Comparison of LQG and Adaptive PID Controller for USV Heading Control

Combined iterative learning and delta-operator adaptive linear quadratic Gaussian control of a commercial rapid thermal processing system

Solving the path-tracking problem of vehicles is a key part of realizing intelligent transportation. The four-wheel- independent-steering (4WIS) electric vehicle (EV) with steer-by-wire (SBW) has the advantages of good controllability, low speed maneuverability and high speed stability, which is beneficial to improving the path tracking performance. In this paper, a novel 4WIS electric vehicle is proposed and the path tracking control research was carried out on it by using the adaptive fuzzy PID algorithm. Firstly, the four-wheel steering strategy was analyzed, and the geometric relationship between the front and rear steering angle when the vehicle's centroid angle is zero was derived. Furthermore, based on the principle of adaptive fuzzy PID control and the single-step heading prediction algorithm, a path-tracking controller was designed for the self-built 4WIS EV. A Simulink/Carsim co-simulation platform was built, on which the simulation was carried out at different condition, and the simulation results show that the designed adaptive fuzzy PID controller has good path-tracking performance and strong robustness. Finally, the serpentine trajectory and the double lane change (DLC) trajectory are taken as the reference trajectory, and the reliability and robustness of the designed adaptive fuzzy PID control algorithm were verified on the open road. The results show that the adaptive fuzzy PID control algorithm has good tracking performance and is far superior to pure PID control.

Abstract- This paper introduces a linear structure of wind turbine, operator and the pitch angle controller. Then, a new method of adaptive fuzzy on the variable speed wind turbines was provided and it was compared with PID and Fuzzy Logic Control ler and the simulation results were analyzed. So when the results of each simulation using PID and Fuzzy controllers and adaptive fuzzy PID controllers provided, the observed advantages and disadvantages of the rotor speed and wind turbine power can be introduced. It can be seen that in PID method the high overshoot is discussed as a disadvantage while it is overcome by using fuzzy controller and overshoot will decrease. The time of reaching to a sustained speed increases slightly and in adaptive fuzzy PID controller, the less overshoot has provided a good and effective performance for system response. The use of adaptive fuzzy PID controller causes the system does not have any steady-state error and at all moments of time, the response rate is better than PID and fuzzy controller. The importance of the amount of overshoot and rate fluctuations is that by reducing these parameters in addition to reducing the cost of preventive care, maintenance and depreciation, the fluctuations in electricity generated by induction generator also greatly reduced.

Based on the PID control and adaptive fuzzy PID control, the paper establishes semi-active suspension model of engineering vehicle and compares vibration control and suspension performance among the semi-active suspensions, taking engineering vehicle as the study object. Through the further simulation research, from the comparative analysis performance indexes of riding comfort and handling safety, we can reach the conclusion that the control effect of adaptive fuzzy PID control improves by different degrees than the passive suspension and PID control suspension. The results show that adaptive fuzzy PID control suspension can reduce the vehicle vibration commendably during operation

This paper investigates intelligent PID flight control approaches for a small Unmanned Aerial Vehicle (UAV) pitch attitude. These methods are based on the longitudinal motion models of the small UAV. These intelligent PID controllers, including integral separated PID controller and adaptive proportion PID controller are designed and simulated for UAV’s pitch attitude control. In order to illuminate the performance of intelligent controller, the classic PID controller is designed and simulated being set to the opposite place. Our results demonstrate that the control result of the adaptive proportion PID controller is highly accepted in both dynamic and stability performance, for the controller’s ability to set a larger proportion control parameter of PID and adjust the parameter according to control process. The work described in this paper provides proposals for the design of Flight Control System of the small UAV.

In general, an adaptive fuzzy PID controller (AF-PIDC) means that the PID parameters are adjusted by fuzzy rules in real-time. In this paper, the controller is functionally divided into three operating units which are the proportional operating unit, the integral operating unit, and the derivative operating unit. The inputs of the controller are composes of two kinds of signals. The main input signal is the weighted system error and the weights are the traditional PID parameters with fixed gains tuned in a traditional way. The auxiliary input signal is produced by a fuzzy logic controller. Since the output of the fuzzy logic controller is changeable, the gain of the controller is adjusted implicitly and adaptively. The simulation results show the validity of the proposed approach.

An optimal adaptive robust PID controller subject to fuzzy rules and sliding modes for MIMO uncertain chaotic systems

In this paper, we propose a self-balancing mobile robot system and further design a control method to improve the resistance to external impact and the adaptability to continuous loads when the robot is at a fixed point. Firstly, the self-balancing robot we put forward in this paper is compared with the classic two-wheeled self-balancing robot to show its structural advantages. Secondly, the mechanical design part and the electrical design part of the robot are introduced, and the system is mathematically modeled and analyzed. Finally, an adaptive double-fuzzy anti-integral saturation PID control is developed and compared with the dual-loop PID control on the proposed robot. Experiments show that when the robot stops at a fixed position, adaptive double-fuzzy anti-integral saturation PID control could effectively improves the impact resistance and adaptability to continuous loads.

