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Showing papers on "PID controller published in 2019"


Journal ArticleDOI
TL;DR: This work developed an open source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL, and used this environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.
Abstract: Autopilot systems are typically composed of an “inner loop” providing stability and control, whereas an “outer loop” is responsible for mission-level objectives, such as way-point navigation. Autopilot systems for unmanned aerial vehicles are predominately implemented using Proportional-Integral-Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However, more sophisticated control is required to operate in unpredictable and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL), which has had success in other applications, such as robotics. Yet previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with state-of-the-art RL algorithms—Deep Deterministic Policy Gradient, Trust Region Policy Optimization, and Proximal Policy Optimization. To investigate these unknowns, we first developed an open source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then used our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.

285 citations


Journal ArticleDOI
TL;DR: The theoretical analysis of the bounded-input bounded-output stability, the monotonic convergence of the tracking error dynamics, and the internal stability of the full form dynamic linearization based MFAC scheme are rigorously presented by the contraction mapping principle.
Abstract: In this paper, the main issues of model-based control methods are first reviewed, followed by the motivations and the state of the art of the model-free adaptive control (MFAC). MFAC is a novel data-driven control method for a class of unknown nonaffine nonlinear discrete-time systems since neither explicit physical model nor Lyapunov stability theory or key technical lemma is used in the controller design and theoretical analysis except only for the input/output (I/O) measurement data. The basis of MFAC is the dynamic linearization data modeling method at each operating point of the closed-loop system. The established dynamic linearization data model is a virtual equivalent data relationship in the I/O sense to the original nonlinear plant by means of a novel concept called pseudo-partial derivative (PPD) or pseudo-gradient (PG) vector. Based on this virtual equivalent dynamic linearization data model and the time-varying PPD or PG estimation algorithm designed merely using the I/O measurements of a controlled plant, the MFAC system is constructed. The main contribution of this paper is that the theoretical analysis of the bounded-input bounded-output stability, the monotonic convergence of the tracking error dynamics, and the internal stability of the full form dynamic linearization based MFAC scheme are rigorously presented by the contraction mapping principle; the well known PID control and the traditional adaptive control for linear time-invariant systems are explicitly shown as the special cases of this MFAC. The simulation results verify the effectiveness of the proposed approach.

280 citations


Journal ArticleDOI
TL;DR: A novel control methodology for tracking control of robot manipulators based on a novel adaptive backstepping nonsingular fast terminal sliding mode control (ABNFTSMC) is developed and compared with other state-of-the-art controllers.
Abstract: This paper develops a novel control methodology for tracking control of robot manipulators based on a novel adaptive backstepping nonsingular fast terminal sliding mode control (ABNFTSMC). In this approach, a novel backstepping nonsingular fast terminal sliding mode controller (BNFTSMC) is developed based on an integration of integral nonsingular fast terminal sliding mode surface and a backstepping control strategy. The benefits of this approach are that the proposed controller can preserve the merits of the integral nonsingular fast terminal sliding mode control (NFTSMC) in terms of high robustness, fast transient response, and finite-time convergence, as well as backstepping control strategy in terms of globally asymptotic stability based on Lyapunov criterion. However, the major limitation of the proposed BNFTSMC is that its design procedure is dependent on the prior knowledge of the bound value of the disturbance and uncertainties. In order to overcome this limitation, an adaptive technique is employed to approximate the upper bound value; yielding an ABNFTSMC is recommended. The proposed controller is then applied for tracking control of a PUMA560 robot and compared with other state-of-the-art controllers, such as computed torque controller, PID controller, conventional PID-based sliding mode controller, and NFTSMC. The comparison results demonstrate the superior performance of the proposed approach.

