nonlinear affine system; ii) giving the exponential stability in the presence of model uncertainty; iii) possessing a piecewise constant output regulation performance. Finally, a micro unmanned helicopter dynamic model is presented to demonstrate the validity of the proposed integral fuzzy controller.

Presents techniques for controlling the motion of an underactuated vehicle when disturbances are present and only imperfect state measurements are available for feedback. A state feedback controller is developed and then it is converted to an imperfect state measurement feedback controller. The state feedback tracking control law uses an H/sup /spl infin//-optimal design and produces a locally exponentially stable closed-loop system. The imperfect state measurement feedback controller combines the state feedback control law with an H/sup /spl infin//-filter to estimate the states and achieves a modified form of disturbance attenuation. The state estimator exploits a unique structure in the nonlinear equations of motion to develop a direct solution. MATLAB simulations illustrate both control algorithms for an underactuated ship model.

Stability analysis of lateral dynamics of a vehicle model utilizing state feedback with integral control and bifurcation method

In this work, we develop a method for dynamic output feedback covariance control of the state covariance of linear dissipative stochastic partial differential equations (PDEs) using spatially distributed control actuation and sensing with noise. Such stochastic PDEs arise naturally in the modeling of surface height profile evolution in thin film growth and sputtering processes. We begin with the formulation of the stochastic PDE into a system of infinite stochastic ordinary differential equations (ODEs) by using modal decomposition. A finite-dimensional approximation is then obtained to capture the dominant mode contribution to the surface roughness profile (i.e., the covariance of the surface height profile). Subsequently, a state feedback controller and a Kalman-Bucy filter are designed on the basis of the finite-dimensional approximation. The dynamic output feedback covariance controller is subsequently obtained by combining the state feedback controller and the state estimator. The steady-state expected surface covariance under the dynamic output feedback controller is then estimated on the basis of the closed-loop finite-dimensional system. An analysis is performed to obtain a theoretical estimate of the expected surface covariance of the closed-loop infinite-dimensional system. Applications of the linear dynamic output feedback controller to the linearized stochastic Kuramoto-Sivashinsky equation are presented.

We propose an algorithm for nonlinear receding-horizon output feedback control by combining a real-time nonlinear optimal state feedback control algorithm and a real-time nonlinear optimal state estimation algorithm. The algorithms of state feedback control and state estimation are obtained by minimizing receding-horizon performance indexes. Application of the stabilized continuation method results in algorithms that do not involve any successive approximation methods. Simulation results of a 2-wheeled car clarify characteristics of the output feedback control algorithm.