Optimal Proportional-integral-derivative Controller Research Articles

A new intelligent online fuzzy tuning approach for multi-area load frequency control: Self Adaptive Modified Bat Algorithm

The primary aim of the Automatic Generation Control (AGC) is to maintain system frequency and tie-line interchanges in a predestine limits by regulating the power generation of electrical generators, in case of fluctuations in the system frequency and tie-line loadings. This paper proposes a new online intelligent strategy to realize the control of multi-area load frequency systems. The proposed intelligent strategy is based on a combination of a novel heuristic algorithm named Self-Adaptive Modified Bat Algorithm (SAMBA) and the Fuzzy Logic (FL) which is used to optimally tune parameters of Proportional–Integral (PI) controllers which are the most popular methods in this context. The proposed controller guaranties stability and robustness against uncertainties caused by external disturbances and impermanent dynamics that power systems face. To achieve an optimal performance, the SAMBA simultaneously optimizes the parameters of the proposed controller as well as the input and output membership functions. The control design methodology is applied on four-area interconnected power system, which represents a large-scale power system. To evaluate the efficiency of the proposed controller, the obtained results are compared with those of Proportional Integral Derivative (PID) controller and Optimal Fuzzy PID (OFPID) controller, which are the most recent researches applied to the present problem. Simulation results demonstrate the successfulness and effectiveness of the Online-SAMBA Fuzzy PI (MBFPI) controller and its superiority over conventional approaches.

Integrated PLC-fuzzy PID Simulink implemented AVR system

Abstract Improving the transient response of power generation systems using automation control in a precise manner is the key issue. We design a fuzzy proportional integral derivative (PID) controller using Matlab and programmable logic controllers (PLCs) for a set point voltage control problem in the automatic voltage regulator (AVR) system. The controller objective is to maintain the terminal voltage all the time under any loads and operational conditions by attaining to the desired range via the regulation of the generator exciter voltage. The main voltage control system uses PLCs to implement the AVR action. The proposed fuzzy controller combines the genetic algorithm (GA), radial-basis function network (RBF-NN) identification and fuzzy logic control to determine the optimal PID controller parameters in AVR system. The RBF tuning for various operating conditions is further employed to develop the rule base of the Sugeno fuzzy system. The fuzzy PID controller (GNFPID) is further designed to transfer in PLCs (STEP 75.5) for implementing the AVR system with improved system response. An inherent interaction between two generator terminal voltage control and excitation current is revealed. The GNFPID controller configures the control signal based on interaction and there by reduces the voltage error and the oscillation in the terminal voltage control process. We achieve an excellent voltage control performance by testing the proposed fuzzy PID controller on a practical AVR system in synchronous generator for improve the transient response.

OPTIMAL PID CONTROLLER DESIGN USING ADAPTIVE VURPSO ALGORITHM

AbstractThe purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.

Robust PID Tuning of Autonomous Underwater Vehicle Using Harmonic Search Algorithm Based on Model Order Reduction

The objective of this paper is to present Genetic Algorithm (GA) and Harmonic Search Algorithm (HSA) based on PID tuning and Model Order Reduction with Balance Truncation (MORBT). This method is used for constructing a second order balanced truncation model order reduction for symmetric linear time invariant third order control systems. These methods are closely related to the proper and improper controllability and observability Gramians and Hankel singular values of descriptor systems. The HS (Harmonic Search) algorithm mimics behaviours of music players in an improvisation process, in order to find a better state of harmony which can be translated into a solution vector in the optimization process. PID (proportional-integral-derivative) controller provides sufficient stability margins and good time responses. It is now possible to design an optimal PID controller with properties of a GA and HSA. In control strategies, like PID controller are successfully designed to control the autonomous underwater vehicle. The elementary focus is to simulate the controller response.

Optimal PID control of DC motor with ABC and PSO algorithms

Proportional-integral-derivative (PID) control is widely used in DC motor system, because of its simple structure, high reliability and so on. However, the parameters of PID controller are commonly tuned by trial and error which also has large overshoot phenomenon for nonlinear and time-varying systems. In order to overcome the deficiency of the PID controller whose parameters are difficult to adjust, two swarm intelligence algorithms - artificial bee colony (ABC) and particle swarm optimisation (PSO) - have been proposed to estimate the optimal parameters of PID controller for speed control. The simulations have been done and results show that ABC and PSO algorithms have stable convergence characteristic and good computational ability, and they are effective and easily implemented methods for optimal tuning of PID parameters.