273 citations


Journal ArticleDOI
Ning Sun1, Tong Yang1, Yongchun Fang1, Yiming Wu1, He Chen1 
TL;DR: This paper proposes a new quasi-proportional integral derivative control method to effectively control underactuated double-pendulum crane systems and gives the first plant-parameter-free controller that incorporates both integral action and actuating constraints without any linearizing operations during controller design or closed-loop analysis.
Abstract: In real-world applications, industrial cranes commonly suffer from effects caused by the so-called double-pendulum phenomenon in many situations. However, at present, the double-pendulum phenomenon is usually directly roughly neglected when designing control methods. For double-pendulum cranes, most currently available approaches are open loop control; the existing feedback methods are mostly developed based on linearized dynamic models (around the equilibrium point) or designed without adding integral terms in the control laws, which may cause positioning errors in the presence of unmodeled dynamics. To address these problems, this paper proposes a new quasi-proportional integral derivative control method to effectively control underactuated double-pendulum crane systems. Then, we provide rigorous theoretical analysis for the equilibrium point of the closed-loop system based on the original nonlinear dynamic equations. To our knowledge, this paper gives the first plant-parameter-free controller that incorporates both integral action and actuating constraints without any linearizing operations during controller design or closed-loop analysis, which theoretically ensures that the controller can work well in the presence of unmodeled dynamics (e.g., insufficient friction compensation), actuating constraints, and large swing angles (i.e., not satisfying linearization conditions). Finally, hardware experimental results are provided to examine the effectiveness of the suggested control method.

170 citations


Journal ArticleDOI
Yuan Cao1, Wang Zhengchao1, Feng Liu1, Peng Li1, Guo Xie 
TL;DR: This paper presents a novel approach for speed curve seeking and tracking control, and presents the random reinforcement genetic algorithm (GA) algorithm to avoid the local optimum efficiently and a sliding mode controller is developed for speed curves tracking with bounded disturbance.
Abstract: Operation optimization for modern subway trains usually requires the speed curve optimization and speed curve tracking simultaneously. For the speed curve optimization, a multi-objective seeking issue should be addressed by considering the requirements of energy saving, punctuality, accurate parking, and comfortableness at the same time. But most traditional searching methods lack in efficiency or tend to fall into the local optimum. For the speed curve tracking, the widely applied proportional integral differential (PID) and fuzzy controllers rely on complicated parameter tuning, whereas robust adaptive methods can hardly ensure the finite-time convergence strictly, and thus are not suitable for applications in fixed time intervals of trains. To address the above-mentioned two problems, this paper presents a novel approach for speed curve seeking and tracking control. First, we present the random reinforcement genetic algorithm (GA) algorithm to avoid the local optimum efficiently. Then, a sliding mode controller is developed for speed curve tracking with bounded disturbance. The Lyapunov theory is adopted to prove that the system can be stabilized in the finite time. Finally, using the real datasets of Yizhuang Line in Beijing Subway, the proposed approach is validated, demonstrating its effectiveness and superiorities for the operation optimization.

167 citations


Journal ArticleDOI
TL;DR: A model predictive control strategy without using any proportional–integral–derivative (PID) regulators is proposed and shows better performance, which is validated in simulation based on a 3.5-MW PV-wind-battery system with real-world solar and wind profiles.
Abstract: In renewable energy systems, fluctuating outputs from energy sources and variable power demand may deteriorate the voltage quality. In this paper, a model predictive control strategy without using any proportional–integral–derivative (PID) regulators is proposed. The proposed strategy consists of a model predictive current and power (MPCP) control scheme and a model predictive voltage and power (MPVP) control method. By controlling the bidirectional dc–dc converter of the battery energy storage system based on the MPCP algorithm, the fluctuating output from the renewable energy sources can be smoothed while stable dc-bus voltage can be maintained. Meanwhile, the ac/dc interlinking converter is controlled by using the MPVP scheme to ensure stable ac voltage supply and proper power flow between the microgrid and the utility grid. Then, a system-level energy management scheme is developed to ensure stable operation under different operation modes by considering fluctuating power generation, variable power demand, battery state of charge, and electricity price. Compared with the traditional cascade control, the proposed method is simpler and shows better performance, which is validated in simulation based on a 3.5-MW PV-wind-battery system with real-world solar and wind profiles.