ABSTRACTAn autonomous power generation system (APGS) contains units such as diesel energy generator, solar photovoltaic units, wind turbine generator and fuel cells along with energy-storing units such as the flywheel energy storage system and battery energy storage system. The components either run at lower/higher power output or may turn on/off at different instants of their operation. Due to this, the conventional controllers will not provide desired performance under varied load conditions. This paper proposes an adaptive fuzzy logic PID (AFPID) controller for load frequency control. In order to achieve an improved performance, a modified whale optimization algorithm (mWOA) was also proposed in this paper for tuning of the AFPID parameters. The proposed algorithm was first evaluated using standard test functions and compared with other recent algorithms to authenticate the competence of algorithm. The proposed mWOA algorithm outperforms PSO, GSA, DE and FEP algorithms in five out of seven unimodal test functions and four out of six multimodal test functions. The effectiveness of the AFPID compared with the conventional PID and the proposed AFPID provides better performance. Reduction of 39.13% in error criteria (objective function) compared with WOA-PID controller. The proposed approach was also compared with some recently proposed frequency control approaches in a widely used two-area test system.

A novel adaptive fuzzy proportional-integral-derivative (PID) controller based on the proposed piecewise PID controller is put forward for the sandglass-type floating, drilling, production, storage and offloading unit (FDPSO). First, on the premise of appropriate positioning accuracy, a piecewise linear PID controller considering the directional characteristic between the motions and environmental loads is proposed to conserve energy of traditional linear PID controller. Then, to improve the stability during the transient process and alleviate oscillation induced by piecewise PID controller, the robust adaptive fuzzy PID controller based on the piecewise PID controller is put forward, whose stability is verified by fuzzy Lyapunov method. The proposed fuzzy output variables are designed including motions in both the horizontal and vertical planes to solve the surge–pitch coupling problem and optimize energy consumption. The time-domain simulations with a complete model of all DP components and external forces are carried out. Therein, the thruster model is improved by introducing a virtual thrust command to decrease the obvious phase lag, response delay and amplitude reduction of the traditional thruster model. In addition, the application of wave filter is discussed, especially when the wave-frequency (WF) motions are more prominent and significant than low-frequency (LF) motions. Finally, comparisons between the proposed adaptive fuzzy PID controller and traditional linear PID controller are made and analyzed. Good control effects of the proposed controllers are demonstrated with the assistance of numerical simulations with a series of control parameters.

In this paper, an adaptive single neuron-based PID controller for the Triaxial and Torsional Shear Apparatus is proposed. Considering variations of the control precision, the single neuron adaptive PID controller is used to construct the control system to achieve the adaptive control of triaxial and torsion testing. The single neuron adaptive PID controller using hybrid Supervised-Hebb rule is proposed to tune control parameters. The testing results of actual application show that the single neuron adaptive PID controller makes better improvement in the control precision and robustness of control system.

An adaptive failure compensation control scheme is developed based on linear quadratic (LQ) control for discrete-time possibly nonminimum phase systems with unknown actuator failures with characterizations that some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants. The failure compensation control problem is formulated as a new LQ control problem. Based on some results on new controller structures and design conditions developed in [9], a stable adaptive law is proposed for the estimation of uncertain parameters from both the plant and actuator-failures. The adaptive failure compensation controller is implemented with adaptive parameter estimates, for achieving closed-loop stability and output regulation. Simulation results are presented to verify the desired adaptive control performance in the presence of uncertain actuator failures.

In this paper, the systematic model reference adaptive PID (APID) controller design for a class of uncertain non-linear systems is discussed. The exact linear system based upon feedback linearisation approach is employed to establish the equivalent linear model of the process, which is then employed to develop the controller. In order to demonstrate the performance of the proposed technique, the trajectory tracking of the single arm robot manipulator subjected to process uncertainties and input disturbance is conducted. Simulation results illustrate the superior performance of the APID algorithm achieving the targets by fully eliminating the severe load disturbance exerted to the robot consuming feasible amount of control energy. This remarkable performance proposes it as an effective robust control strategy in online and/or offline precise servo control applications.

The paper realizes a design of digital fuzzy adaptive tuning PID electronic governor. It is based on VHDL description and implemented on FPGA. The paper proposes to detect the speed with duty cycle calculation and use fuzzy-control rules to amend the PID parameters on line to achieve the fuzzy adaptive tuning PID control. On this basis, the paper introduces the implementation of the system in detail, including speed detection, fuzzy logic inference, and fuzzy adaptive tuning PID algorithm. The experiment proves that digital wide-range electronic speed governor implemented on control chip FPGA can be used widely and fuzzy adaptive tuning PID control algorithm can improve the dynamic response of the system, which makes the electronic speed governor under good control in a wider work scope.

A linear quadratic Gaussian (LQG) strategy controls a two-link flexible robot manipulator tracking a two-dimensional square trajectory 12.6 m/spl times/12.6 m. Slew angles together with a Gaussian white process and white or non-white measurement noise are fed into a LQG regulator (Kalman filter). A FLC strategy incorporates two fuzzy controllers substituted for the LQR state-space dynamics equations. Trajectories were obtained for the LQG strategy with Gaussian white and non-white measurement noise. The trajectory obtained for white measurement noise closely approaches a perfect square while those obtained for non-white measurement noise deviate. The trajectory obtained with the FLC strategy is similar to that for LQG with white measurement noise. Fuzzy control is found to provide robustness in operation and can be constructed with less mathematical complexity than LQG. The deviation in trajectories for LQG with non-white measurement noise suggests sub-optimal control and possible instability. This study has demonstrated the extent of deviation in tracking with an LQG strategy for non-white measurement noise and the FLC strategy as a viable option for precise and robust tracking control of a two-link flexible robot manipulator.