Fuzzy Precompensation of PD Controllers For Systems with Deadzones

Optimal gain scheduling of conventional PID controller for simultaneous disturbance rejection and faster settling time capabilities is very difficult to achieve for quadrotor system. The nonlinear system dynamics and unstable behavior of quadrotor require a more advanced attitude tracking stabilization. Therefore, in this paper a novel hybrid PD-adaptive neuro-fuzzy interface system controller design is presented. The simulation results of the proposed hybrid controller are compared with conventional PD, PID and fuzzy logic controller. The comparative study of the dynamic response plots reveals that the proposed hybrid controller is able to exhibit better results in terms of response time, robustness and better disturbance rejection capability.

Composite Disturbance Rejection Control for Ball Balancer System

Cascade fuzzy control of a tubular chemical reactor

In this study, pitch angle control is employed to maintain the output power and the rotor speed of the wind turbine, whenever the wind speed is higher than the base wind speed. Blade pitch is a key part of a wind turbine and it is a complex nonlinear system interrupted by many uncertainties. The proposed controller linear active disturbance rejection control is utilized to control the output power and rotor speed of the wind turbine. LADRC is based on the extended state observer and a proportional derivative control law. The distinguished feature of the LADRC is, that it can estimate the internal and external disturbances and to compensate for it in real time. The other advantage of the LADRC controller is that only two parameters need to be tuned: observer bandwidth and controller bandwidth. The purpose of the proposed pitch angle Control is to regulate the pitch angle of the wind turbine with respect to wind speed to improve the efficiency of the wind turbine and overcome the safety issues. To validate the effectiveness and superiority of the proposed controller, the results are compared with the conventional PD and Fuzzy logic controller. The simulation results show that the LADRC controller is better in performance as compared to the conventional PD and FLPI controller.

Mechatronic systems are conventionally modeled by integer order state equations for simplicity. Based on these integer-order models, integer-order PID or other classical model-based controllers are designed. However, often complex mechatronic systems require fractional-order state equations for a more adequate description. In this paper, a Steward platform (ball and plate system) with different balls is used to demonstrate this. A fractional-order PD controller is designed and tuned by a genetic algorithm based global optimizer to obtain the suitable parameter values in time domain. The experimental results show that the fractional-order PD controllers can provide better performances than conventional integer-order PD controllers.

This paper presents an implementation of conventional PID (CPID) controller using Ziegler-Nichols rules and fuzzy PD (FPD) controller for position servo motor control based on Lab View (Laboratory Virtual Instrument Engineering Workbench Environment) through Data Acquisition (DAQ) Device PCI- 6521 of National Instrument's and Data Acquisition Accessory Board Model (CB-68LP).CPID controller is perhaps the most well-known and most widely used in industrial applications. However, it has been known that CPID controller generally don’t work well for non-linear systems, higher order and time-delayed linear system and particularly complex and vague system. To overcome these difficulties, this paper proposes to use the FPD controller for a servo motor system instead of CPID. The parameters of servo motor used are completely unknown. The FPD structure has two-input single-output and fairly similar characteristic to its conventional counterpart and provides good performance. Simple rules base are used for FPD (nine rules only). Performance evaluation was carried out via a comparison study for the proposed control scheme and other existing control scheme, such as CPID controller. The critical point for this experiment on position system is a steady state error and settling time. The performance showing that the FPD has less settling time and zero steady state error over its CPID. The algorithms of FPD and CPID controllers are implemented using PID, Fuzzy Logic and simulation toolkits of the Lab View environment.

This paper presents a method of controlling a serial robot for milling by an inverse kinematic controller combined with an outer PD loop (Inverse Dynamics + PD controller), with calibration and compensation of errors in calculating the cutting forces. Because the cutting forces are generated at the time of cutting, at the contact area between the workpiece and the cutting tool, the generalized forces of the cutting forces in the differential equations of motion of robot is always variable and difficult to determine precisely. The cutting forces depend on the cutting mode, the geometric parameters of the cutting layer, the cutting conditions, etc. This study shows an inverse dynamic controller with the outer PD loop and an additional calibration block to compensate the differences between the actual cutting forces and calculated cutting forces (which are caculated by the empirical formula). The cutting forces at each machining time of the calibration block is determined based on the differential equation of motion. The efficiency (convergence time and accuracy) of the proposed controller is evaluated by comparison between the numerical simulation results of the controller with cutting force calibration and the conventional PD controller. In the conventional PD controller, the dynamic model of the robot is assumed to define precisely. The results contribute to design and manufacture the controllers for robotic milling, and to improve the quality of the machined surface.