166 citations


Journal ArticleDOI
TL;DR: The numerical simulations of the proposed ChASO-FOPID and ASO-fOPID controllers for the dc motor speed control system demonstrated the superior performance of both the chaotic ASO and the original ASO, respectively.
Abstract: In this paper, atom search optimization (ASO) algorithm and a novel chaotic version of it [chaotic ASO (ChASO)] are proposed to determine the optimal parameters of the fractional-order proportional+integral+derivative (FOPID) controller for dc motor speed control. The ASO algorithm is simple and easy to implement, which mathematically models and mimics the atomic motion model in nature, and is developed to address a diverse set of optimization problems. The proposed ChASO algorithm, on the other hand, is based on logistic map chaotic sequences, which makes the original algorithm be able to escape from local minima stagnation and improve its convergence rate and resulting precision. First, the proposed ChASO algorithm is applied to six unimodal and multimodal benchmark optimization problems and the results are compared with other algorithms. Second, the proposed ChASO-FOPID, ASO-FOPID, and ASO-PID controllers are compared with GWO-FOPID, GWO-PID, IWO-PID, and SFS-PID controllers using the integral of time multiplied absolute error (ITAE) objective function for a fair comparison. Comparisons were also made for the integral of time multiplied squared error (ITSE) and Zwe-Lee Gaing's (ZLG) objective function as the most commonly used objective functions in the literature. Transient response analysis, frequency response (Bode) analysis, and robustness analysis were all carried out. The simulation results are promising and validate the effectiveness of the proposed approaches. The numerical simulations of the proposed ChASO-FOPID and ASO-FOPID controllers for the dc motor speed control system demonstrated the superior performance of both the chaotic ASO and the original ASO, respectively.

156 citations


Journal ArticleDOI
TL;DR: This paper proposes a robust tracking output-control strategy for a quadrotor under the influence of external disturbances and uncertainties that is composed of a finite-time sliding-mode observer, which estimates the full state from the measurable output and identifies some types of disturbances.
Abstract: The design of robust tracking control for quadrotors is an important and challenging problem nowadays. In this paper, a robust tracking output-control strategy is proposed for a quadrotor under the influence of external disturbances and uncertainties. Such a strategy is composed of a finite-time sliding-mode observer, which estimates the full state from the measurable output and identifies some types of disturbances. It is also composed of a combination of PID controllers and continuous sliding-modes controllers that robustly track a desired time-varying trajectory with exponential convergence despite the influence of external disturbances and uncertainties. The closed-loop stability is provided based on the input-to-state stability (ISS) and finite-time ISS properties. Finally, experimental results in real time show the performance of the proposed control strategy.

143 citations


Journal ArticleDOI
TL;DR: The main objective of the proposed approach is to optimize the transient response of the AVR system by minimizing the maximum overshoot, settling time, rise time and peak time values of the terminal voltage, and eliminating the steady state error.
Abstract: This paper proposes a novel tuning design of proportional integral derivative (PID) controller via an improved kidney-inspired algorithm (IKA) with a new objective function. The main objective of the proposed approach is to optimize the transient response of the AVR system by minimizing the maximum overshoot, settling time, rise time and peak time values of the terminal voltage, and eliminating the steady state error. After obtaining the optimal values of the three gains of the PID controller (KP, KI, and KD) with the proposed approach, the transient response analysis was performed and compared with some of the current heuristic algorithms-based approaches in literature to show the superiority of the optimized PID controller. In order to evaluate the stability of the automatic voltage regulator (AVR) system tuned by IKA method, the pole/zero map analysis and Bode analysis are performed. Finally, the robustness analysis of the proposed approach has been carried out with variations in the parameters of the AVR system. The numerical simulation results demonstrated that the proposed IKA tuned PID controller has better control performances compared to the other existing approaches. The essence of the presented study points out that the proposed approach may successfully be applied for the AVR system.

138 citations


Journal ArticleDOI
TL;DR: The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot / undershoot and the increased number of iterations which outperformed the other optimization algorithms based controller.
Abstract: Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control, automatic generation control ( AGC ) plays a crucial role. In this paper, multi-area ( Five areas: area 1, area 2, area 3, area 4 and area 5 ) reheat thermal power systems are considered with proportional-integral-derivative ( PID ) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm ( FFA ). The experimental results demonstrated the comparison of the proposed system performance ( FFA-PID ) with optimized PID controller based genetic algorithm ( GA-PID ) and particle swarm optimization ( PSO ) technique ( PSO-PID ) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error ( ITAE ) cost function with one percent step load perturbation ( 1 % SLP ) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot / undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.

