Laboratory Implementation of Direct Torque Controller based Speed Loop Pseudo Derivative Feedforward Controller for PMSM Drive

This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller(HCC).The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed,electromagnetic torque,and stator current.Two case studies,one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation,has been validated to show the effectiveness of the proposed control strategy.The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.

DERIVATION AND INTEGRAL SLIDING MODE VARIABLE STRUCTURE CONTROL OF HYDRAULIC VELOCITY TRACKING SYSTEM

The velocity tracking control of a hydraulic servo system is studied. Sincethe dynamics of the system are highly nonlinear and have large extent of model uncertainties, suchas big changes in load and parameters, a derivation and integral sliding mode variable structurecontrol scheme (DI-SVSC) is proposed. An integral controller is introduced to avoid the assumptionthat the derivative of desired signal must be known in conventional sliding mode variable structurecontrol, a nonlinear derivation controller is used to weaken the chattering of system. The designmethod of switching function in integral sliding mode control, nonlinear derivation coefficient andcontrollers of DI-SVSC is presented respectively. Simulation shows that the control approach is ofnice robustness and improves velocity tracking accuracy considerably.

PSO Based Multi-Objective Approach for Controlling PID Controller

CSTR(Continuous stirred tank reactor)is employed in process control and chemical industries to improve response characteristics and system efficiency.It has a highly nonlinear characteristic that includes complexities in its control and design.Dynamic performance is compassionate to change in system parameterswhich need more effort for planning a significant controller for CSTR.The reactor temperature changes in either direction from the defined reference value.It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature,and deviation from reference values may cause degradation of biomass quality.Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential.In this paper,a conventional Proportional Integral Derivative(PID)controller is designed.The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters.Hence,A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller’s limitation.In the proposed technique,PID parameters are tuned by Particle Swarm Optimization(PSO).It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response.In this article,a multi-objective function is proposed for PSO based controller design of CSTR.

Modeling Method and Control Strategy for Hose-Drogue Aerial Refueling System

Conventional method for hose-drogue model of aerial refueling system is known to be complex due to the flexible body of hose.And as reported,drogues are unstable in atmospheric turbulence,which greatly decreases docking success rates.This paper proposes a dynamic model for a hose-drogue aerial refueling system based on Kane equation and rigid multi-body dynamics,and analyzes its performance.Furthermore,the nonlinear dynamic model is linearized at the equilibrium point and simplified from full order to 2 nd order.Based on the simplified 2 nd order model,active control strategies,including proportion integral derivative(PID)and liner quadratic regulator(LQR)control laws,are designed to inhibit the pendulum movement of drogue due to,atmospheric turbulences.Numerical simulation results show the significant correctness of the proposed dynamic model by steady-state drag and balance position of drogue when the tanker flights under different conditions.Moreover,the steady state position error varies within 1 cm,thanks to either controller,when the drogue suffers from moderate-level atmospheric turbulences.Further,the PID controller exhibits better control effect and higher control precision than LQR controller.

Nonlinear Derivative and Integral Sliding Control for Tracked Vehicle Steering with Hydrostatic Drive

In the steering process of tracked vehicle with hydrostatic drive,the motion and resistance states of the vehicle are always of uncertain and nonlinear characteristics,and these states may undergoe large-scale changes.Therefore,it is significant to enhance the steering stability of tracked vehicle with hydrostatic drive to meet the need of future battlefield.In this paper,a sliding mode control algorithm is proposed and applied to achieve desired yaw rates.The speed controller and the yaw rate controller are designed through the kinematics and dynamics analysis.In addition,the nonlinear derivative and integral sliding mode control algorithm is designed,which is supposed to efficiently reduce the integration saturation and the disturbances from the unsmooth road surfaces through a conditional integrator approach.Moreover,it improves the response speed of the system and reduces the chattering by the derivative controller.The hydrostatic tracked vehicle module is modeled with a multi-body dynamic software RecurDyn and the steering control strategy module is modeled by MATLAB/Simulink.The co-simulation results of the whole model show that the control strategy can improve the vehicle steering response speed and also ensure a smooth control output with small chattering and strong robustness.