Sugeno fuzzy PID tuning, by genetic-neutral for AVR in electrical power generation

We report a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system, using a combined genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic approaches. GA and a RBF-NN with a Sugeno fuzzy logic are proposed to design a PID controller for an AVR system (GNFPID). The problem for obtaining the optimal AVR and PID controller parameters is formulated as an optimization problem and RBF-NN tuned by GA is applied to solve the optimization problem. Whereas, optimal PID gains obtained by the proposed RBF tuning by genetic algorithm for various operating conditions are used to develop the rule base of the Sugeno fuzzy system and design fuzzy PID controller of the AVR system to improve the system's response (∼0.005s). The proposed approach has superior features, including easy implementation, stable convergence characteristic, good computational efficiency and this algorithm effectively searches for a high-quality solution and improve the transient response of the AVR system (7E−06). Numerical simulation results demonstrate that this is faster and has much less computational cost as compared with the real-code genetic algorithm (RGA) and Sugeno fuzzy logic. The proposed method is indeed more efficient and robust in improving the step response of an AVR system.

Tuning Optimal Proportional–Integral–Derivative Controllers for Desired Closed-Loop Response Using the Method of Moments

In the direct synthesis method of controller design, the optimality of the controller parameters are strongly affected by the approximations involved in developing the process models and the choice of desired closed-loop response characteristics. This work (i) captures the process dynamic characteristics through the method of moments; (ii) proposes a tuning parameter λ in the direct synthesis method, which is optimized through the minimization of integrated absolute error (IAE) subject to the constraint on peak sensitivity; and (iii) provides correlations for optimal λ values as a function of process parameters. First-order plus delay time (FOPDT), second-order plus delay time (SOPDT), and second-order with inverse response (SOIR) models are used to characterize the process dynamics and as desired closed-loop transfer functions. The efficacy of the proposed method is illustrated with the help of several types of processes. The proposed controller tuning method performs equally well as compared to other methods for plants approximated with FOPDT models and has been shown to be very effective for SOPDT and SOIR models.

PSO-Based Tunning of PID Controller for Speed Control of DC Motor

In this paper, a Proportional-Integral-Derivative (PID) controller of DC motor is designed by using particle swarm optimization (PSO) strategy for formative optimal PID controller tuning parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment. Comparing with conventional PID controller using Genetic Algorithm, the planned method is more proficient in improving the speed loop response stability, the steady state error is reduced, the rising time is perfected and the change of the required input do not affect the performances of driving motor with no overtaking.

Small signal stability analysis of dish-Stirling solar thermal based autonomous hybrid energy system

Present work presents small signal stability analysis of an autonomous hybrid energy system with dish-Stirling solar thermal systems (DSTS) in integration with diesel engine generators (DEG), fuel cells (FC), battery energy storage system (BESS), and aqua electrolyzer (AE). The performance of Genetic algorithm (GA) optimized integral (I), proportional plus integral (PI), and proportional-integral-derivative (PID) controllers in containing the frequency deviation in the proposed system has been investigated. The dynamic performance of all three controllers, so optimized, is compared with manually tuned I controller. Simulation results revealed that the performance of the GA optimized PID controller is found to be the best amongst all three controllers. Further, sensitivity analysis is carried out to access the robustness of the controllers.

Design and Implementation of Optimal Fuzzy PID Controller for DC Servo Motor

A method is proposed for improving the transient positioning response of a DC servo motor controlled by a proportional- integral-derivative (PID) controller. In the proposed approach, the optimal gains of the PID controller are determined using an Evolutionary Programming (EP) scheme and a Genetic Algorithm (GA), respectively, based on an integrated-absolute error (IAE) fitness function. The optimal gain constants obtained using the two methods a re then fine tuned using a Fuzzy Logic Controller (FLC). The experimental results show that the FLC controller yields an effective improvement in the positioning response of the DC servo motor compared to that obtained using the optimized PID controller alone. In addition, it is shown that the FLC + EP based PID controller provides a more rapid response than the FLC + GA based PID controller. The effectiveness of proposed controllers is verified experimentally.

An Optimal PID Controller for a Bidirectional Inductive Power Transfer System Using Multiobjective Genetic Algorithm

Bidirectional inductive power transfer (IPT) systems are suitable for applications that require wireless and two-way power transfer. However, these systems are high-order resonant networks in nature and, hence, design and implementation of an optimum proportional–integral–derivative (PID) controller using various conventional methods is an onerous exercise. Further, the design of a PID controller, meeting various and demanding specifications, is a multiobjective problem and direct optimization of the PID gains often lead to a nonconvex problem. To overcome the difficulties associated with the traditional PID tuning methods, this paper, therefore, proposes a derivative-free optimization technique, based on genetic algorithm (GA), to determine the optimal parameters of PID controllers used in bidirectional IPT systems. The GA determines the optimal gains at a reasonable computational cost and often does not get trapped in a local optimum. The performance of the GA-tuned controller is investigated using several objective functions and under various operating conditions in comparison to other traditional tuning methods. It was observed that the performance of the GA-based PID controller is dependent on the nature of the objective function and therefore an objective function, which is a weighted combination of rise time, settling time, and peak overshoot, is used in determining the parameters of the PID controller using multiobjective GA. Simulated and experimental results of a 1-kW prototype bidirectional IPT system are presented to demonstrate the effectiveness of the GA-tuned controller as well as to show that gain selection through multiobjective GA using the weighted objective function yields the best performance of the PID controller.