127 citations


Journal ArticleDOI
TL;DR: An adaptive droop scheme for dc microgrids to overcome the non-linearity of the system is presented and the sliding mode control, which is distinguished by robustness and fast dynamic response, is utilized to manipulate the output voltage and the input current of each converter, instantaneously.
Abstract: One of the most widely used techniques for controlling the dc microgrid is the droop control method. The associated problems of the droop-based systems, such as the current sharing errors and the voltage deviation are solved using current sharing loops and secondary control loop, respectively. This paper presents an adaptive droop scheme for dc microgrids to overcome the non-linearity of the system. The droop resistance is adjusted using the adaptive PI controller to eliminate the current sharing error of each unit in the microgrid. In addition, another adaptive PI controller is dedicated for the secondary loop to regulate the dc bus voltage of the microgrid by shifting the droop lines. In the proposed scheme, only the current and voltage at the dc bus of the microgrid need to transmit through low-bandwidth communication channels to individual units. Moreover, the sliding mode control, which is distinguished by robustness and fast dynamic response, is utilized to manipulate the output voltage and the input current of each converter, instantaneously. The dynamic performance of the proposed adaptive droop scheme is evaluated using the PSCAD/EMTDC simulation package.

Journal ArticleDOI
TL;DR: Results show that it is superior to pure proportional–integral (PI) controller and even PI controller combined with inverse compensator in the sense that the root mean square, relative, as well as maximal absolute errors of output tracking have been decreased remarkably within five iterations.
Abstract: Rate-dependent hysteretic nonlinearity, which is an inherent characteristic of piezoelectric actuators (PEAs), causes a significant challenge in precise motion control of piezoelectric nanopositioning stages. In this paper, by assuming that the model of PEA takes a Hammerstein structure, a novel control strategy that combines iterative learning control (ILC) and the direct inverse of hysteresis is proposed to compensate for both nonlinearities and uncertainties of system simultaneously. Different from those existing direct inverse compensation methods whose control performance highly relies on the accuracy of the hysteresis model, the proposed control strategy is more robust by adding an additional ILC loop. Since ILC is essentially a feedforward control scheme that fully utilizes the input and output information in previous iterations, the tracking precision can be improved promptly in the iteration domain. Comparative experiments are performed to test the efficacy of the proposed algorithm for polynomial, triangular, and step signals. Results show that it is superior to pure proportional–integral (PI) controller and even PI controller combined with inverse compensator in the sense that the root mean square, relative, as well as maximal absolute errors of output tracking have been decreased remarkably within five iterations.

Journal ArticleDOI
TL;DR: In this paper, a salp swarm algorithm (SSA) was used to fine-tune the gains of proportional-integral-derivative (PID) controllers of load frequency control (LFC) of a multi-area hybrid renewable nonlinear power system.

Journal ArticleDOI
TL;DR: This brief aims to show that a linear proportional–integral–derivative (PID) controller is theoretically valid for tracking control of robotic manipulators driven by compliant actuators.
Abstract: This brief aims to show that a linear proportional–integral–derivative (PID) controller is theoretically valid for tracking control of robotic manipulators driven by compliant actuators. The control problem is formulated into a three-time-scale singular perturbation formula, including a slow time scale at the rigid robot dynamics, one actual fast time scale at the actuator dynamics, and another virtual fast time scale at the controller dynamics. A PID-type controller is derived to guarantee semiglobal practical exponential stability of the rigid robot dynamics, and a derivative-type controller is applied to establish global exponential stability of the actuator dynamics. Based on a state transformation to the closed-loop rigid robot dynamics and the extended Tikhonov’s theorem, it is proven that the entire system has semiglobal practical exponential stability under a proper choice of control parameters. The proposed controller is not only structurally simple and model-free resulting in low implementation cost, but also robust against external disturbances and parameter variations. The current design is only valid while the spring stiffness is relatively large compared with other parameters of the robot dynamics. Experimental results based on a single-link compliant robotic manipulator have verified effectiveness of the proposed approach.

Journal ArticleDOI
TL;DR: Whale optimization algorithm (WOA) technique is used in this paper to tune the controllers’ parameters and shows its superiority through comparison with several optimization techniques.