Parallel Fuzzy P+Fuzzy I+Fuzzy D Controller:Design and Performance Evaluation

In this paper, a parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative （FP＋FI＋FD） controller is proposed. It is derived from the conventional parallel proportional-integral-derivative （PID） controller. It preserves the linear structure of a conventional parallel PID controller, with analytical formulas. The final shape of the controller is a discrete-time fuzzy version of a conventional parallel PID controller. Computer simulations are performed to evaluate the performance of the FP＋FI＋FD controller for setpoint tracking and load-disturbance rejection for some complex processes, such as first- and second-order processes with delay, inverse response process with and without delay and higher order processes. Also, the performance of the proposed fuzzy controller is evaluated experimentally on highly nonlinear liquid-flow process with a hysteresis characteristic due to a pneumatic control valve. The simulation and real time control is done using National InstrumentTM hardware and software （LabVIEWTM）. The response of the FP＋FI＋FD controller is compared with the conventional parallel PID controller, tuned with the Ziegler-Nichols （Z-H） and /~strSm- H^gglund （A-H） tuning technique. It is observed that the FP＋FI＋FD controller performed much better than the conventional PI/PID controller. Simulation and experimental results demonstrate the effectiveness of the proposed parallel FP＋FI＋FD controller.

Fractional Order Proportional Integral Derivative Controller Design and Simulation for Bioengineering Systems

This paper deals with the study of fractional order system tuning method based on Factional Order Proportional Integral Derivative( FOPID) controller in allusion to the nonlinear characteristics and fractional order mathematical model of bioengineering systems. The main contents include the design of FOPID controller and the simulation for bioengineering systems. The simulation results show that the tuning method of fractional order system based on the FOPID controller outperforms the fractional order system based on Fractional Order Proportional Integral( FOPI) controller. As it can enhance control character and improve the robustness of the system.

Automatic measurement of air-pressure sensor based on two-pressure control instrument

To measure the performance of high precision air-pressure sensors in below normal pressure,an automatic measurement instrument has been designed and implemented.It can simulate environment of low pressure from 300hPa to 1 000hPa with high accuracy by proportional-integral-derivative(PID)control quickly,and it can also generate various relative humidity by two-pressure control.The results show that this instrument can reach controlled pressure quickly.And it works well with the minimum average pressure difference,and the fluctuation is±0.02hPa at 500hPa.And it can keep in a stable status for a long time.It works well in performance testing of pressure sensors.The structure of the system is simple,takes small investment,and can be operated conveniently.

Trajectory tracking control of the bionic joint of the musculoskeletal leg mechanism

Pneumatic artificial muscles(PAMs) have properties similar to biological muscles,which are widely used in robotics as actuators.It is difficult to achieve high-precision position control for robotics system driven by PAMs.A 3-DOF musculoskeletal bionic leg mechanism is presented,which is driven by PAMs for quadruped robots.PAM is used to simulate the compliance of biological muscle.The kinematics of the leg swing is derived,and the foot desired trajectory is planned as the sinusoidal functions.The swing experiments of the musculoskeletal leg mechanism are conducted to analyse the extension and flexion of joints.A proportional integral derivative(PID) algorithm is presented for controlling the flexion/extension of the joint.The trajectory tracking results of joints and the PAM gas pressure are obtained.Experimental results show that the developed leg mechanism exhibits good biological properties.

Research on Stick-Slip Vibration Suppression Method of Drill String Based on Machine Learning Optimization

During the drilling process,stick-slip vibration of the drill string is mainly caused by the nonlinear friction gen-erated by the contact between the drill bit and the rock.To eliminate the fatigue wear of downhole drilling tools caused by stick-slip vibrations,the Fractional-Order Proportional-Integral-Derivative(FOPID)controller is used to suppress stick-slip vibrations in the drill string.Although the FOPID controller can effectively suppress the drill string stick-slip vibration,its structure isflexible and parameter setting is complicated,so it needs to use the cor-responding machine learning algorithm for parameter optimization.Based on the principle of torsional vibration,a simplified model of multi-degree-of-freedom drill string is established and its block diagram is designed.The continuous nonlinear friction generated by cutting rock is described by the LuGre friction model.The adaptive learning strategy of genetic algorithm(GA),particle swarm optimization(PSO)and particle swarm optimization improved(IPSO)by arithmetic optimization(AOA)is used to optimize and adjust the controller parameters,and the drill string stick-slip vibration is suppressed to the greatest extent.The results show that:When slight drill string stick-slip vibration occurs,the FOPID controller optimized by machine learning algorithm has a good effect on suppressing drill string stick-slip vibration.However,the FOPID controller cannot get the drill string system which has fallen into serious stick-slip vibration(stuck pipe)out of trouble,and the machine learning algorithm is required to mark a large amount of data on adjacent Wells to train the model.Set a reasonable range of drilling parameters(weight on bit/drive torque)in advance to avoid severe stick-slip vibration(stuck pipe)in the drill string system.