Design Optimization of PID Controller in Automatic Voltage Regulator System Using Taguchi Combined Genetic Algorithm Method

The optimum design of the proportional-integral-derivative (PID) controller plays an important role in achieving a satisfactory response in the automatic voltage regulator (AVR) system. This paper presents a novel optimal design of the PID controller in the AVR system by using the Taguchi combined genetic algorithm (TCGA) method. A multiobjective design optimization is introduced to minimize the maximum percentage overshoot, the rise time, the settling time, and the steady-state error of the terminal voltage of the synchronous generator. The proportional gain, the integral gain, the derivative gain, and the saturation limit define the search space for the optimization problem. The approximate optimum values of the design variables are determined by the Taguchi method using analysis of means. Analysis of variance is used to select the two most influential design variables. A multiobjective GA is used to obtain the accurate optimum values of these two variables. MATLAB toolboxes are used for this paper. The effectiveness of the proposed method is then compared with that of the earlier GA method and the particle swarm optimization method. With this proposed TCGA method, the step response of the AVR system can be improved.

Modeling and model predictive control of a nonlinear hydraulic system

This paper deals with modeling and control of a hydraulic three-tank system. A process of creating a computer model in the MATLAB/Simulink environment is described, and optimal PID (proportional–integral–derivative) and model predictive controllers are proposed. The modeling starts with the creation of an initial mathematical model based on a first-principles approach. Then, the initial model is enhanced to obtain better correspondence with a real-time system, and parameters of the modified system are identified from measurements. The real-time system contains nonlinearities which cannot be neglected, and therefore are identified and included in the final mathematical model. The resulting model is used for a control design. As the real-time system has long time constants, usage of the Simulink model dramatically speeds up the design process. Optimal PID and model predictive controllers (MPC) are proposed and compared. Model predictive controllers for both a linearized model and the nonlinear system are proposed. The techniques described are not limited to one particular modeling problem, but can be used as an illustrative example for the modeling of many technological processes.

Design of Optimal PID Controller withɛ-Routh Stability for Different Processes

This paper presents a design method of the optimal proportional-integral-derivative (PID) controller withɛ-Routh stability for different processes through Lyapunov approach. The optimal PID controller could be acquired by minimizing an augmented integral squared error (AISE) performance index which contains control error and at least first-order error derivative, or even may containnth-order error derivative. The optimal control problem could be transformed into a nonlinear constraint optimization (NLCO) problem via Lyapunov theorems. Therefore, optimal PID controller could be obtained by solving NLCO problem through interior method or other optimization methods. The proposed method can be applied for different processes, and optimal PID controllers under various control weight matrices andɛ-Routh stability are presented for different processes. Control weight matrix andɛ-Routh stability’s effects on system performances are studied, and different tuning methods’ system performances are also discussed.ɛ-Routh stability’s effects on disturbance rejection ability are investigated, and different tuning methods’ disturbances rejection ability is studied. To further illustrate the proposed method, experimental results of coupled water tank system (CWTS) under different set points are presented. Both simulation results and experiment results show the effectiveness and usefulness of the proposed method.

A NEW DESIGN METHOD OF OPTIMAL PID CONTROLLER WITH DYNAMIC PERFORMANCES CONSTRAINED

A systematic tuning method is proposed to design the optimal proportional-integral-derivative (PID) controller with dynamic performances constraint for different processes. The proposed optimal PID controller can be acquired by minimizing an augmented integral squared error–hybrid dynamic performance items (AISE– HDPI) performance index. The AISE–HDPI performance index is constituted by error items and dynamic performance items which at least include one dynamic performance item. The optimal control problem is approximate equivalent transformed into a nonlinear constraint optimization (NLCO) problem through Lyapunov theorems and dominant poles method. Therefore, the proposed optimal PID controller can be obtained by solving the NLCO problem. The proposed method is utilized to design the optimal PID controller for different processes, and the optimal PID controllers under various control weight matrices are presented. The dynamic performances of different tuning methods are discussed. To deep study the proposed method, the robustness of different tuning methods is also investigated and a robustness evaluation function is proposed to assess the robust performances of different tuning methods. To further validate the proposed method, different tuning methods’ disturbance rejection ability is also investigated. The simulation results show effectiveness, usefulness, and robustness of the proposed method.

