Finite dimensional repetitive controller for identified model of a DC servo system

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload

Quadrotor is a key technology widely used in national defence, electric power and agriculture. In order to solve the problems of response hysteresis and sensitivity to sudden input changes of the conventional Proportional-Integral-Derivative (PID) control algorithm in quadrotor control, a filter is added to the outer differential term based on the conventional serial PID control to form an incomplete differential algorithm, which improves the dynamic characteristics of the system and reduces the sensitivity to sudden signal changes; at the same time, a filter is added based on the conventional serial PID control algorithm to form an incomplete differential algorithm, which improves the dynamic characteristics of the system and reduces the sensitivity to sudden signal changes. A filter is added based on the conventional series PID control algorithm to form an incomplete differential algorithm, which improves the dynamic characteristics of the system and reduces the sensitivity to abrupt signal changes; at the same time, a filter is added based on the conventional series PID control algorithm to achieve a smooth controller response curve. Simulation results show that compared with the conventional PID controller, the improved PID control algorithm reduces the maximum overshoot of the system step response from the original 8% to 3%, and its robustness and reliability are greatly improved.

A fractional-order fuzzy proportional integral derivative (PID) controller is a controller that combines the benefits of fractional calculus and fuzzy logic with the conventional PID controller. In this paper, a four-stage optimization algorithm is proposed for the design of a Type-2 Fuzzy fractional-order PID controller based on the Fourier Series Method (FSM). Three distinct control structures are introduced: Type-2 fuzzy fractional PD + fractional PI controller, Type-2 fuzzy fractional PID, and Type-2 fuzzy fractional PD + Type-2 fuzzy fractional PI controller. In addition to a modified multi-performance criterion cost function, four integral performance criteria are employed as cost functions for each stage. The suggested algorithm avoids the utilization of the approximation equivalent for the fractional-order system and instead employs FSM. Furthermore, the approach optimizes the nonlinearity within the upper membership function (UMF) and the uncertainty range through the lower membership function, as opposed to arbitrary selection. By considering variations in the membership functions, the outcomes exhibit a superior response compared to previous investigations. The results of the three control structures are compared with the traditional PID controller, and simulation results demonstrate the feasibility of this technique. The findings suggest that by optimizing different integral performance criteria using this design technique, controllers for both integer and fractional-order plants can yield favorable step responses. The proposed algorithm is validated by comparing its step response performance with that of previous research, followed by a discussion on sensitivity analysis and computational requirements.

This study presents the development of dead-time compensator with Proportional Integral-Derivative (PID) controller and their implementation in real-time using the Programmable Logic Controller (PLC) on a heater-furnace system that constitute the temperature process. Though PID controller is effective to most of the processes, it has draw backs when used to control the processes with significant dead time. Therefore to eliminate the effects of long dead time, the dead-time compensator combined with PID is proposed. The control equation for the proposed dead time compensator is developed using the mathematical model of heater-furnace system and ladder logic program is created in GE Fanuc Versamax PLC. The PID controller is tuned using process reaction curve method and programmed in PLC. The proposed controller has an outer loop control mechanism with dead time compensator which feeds the inner loop PID controller. The effects of dead time on the performance of PID controller are eliminated through the outer loop control mechanism whose output does not contain modelling errors. The results of the proposed dead-time compensator with PID are compared with the results of the conventional PID controller without dead time compensation for both steady state response and servo-regulatory response. The results of the proposed controller show better improvement in the time domain specifications compared to conventional PID controller.

This paper demonstrates the design and analysis of automatic generation control using intelligent genetic algorithm tuned fuzzy based controller. A two area thermal power system simulated for four different scenarios considers a reheat steam turbine in each area with Generator rate constraints. The Integral Time Squared Error (ITSE) employed to get an objective function for the optimization of controller gains. The simulation results compared with the conventional Proportional Integral Derivative (PID) controller, Genetic Algorithm (GA) tuned PID controller and GA tuned Fuzzy PID controller. The proposed GA tuned Fuzzy based PID Controller can generate the best performance for peak overshoot, undershoot and settling time with step load disturbances. Robustness of the performance of the proposed controller provided with system parametric uncertainties.