Design of an Executable ANFIS-based Control System to Improve the Attitude and Altitude Performances of a Quadcopter Drone

Nowadays,quadcopters are presented in many life applications which require the performance of automatic takeoff,trajectory tracking,and automatic landing.Thus,researchers are aiming to enhance the performance of these vehicles through low-cost sensing solutions and the design of executable and robust control techniques.Due to high nonlinearities,strong couplings and under-actuation,the control design process of a quadcopter is a rather challenging task.Therefore,the main objective of this work is demonstrated through two main aspects.The first is the design of an adaptive neuro-fuzzy inference system(ANFIS)controller to develop the attitude and altitude of a quadcopter.The second is to create a systematic framework for implementing flight controllers in embedded systems.A suitable model of the quadcopter is also developed by taking into account aerodynamics effects.To show the effectiveness of the ANFIS approach,the performance of a well-trained ANFIS controller is compared to a classical proportional-derivative(PD)controller and a properly tuned fuzzy logic controller.The controllers are compared and tested under several different flight conditions including the capability to reject external disturbances.In the first stage,performance evaluation takes place in a nonlinear simulation environment.Then,the ANFIS-based controllers alongside attitude and position estimators,and precision landing algorithms are implemented for executions in a real-time autopilot.In precision landing systems,an IR-camera is used to detect an IR-beacon on the ground for precise positioning.Several flight tests of a quadcopter are conducted for results validation.Both simulations and experiments demonstrated superior results for quadcopter stability in different flight scenarios.

Control of a 3-RRR Planar Parallel Robot Using Fractional Order PID Controller

3-RRR planar parallel robots are utilized for solving precise material-handling problems in industrial automation applications.Thus,robust and stable control is required to deliver high accuracy in comparison to the state of the art.The operation of the mechanism is achieved based on three revolute(3-RRR)joints which are geometrically designed using an open-loop spatial robotic platform.The inverse kinematic model of the system is derived and analyzed by using the geometric structure with three revolute joints.The main variables in our design are the platform base positions,the geometry of the joint angles,and links of the 3-RRR planar parallel robot.These variables are calcula ted based on Cayley-Menger determinants and bilateration to det ermine the final position of the platform when moving and placing objects.Additionally,a proposed fractional order proportional integral derivative(FOPID)is optimized using the bat optimization algorithm to control the path tracking of the center of the 3-RRR planar parallel robot.The design is compared with the state of the art and simulated using the Matlab environment to validate the effectiveness of the proposed controller.Furthermore,real-time implementation has been tested to prove that the design performance is practical.

Some Investigations on Fuzzy P+Fuzzy I+Fuzzy D Controller for Non-stationary Process

The paper addresses the adaptive behaviour of parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative (FP+FI+FD) controller. The parallel FP+FI+FD controller is actually a non-linear adaptive controller whose gain changes continuously with output of the process under control. Two non-stationary processes, whose characteristics change with time, are considered for simulation study. Simulation is performed using software LabVIEW TM . The set-point tracking response of parallel FP+FI+FD is compared with conventional parallel proportional plus integral plus derivative (PID) controller, tuned with the Ziegler-Nichols (Z-N) tuning technique. Simulation results show that conventional PID controller fails to track the set-point and becomes unstable as the process changes its characteristic with time. But the parallel FP+FI+FD controller shows considerably much better set-point tracking response and does not deviate from steady state. Also, a huge spike is observed in the output of PID controller as the reference set-point and process parameters are changed, while the FP+FI+FD controller gives spike free control signal.

Application of Fractional Order PID Controller for AGC Under Deregulated Environment

In this paper, a fractional order proportional integral derivative （FOPID） controller for multiarea automatic generation control （AGC） scheme has been designed. FOPID controller has five parameters and provides two additional degrees of flexibility in comparison to a proportional integral derivative （PID） controller. The optimal values of parameters of FOPID controller have been determined using Big Bang Big Crunch （BBBC） search algorithm. The designed controller regulates real power output of generators to achieve the best dynamic response of frequency and tie-line power on a load perturbation. The complete scheme for designing of the controllers has been developed and demonstrated on multiarea deregulated power system. The performance of the designed FOPID controllers has been compared with the optimally tuned PID controllers. It is observed from the results that the FOPID controller shows a considerable improvement in the performance as compared to the conventional PID controller.