Automatic Tuning Of Proportional-Integral-Derivative (Pid) Controller Using Particle Swarm Optimization (Pso) Algorithm

The proportional-integral-derivative (PID) controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI) method of particle swarm optimization (PSO) algorithm for tuning the optimal proportional-integral derivative (PID) controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. Ziegler- Nichols, tuning method was applied in the PID tuning and results were compared with the PSO-Based PID for optimum control. Simulation results are presented to show that the PSO-Based optimized PID controller is capable of providing an improved closed-loop performance over the Ziegler- Nichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of Ziegler-Nichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of DC motor.

Position Control of a Single Arm Manipulator Using GA-PID Controller

This paper demonstrates in detail how to employ the genetic algorithm (GA) optimization technique to search efficiently the optimal proportional-integral-derivative (PID) controller gains to control the position of a fixed arm manipulator system. The system identification technique is used to find an equivalent transfer function for the system under study. GA is applied off-line to find the optimal PID controller parameters based on the identified model. The experimental and simulation results of the actual system and its identified model under the influence of the optimal PID controller are explored. The proposed approach shows superior features, including easy implementation, stable convergence characteristic, and good computational efficiency.

Adaptive proportional–integral–derivative tuning sliding mode control for a shape memoryalloy actuator

In this paper, a novel adaptive sliding mode control with a proportional–integral–derivative(PID) tuning method is proposed to control a shape memory alloy (SMA) actuator. Thegoal of the controller is to achieve system robustness against the SMA hysteresisphenomenon, system uncertainties and external disturbances. In the controller, the PIDcontroller is employed to approximate the sliding mode equivalent control along thedirection that makes the sliding mode asymptotically stable. Due to the systemnonlinearity, the PID control gain parameters are systematically computed online accordingto the adaptive law. To improve the transient performance, the initial PID gain parametersare optimized by the particle swarm optimization (PSO) method. Simulationand experimental results demonstrate that the controller performs well for thedesired trajectory tracking, and the hysteresis phenomenon is compensated forcompletely. The control results are also compared with the optimized PID controller.

Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization

In this paper, two lbests multi-objective particle swarm optimization (2LB-MOPSO) is applied to design multi-objective robust Proportional-integral-derivative (PID) controllers for two MIMO systems, namely, distillation column plant and longitudinal control system of the super maneuverable F18/HARV fighter aircraft. Multi-objective robust PID controller design problem is formulated by minimizing integral squared error (ISE) and balanced robust performance criteria. During the search, 2LB-MOPSO can focus on small regions in the parameter space in the vicinity of the best existing fronts. As the lbests are chosen from the top fronts in a non-domination sorted external archive of reasonably large size, the offspring obtained can be more diverse with good fitness. The performance of various optimal PID controllers is compared in terms of the sum of ISE and balanced robust performance criteria. For the purpose of comparison, 2LB-MOPSO, NSGA-II as well as earlier reported Riccati, IGA and OSA methods are considered. The performance of PID controllers obtained using 2LB-MOPSO is better than that of others. In addition, Hypervolume-based comparisons are carried out to show the superior performance of 2LB-MOPSO over NSGA-II. The results reveal that 2LB-MOPSO yields better robustness and consistency in terms of the sum of ISE and balanced robust performance criteria than various optimal PID controllers.

Design of robust optimal proportional–integral–derivative controller based on new interval polynomial stability criterion and Lyapunov theorem in the multiple parameters' perturbations circumstance

Based on the new interval polynomial stability criterion and Lyapunov theorem, a robust optimal proportional-integral-derivative (PID) controller is proposed here to design for different plants that contain the perturbations of multiple parameters. A new stability criterion of the interval polynomial is presented to determine whether the interval polynomial belongs to Hurwitz polynomial. The robust optimal PID controller is acquired through minimising an augmented integral squared error (AISE) performance index. The robust optimal control problem is transformed into a non-linear constraint optimisation (NLCO) problem by applying new polynomial stability criterion and Lyapunov approach. The robust optimal PID parameters are obtained from solving the NLCO problem. The robustness and performances of the proposed method and other different tuning methods are compared. The ability of the proposed PID tuning method and other tuning methods to reject disturbances is discussed as well. The simulation results are presented to demonstrate the effectiveness of the proposed method and show better robustness of the robust optimal PID controller.