Superheaters are heat exchangers that transfer energy from flue gas to superheated steam. Controlling of steam temperature at the superheater outlet is a very difficult task in thermal power plant. A superheater model and an appropriate optimal control strategy are the essential tools for improving the accuracy of this control system. Superheater is simulated as a unit of a control loop that generates steam of desired state values. To simulate the steam superheater on the computer, the heat exchanger assembly is described by sets of non-linear partial differential equations (PDE). The equations are then solved by modified finite difference method. A linearized model of the superheater is identified using system identification tool box in order to design optimal control strategies for Superheated Steam (SHS) temperature system. Then to tune Proportional Integral Derivative (PID) controller parameters optimally, different tuning schemes were applied to superheated steam temperature system. However, due to new challenges in keeping up with rapid changes in load demand and the transients, performance of the PID controller is far from being optimal. Hence a Model Predictive Controller (MPC) is designed for the superheated steam temperature regulation in a supercritical coal-fired power plant. Compared with the performance of the plant using a conventional PID controller, the steam temperature controlled by the MPC is found to be more optimal which leads to more efficient plant operation and energy saving.

Headbox is a critical sub-process of paper machine. Headbox is a two input and two output process with significant loop interaction. Quality of the paper produced depends on its operation. Hence, its precise control is required to produce good quality paper. This paper presents design of proportional integral derivative (PID) controller for headbox using particle swarm optimization (PSO) technique. Performance and robustness of PSO - PID controller have been compared with conventional PID controllers on the basis of time response, frequency response and performance indices (ISE, IAE, ITSE and ITAE).

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

This paper presents the designing and simulation for vertical moving control of Unmanned Air Vehicles (UAV) system using Proportional Integral Derivative (PID) control method, Linear Quadratic Regulator (LQR) control method and fuzzy logic control technique.The UAV system pose a challenger control problem that in it precise model of system is non-linear. The conventional PID controller, the LQR controller, Fuzzy Logic Controller (FLC) and Self-Tuning Fuzzy PID controller are designned based on the dynamic modeling of the system is derived from a suitable mathematical model to describe the vertical motion of a UAV. Simulation results in Matlab and Simulink show that by using self-tuning Fuzzy PID, the performance of vertical moving control system is improved significantly compared to Other controllers. DOI: http://dx.doi.org/10.11591/ijece.v3i5.3440

DC motors are the most widely used electrical motors since their ease of use. Controlling a DC motor can be done by adjusting amount of voltage and current supplied to the motor. In this design the controlling is done using proportional integral derivative (PID) control, applied for movement in XY plane. Ziegler-Nichols method is used to determine PID constants. A Raspberry Pi is used to receive and process input from the user through a graphical user interface (GUI). The data input consists of two values, i.e. position and velocity. The GUI also shows the actual coordinate position on the screen. Two motors are controlled simultaneously by the Raspberry Pi through serial peripheral interface (SPI) with one microcontroller for each motor axis. The program contains a PID control algorithm to stabilize the motor speed. The coordination of the two stabilized motor speed is used to draw a sloping line. Sloping lines are drawn in order to determine the performance of the PID controller.