A practical filter error method for aerodynamic parameter estimation of aircraft in turbulence

It is common for aircraft to encounter atmospheric turbulence in flight tests.Turbulence is usually modeled as stochastic process noise in the flight dynamics equations.In this paper,parameter estimation of nonlinear dynamic system with both process and measurement noise was studied,and a practical filter error method was proposed.The linearized Kalman filter of first-order approximation was used for state estimation,in which the filter gain,along with the system parameters and the initial states,constituted the parameter vector to be estimated.The unknown parameters and measurement noise covariance were estimated alternately by a relaxation iteration method,and the sensitivities of observations to unknown parameters were calculated by finite difference approximation.Some practical aspects of the method application were discussed.The proposed filter error method was validated by the flight simulation data of a research aircraft.Then,the method was applied to the flight tests of a subscale aircraft,and the aerodynamic stability and control derivatives were estimated.All the estimation results were compared with the results of the output error method to demonstrate the effectiveness of the approach.It is shown that the filter error method is superior to the output error method for flight tests in atmospheric turbulence.

Stability Analysis of Parallel Fuzzy P + Fuzzy I + Fuzzy D Control Systems

The study presented in this paper is in continuation with the paper published by the authors on parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative （FP ＋ FI ＋ FD） controller. It addresses the stability analysis of parallel FP ＋ FI ＋ FD controller. The famous＂small gain theorem＂ is used to study the bounded-input and bounded-output （BIBO） stability of the fuzzy controller. Sufficient BIBO-stability conditions are developed for parallel FP ＋ FI ＋ FD controller. FP ＋ FI ＋ FD controller is derived from the conventional parallel proportional plus integral plus derivative （PID） controller. The parallel FP ＋ FI ＋ FD controller is actually a nonlinear controller with variable gains. It shows much better set-point tracking, disturbance rejection and noise suppression for nonlinear processes as compared to conventional PID controller.

Design and analysis of control system using neural network for regulated DC power supply

Conventional control systems used for regulated power supplies,including the proportional integral and derivation(PID)controller,have some serious disadvantages.The PID controller has a delayed feedback associated with the control action and requires a lot of mathematical derivations.This paper presents a novel controlling system based on the artificial neural network(ANN),which can be used to regulate the output voltage of the DC power supply.Using MATLABTM,the designed control system was tested and analyzed with two types of back-propagation algorithms.This paper presents the results of the simulation that includes sum-squared error(SSE)and mean-squared error(MSE),and gives a detailed comparison of these values for the two algorithms.Hardware verification of the new system,using RS232 interface and Microsoft Visual Basic 6.0,was implemented,showing very good consistency with the simulation results.The proposed control system,compared to PID and other conventional controllers,requires less mathematical derivation in design and it is easier to implement.

Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm

Conventional proportional integral derivative(PID)controllers are being used in the industries for control purposes.It is very simple in design and low in cost but it has less capability to minimize the low frequency noises of the systems.Therefore,in this study,a low pass filter has been introduced with the derivative input of the PID controller to minimize the noises and to improve the transient stability of the system.This paper focuses upon the stability improvement of a wind-diesel hybrid power system model(HPSM)using a static synchronous compensator(STATCOM)along with a secondary PID controller with derivative filter(PIDF).Under any load disturbances,the reactive power mismatch occurs in the HPSM that affects the system transient stability.STATCOM with PIDF controller is used to provide reactive power support and to improve stability of the HPSM.The controller parameters are also optimized by using soft computing technique for performance improvement.This paper proposes the effectiveness of symbiosis organisms search algorithm for optimization purpose.Binary coded genetic algorithm and gravitational search algorithm are used for the sake of comparison.

Study of Model-free Adaptive Data-driven SMC Algorithm

A kind of adaptive sliding model control algorithm is developed to solve and improve the mathematical model dependency and un-modeled dynamics of a controlled system. The control strategy derived from a kind of data-driven control method in essence, thereby the input and output data are utilized by the controller with no information about the control system model. Theoretical analysis proves that this proposed control algorithm can improve the utilization of the estimated pseudo partial derivative information and accelerate the velocity of the convergence. The stability of the control system is further verified by rigorous mathematical analysis. This new discrete-time nonlinear systems model-free control algorithm obtained better control performance through the simulations for the linear motor position and the information tracking speed, which also achieved robust and accurate traceability.