In this paper, an adaptive robust PD controller for Unmanned Underwater Vehicle (UUV) is proposed. Though conventional PD control is simple and effective for UUV heading control, in many circumstances, a large initial torque is required, while the maximum torque of the rudder is limited, thus making it unavailable to further improve the heading control effects. The adaptability and robustness of this control system is investigated by computer simulation, lake and sea trials. Experimental results indicate the effectiveness of the proposed controller in dealing with model uncertainties, nonlinearities of UUV, and eliminating the excessive initial output torque of the rudder and environmental disturbance caused by sea currents and waves.

Power stabilization is a critical issue in nuclear reactors. A conventional PD controller is currently used in Egypt's 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.

Existing control methods do not perform well when applied to systems containing nonlinearities arising from unknown deadzones. In particular, we show that a usual fuzzy controller applied to a system with a deadzone suffers from poor transient performance and a large steady-state error. In this paper, we propose a novel two-layered logic controller for controlling systems with deadzones. The two-layered control structure consists of a logic-based precompensator followed by a usual PD controller. Our proposed controller exhibits superior transient and steady-state performance compared to usual PD controllers. In addition, the controller is robust to variations in deadzone nonlinearities. We illustrate the effectiveness of our scheme using computer simulation examples. >

Simple tuning methods of PI, PD and PID controller are proposed for an integrating process with time delay. This is based on matching the coefficients of corresponding powers of s in the numerator and that in the denominator of the closed-loop transfer function. For set-point tracking problem, the derived controller is found to be a PD controller which is shown by Lee's tuning rule based on minimizing the performance indexes (ISE, IAE, ITAE) using a real-coded genetic algorithm. A method can be also proposed PI, PID controllers according to tuning parameter lambda(λ) similar to IMC method. Simulation example is given to illustrate the set-point tracking and disturbance rejection performance of the proposed method.

For two-wheeled self-balancing robot nonlinear model uncertainties and other characteristics, the design of fuzzy immune PD controller and a simulation study. The simulation results show that the control system than the conventional fuzzy PD controller with a small overshoot, adjust the time is short, anti-interference ability, etc., applicable to the mathematical model and the parameter change is difficult to determine two-wheeled self-balancing robot control system.

In this paper, we propose the LQ servo control method for the automatic train control of urban rail vehicle. Structures of the conventional PID control and LQ servo controller are compared in order to demonstrate the simplicity of the proposed controller which doesn't have zeros of the closed loop systems. The energy consumption is dealt with as an object function to be minimized, which consists of the quadratic performance indices for optimal control with the input of the feedback linearization. The effectiveness of the proposed method is shown by the practical example, compared with the conventional PD controller.

The traditional manual operation mode has problems such as high manpower cost, low efficiency, low safety, low operating precision, etc. Achieving unmanned intelligent systems in the field of mechanical and electrical engineering has become the key to transforming and upgrading relevant enterprises and improving their overall competitiveness. In this paper, an improved fuzzy PID control system is constructed based on a PLC control system combined with a fuzzy PID intelligence algorithm. It is applied to the pumping motor, coagulation dosing system, and other specific devices for simulation experiments, compared to the conventional PID algorithm, and evaluated the control system in this paper. The fuzzy PID control algorithm of this paper reduces the steady-state adjustment time of the response curve by 30% compared to the conventional PID algorithm, and the overshooting amount is reduced by 25%. With almost no overshoot, the pumping motor that was equipped with this paper’s PID algorithm reached its stable target speed in 0.3 seconds. In addition, this study found that in the coagulation dosing system model incorporating the fuzzy PID algorithm of this paper, when the model is accurate, only 20% of the overshooting amount occurs after the system has been running for some time. The conventional PID algorithm’s 50% overshoot is significantly less than this, and the time it takes to reach a steady state is also 50 minutes earlier than the conventional PID control. When the model is imprecise, fluctuations occur in both conventional PID control and fuzzy PID control, but the amplitude of the fluctuations in fuzzy PID control is relatively small. In conclusion, this paper shows that the fuzzy PID control algorithm in this paper has higher control accuracy, response speed, and stability than the conventional PID control algorithm.

A set of general expressions is derived from a single asymmetrical relay feedback test for online plant identification. The expressions also remain valid for an odd symmetrical limit cycle test method. Using the expressions, the exact parameters of open-loop stable and unstable first-order plus time delay (FOPDT) and second-order plus time delay (SOPDT) transfer function models may be obtained from simple measurements made on the limit cycle. The approach can also be used to identify transfer functions of integrating processes. Conditions for the existence of limit cycles in unstable FOPDT and SOPDT processes are derived. The design of controllers for these processes is then considered and a simple, but very effective, approach using standard forms with a variable zero is presented. The advantages of using PI-PD control, compared with conventional PID or PI-D control, particularly for unstable and integrating processes is clearly shown. Examples are given to illustrate the value of the proposed general identification method and the improved system performance provided by the proposed controllers.