Synthesis of a control system using a fuzzy inference is proposed for switching regulators. A computer simulation result is given, as an example, where an output voltage of a Cuk converter is regulated by using the proposed fuzzy inference. The simulated waveforms of the output voltage at start-up and the responses to disturbances (variations of the load resistance and of the input voltage) are studied, as compared with those of a conventional proportional integral derivative (PID) control. The variation of the output voltage is smaller and the output voltage settles faster than that using the PID control. >

Aiming at balancing and positioning of a new electro-hydraulic servo system with iso-actuation configuration, an extended state observer–based fractional order proportional–integral–derivative controller is proposed in this study. To meet the lightweight requirements of heavy barrel weapons with large diameters, an electro-hydraulic servo system with a three-chamber hydraulic cylinder is especially designed. In the electro-hydraulic servo system, the balance chamber of the hydraulic cylinder is used to realize active balancing of the unbalanced forces, while the driving chambers consisting of the upper and lower chambers are adopted for barrel positioning and dynamic compensation of external disturbances. Compared with conventional proportional–integral–derivative controllers, the fractional order proportional–integral–derivative possesses another two adjustable parameters by expanding integer order to arbitrary order calculus, resulting in more flexibility and stronger robustness of the control system. To better compensate for strong external disturbances and system nonlinearities, the extended state observer strategy is further introduced to the fractional order proportional–integral–derivative control system. Numerical simulation and bench test indicate that the extended state observer–based fractional order proportional–integral–derivative significantly outperforms proportional–integral–derivative and fractional order proportional–integral–derivative control systems with better control accuracy and higher system robustness, well demonstrating the feasibility and effectiveness of the proposed extended state observer–based fractional order proportional–integral–derivative control strategy.

Electroactive polymer actuators, especially those based on polypyrrole (PPy), possess unique characteristics such as an ability to operate at the macro or micro scale, large force-to-weight ratio, biocompatibility, low cost and operation in aqueous and non-aqueous environments. Therefore, they are very suitable for the establishment of bio-mimetic devices, single-cell manipulators, robotics, prosthetics, and numerous biomedical applications. In this paper, we report on the neuro-fuzzy control of these actuators, which are typified by the tri-layer polypyrrole actuators considered in this paper, in order to improve their positioning accuracy and speed of response. We experimentally evaluated two model-free intelligent control strategies, which are fuzzy logic PD+I control and neuro-fuzzy Adaptive Neural Fuzzy Inference System (ANFIS) control. The performance of these intelligent controllers is compared to that of a conventional Proportional Integral Derivative (PID) controller. The experimental results demonstrated that they significantly outperformed the conventional PID controller with an improvement in rise time of at least 18 times and in settling time of at least 2 times. To the best of authors' knowledge, this is the first study to design and evaluate fuzzy logic PD+I and neuro-fuzzy ANFIS PD+I intelligent control methodologies for an important class of electroactive polymer actuators.

This study aims to examine the use of Proportional Integral Derivative (PID) and Model Predictive Control (MPC) in Load Frequency Control (LFC). A balance between the generation and the load is necessary to ensure the consistency of the electrical supply. These days, MPC techniques are becoming popular due to their merits over conventional PID controllers. In this study, performance of the designed PID and MPC schemes has been tested and compared for two area interconnected power system. This system consists hydro unit in area-1 and three Thermal units in area-2. A step load perturbation scenario in both areas has been obtained to evaluate the effectiveness of the designed control strategies. The results show that both the designed control approaches performed successfully however, the MPC scheme outperform the PID scheme in terms of time domain specifications like reduced oscillation and smaller settling time. It can be concluded that MPC can be used as a secondary controller for LFC applications in future research.

This paper focuses on the design of a fuzzy fractional Proportional-Integral-Derivative (PID) controller. The controller which is based on the conventional PID controller is modified to be P1–α I1–β D1–γ with the objective to make the controller more flexible and robust to the changes in input and the parameters. In this control structure, the fuzzy logic control acts as the tuner for the parameters α, β and γ, so that the overall controller parameter can be varied and converge better to the changes in a system. The designed fuzzy fractional PID controller is applied to a validated antilock braking system model using an electronic wedge brake system as the actuator. Numerous numerical simulations and comparisons with other fractional PID/conventional PID controllers show that the fuzzy fractional PID controller can not only ensure good control performance with respect to reference input but also improve the system robustness with respect to model uncertainties.