A global sliding mode controller for missile electromechanical actuator servo system

TOWARD THE DESIGN AND IMPLEMENTATION OF A GLOBAL SLIDING-MODE CONTROL SCHEME WITH INPUT CONSTRAINT

A novel global sliding-mode control (GSMC) scheme with adjustable robustness is presented in this article. The proposed scheme offers a switching function together with unperturbed system dynamics to weigh the contribution from SMC such that all of the closed-loop poles can be located within predefined regions to provide design flexibility, and the robustness of system can thus be adjusted. By this scheme, the maximal control effort and chattering level can be reduced according to designer's specifications directly. Since the switching function can initially be made to equal to zero, the adjustable performance during the entire response can be guaranteed, and the reaching condition is thus lifted. The efficacy of this scheme is demonstrated via successful implementation on a linear variable reluctance motor (LVRM) servo system. Both simulation and experimental studies further demonstrate its feasibility and effectiveness.

The authors present a genetic approach to the optimal linear quadratic regulator (LQR) problem. Design of the optimal LQR depends on choosing weighting matrices. One must carry out a trial-and-error process to choose the proper weighting matrices in general. It is not a trivial problem. They show a new possibility using a genetic algorithm (GA) to find the proper weighting matrices and hence to obtain the optimal control gain. They apply the approach to the attitude control of an inverted pendulum system, which is known as the LQR problem. They perform computer simulations on the system with a 50 cm long inverted pendulum and obtain good results.

In this paper, an optimal PID (proportional-integral-derivative) controller is designed for controlling the non-minimum phase system with time delay and performed greater than the conventional control approaches. When there is an increment in time delay then complications related to NMP system determines the undershoot response due to presence of zeros in the right half plane. Furthermore, Ziegler-Nichols, Internal Model Control (IMC) and Chien-Hrones & Reswick tuning approaches are developed for the conventional PID controller. The time-domain characteristics obtained are unable to persuade the requirements of the system. In order to improve the performance criteria, Smith’s predictor is used for controlling the NMP system. Smith’s predictor is better than the conventional PID controller techniques for tuning the parameters but has the incapability to achieve all the design requirements. For improving the performance requirements, the optimal PID controller is designed with the employment of Linear Quadratic Regulator (LQR). The tuning parameters of PID controller are optimal values using popular technique that is Linear Quadratic Regulator (LQR). In order to minimize integral time absolute error (ITAE) and integral time square error (ITSE), the tuning parameters are optimal and give precise values of rising time, peak time, % overshoot and settling time. The simulation results are obtained and according to that LQR gives better results than Smith Predictor and other conventional PID controller techniques.KeywordsNon-minimum phase systemPID controllerSmith predictorConventional tuning methodsLinear quadratic regulator

The present contribution presents a comparison between two types of controls, namely, the optimal linear quadratic regulator (LQR) and the Kalman-LQG controller using the model order reduction process. Due to numerical constraints, the models of structures have been reduced so that the design of controllers and/or estimators could be performed. The proposed method results in a significant reduction in computational costs for dynamic analysis without compromising on accuracy. Transforming the full order state-space resulting from finite element space to a lower model reduces the simulation time with a few degrees of freedom and helps to implement easily the control without changes in the dynamics of the structure. The estimator Kalman is used here in order to estimate the modal states of the system that are used in modal analysis. In this context, a one-side cantilever Timoshenko beam is chosen with perfectly bonded piezoelectric layers of actuators and sensors to apply this comparison. The Monte Carlo simulation was used to improve the number and location selection of piezoelectric sensors on the chosen beam model. Neglecting environmental effects, numerical results relating to this comparison without and with model order reduction are established. Simulation results are presented to illustrate the effectiveness of the proposed vibration control algorithm for the studied beam.

It is well known that the dynamics of the chaotic system is very sensitive to the initial conditions of the state, and the synchronization of two identical chaotic systems is only obtained, in general, with the high gain control law once their initial conditions are in a certain large deviation. Furthermore, the initial conditions are commonly unknown in practice, which causes difficulty in synchronizing two chaotic systems. This paper deals with the synchronization of two unified chaotic systems with input constraint. First, the scalar sign function is utilized to approximate the constrained non-smooth input function so that a continuous smooth nonlinear input function and an approximated nonlinear synchronized error system are obtained. Then, an optimal linear quadratic regulator (LQR) continuous-time control law is designed based on the optimal linear model, which is constructed at the sampled operating point of the afore-mentioned approximated nonlinear synchronized error system. To reduce the high magnitude of the obtained control law, the continuous-time control law is digitally redesigned for the implementation and an iterative procedure is proposed to adjust the weighting matrices in the LQR performance index so as to avoid input saturation occurs. Finally, three illustrative examples of the Lorenz, the Chen and the L chaotic systems decomposed from the unified chaotic system are given to demonstrate the effectiveness of the proposed method.

The modeling of electric power system on the state space with single, two and three area with integral and optimal control system known as Linear Quadratic Regulator(LQR) for designing the load frequency control system are realized in this paper. The fluctuations on voltage and frequency must be reduced to minimum level against load changes for obtaining high quality electric energy on electric power system. In this paper this is achieved by designing the integral controller and LQR .By using state-space analysis a state equation for three area load frequency is obtained. The proposed optimal LQR load frequency has been compared with integral control Simulink using MATLAB.

ABSTRACTDue to the challenges in determining the parameters of traditional controllers, achieving efficient performance during the control process remains difficult. To improve control performance, this paper presents a novel speed control scheme based on the parameter tuning of a linear quadratic regulator (LQR). The proposed control approach begins by establishing the speed state error in the rotor reference coordinate system, followed by the design of a custom parameter switching surface and control law. LQR theory is then utilized to adjust the state‐space equations, allowing for the calculation of the parameters for the tracking error function and control law. This controller incorporates both the speed tracking error and the integral of the speed tracking error. Simulations and experiments are designed to validate the effectiveness of the proposed control scheme. Comparison results with traditional control methods, such as PI control based on motor parameters, sliding mode control, and PI control with state feedback, indicate that the proposed LQR parameter tuning control scheme excels in the speed control of PMSM.

The stability properties of the linear quadratic deterministic optimal regulator are investigated in the discrete-time case. Guaranteed phase- and gain- margins are obtained for the regulator in terms of the singular values of the matrices of the regulator state-space description. These margins are compared to their continuous-time counterparts and their implication on the regulator robustness is discussed.

This paper considers the linear quadratic regulator (LQR) problem for two-dimensional (2D) systems. The general 2D system is assumed to be represented by the Givone-Roesser model. In attempting to solve the standard 2D LQR problem, it is shown that the notion of diagonal causality arises naturally. The system under consideration is redefined accordingly and it is demonstrated that the new 2D system can be cast as a one-dimensional (1D) non-square descriptor system. Existing results in 1D non-square descriptor system theory are then called upon to solve the modified 2D LQR problem.

High-speed motors working in harsh environments such as high temperatures, radiation, and poisonous substances are limited because of motor breakdown due to vibrations of mechanical bearings. The concept of magnetic bearings is an alternative to the issues of slide or ball bearings because of its advantages of friction-free, negligible thermal problems, and lubrication free. Rotor vibration control during start-up, acceleration and deceleration phases are one of the key problems besides stable levitation, in high-speed applications of bearingless switched reluctance motor (BSRM). The use of linear control strategies alone is not effective in suppressing vibration due to residual unbalance and external disturbances. In this paper, a global sliding mode controller (GSMC) is proposed to control the speed and position of BSRM and also sensorless operation with sliding mode observer. In this method, rotor displacement tracking error functions are used in the sliding mode switching functions and the new sliding mode displacement control and speed tracking equations are obtained with an extra exponential fast decaying nonlinear function along with conventional linear sliding mode switching surface. Simulation is carried on 12/14 BSRM with the proposed controller and observer and the results of rotor displacements and speed are presented. In addition to the improvement of performance characteristics when compared to sliding mode controller, GSMC cancels the reaching mode, reduce the chatting, and overcome the disturbance and the time delay.

An adaptive method with weight matrix as a function of the state to design the rotatory flexible system control law

Almost the half of mechanical energy of a human gait cycle is generated by a hip joint. Therefore, this paper discusses a rehabilitation hip exoskeleton, starting with the modeling, simulation and ending with controlling its hip joints. A MATLAB based simulation environment with the use of Simscape Multibody toolbox was utilized to design and control the robotic hip exoskeleton. More details of adding a series elastic actuators (SEA) to the hip joints with the mathematical model are presented. However, the linearised mathematical model of the entire exoskeleton was found in simulation software which establish the basic need of controlling hip joints. The trajectory tracking is commonly used of controlling rehabilitation exoskeleton and to ensure a safe and reliable motion tracking methods, two desired torque signals were tested and analysed with the optimal linear quadratic regulator (LQR). The experiments with two torque signals–representing the sit-to-stand (STS) and the walking activity, demonstrated good performance of the motion gait tracking based on torque signals of a healthy person, which is carried out in the simulation environment. Furthermore, some studies in a human robot interaction are also mentioned in this paper.

This paper is on the design of an adaptive sending rate for frequency regulation of a smart microgrid. The frequency controller itself is designed using an optimal LQR (Linear Quadratic Regulator) approach whose parameters are obtained using Particle Swarm Optimization (PSO). The utilized isolated smart microgrid comprises different Distributed Generation (DG) units and Renewable Energy Sources (RESs). Implementation of the optimal LQR controller requires a communication network. The key issue in this Networked Control System (NCS) is the transmission of measured data from the multiple sensing agents to the controller. Usually the data is sent in constant intervals which raises energy issues and channel capacity and requires bigger sending rate that will cause more error in the frequency response. The paper proposes different adaptive approaches for determining an adequate sending rate. Simulation results show the effectiveness of the proposed approaches as compared to conventional methods.

In this paper, a cooperative multi-agent system (MAS) is presented for active and reactive power sharing in microgrids through a coupling of distributed control and communication techniques. Due to the natural intermittency of wind and solar, and limited capacity of battery storages, it is essential to manage proper power sharing to ensure the supply-demand balance. In order to optimally utilize of battery storage capacity, this paper emphasizes the power regulation from power electronic inverter interfaced battery storage units. A benchmark low voltage (LV) microgrid is considered and the MAS framework is developed through the well-known graph theory to enable a communication link among the controllable battery inverters where each agent is considered to be embedded to the battery inverter control unit. An optimal linear quadratic regulator (LQR) is designed within a distributed envelop to regulate the power injections into the system from the battery inverters. The consensus of intelligent agents is achieved through the information exchange among the controllable battery units.