Proceedings ArticleDOI
TL;DR: In this article, a deep reinforcement learning (DRL) controller is proposed to handle the nonlinear attitude control problem, enabling extended flight envelopes for fixed-wing UAVs.
Abstract: Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far more limited in their flight envelope as compared to experienced human pilots, thereby restricting the conditions UAVs can operate in and the types of missions they can accomplish autonomously. This paper proposes a deep reinforcement learning (DRL) controller to handle the nonlinear attitude control problem, enabling extended flight envelopes for fixed-wing UAVs. A proof-of-concept controller using the proximal policy optimization (PPO) algorithm is developed, and is shown to be capable of stabilizing a fixed-wing UAV from a large set of initial conditions to reference roll, pitch and airspeed values. The training process is outlined and key factors for its progression rate are considered, with the most important factor found to be limiting the number of variables in the observation vector, and including values for several previous time steps for these variables. The trained reinforcement learning (RL) controller is compared to a proportional-integral-derivative (PID) controller, and is found to converge in more cases than the PID controller, with comparable performance. Furthermore, the RL controller is shown to generalize well to unseen disturbances in the form of wind and turbulence, even in severe disturbance conditions.

Journal ArticleDOI
TL;DR: The impact of CES units in automatic generation control (AGC) of interconnected power system is analysed and contrasted critically, and the proposed approach asserts better and vigorous results to supply reliable and high-quality electric power to the end user.

Journal ArticleDOI
TL;DR: The paper addresses the leader tracking problem for a platoon of connected autonomous vehicles in the presence of both homogeneous time-varying parameter uncertainties and vehicle-to-vehicle time-Varying communication delay with a novel distributed robust proportional-integral-derivative control framework.
Abstract: The paper addresses the leader tracking problem for a platoon of connected autonomous vehicles in the presence of both homogeneous time-varying parameter uncertainties and vehicle-to-vehicle time-varying communication delay. To this aim, leveraging the multiagent systems (MAS) framework, a novel distributed robust proportional-integral-derivative control is proposed. The stability of the cohesive formation is analytically proved with a Lyapunov–Krasovskii approach by exploiting the descriptor transformation for time-delayed systems of neutral type. The delay-dependent robust stability conditions are expressed as a set of linear matrix inequalities allowing the proper tuning of the proportional, integral, and derivative actions implemented on each of the vehicles within the fleet. Extensive simulation analysis in different driving scenarios confirms the effectiveness of the theoretical derivation.

Journal ArticleDOI
TL;DR: Investigation affirms that the dynamic performance of the systems with FTIDF-II controller improves further in the presence of HAE-FC units and sensitivity analysis demonstrated that the proposed controller is robust and executes competently at variations in the system parameters and random load perturbations.

Journal ArticleDOI
TL;DR: The obtained results confirmed the accuracy and reliability of the proposed approach in designing LFC for multi-interconnected power systems.
Abstract: This paper proposes optimal load frequency control (LFC) designed by Adaptive Neuro Fuzzy Inference System (ANFIS) trained via antlion optimizer (ALO) for multi-interconnected system comprising renewable energy sources (RESs). Two systems are modeled and investigated; the first one has two plants of grid connected photovoltaic (PV) system with maximum power point tracker (MPPT) and thermal plant while the second comprises four plants of thermal, wind turbine and grid connected PV systems. ALO is employed to get the optimal gains of Proportional-Integral (PI) controller such that the integral time absolute error (ITAE) of frequency and tie line power deviations is minimized. The input and output of the optimized PI controller are used to train the ANFIS-LFC with Gaussian surface membership functions. Different load disturbances are studied and the results are compared with other reported approaches. The obtained results confirmed the accuracy and reliability of the proposed approach in designing LFC for multi-interconnected power systems.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a new control and power management strategy for a grid-connected microgrid, which includes a hybrid renewable energy sources (HRES) system and a three-phase load.

Journal ArticleDOI
TL;DR: This methodology integrates the previously separated components, such as the profile generator, state observer, feedback controller, feedforward terms, and disturbance rejection, into one unified structure, thus making it compatible with standard industrial control function blocks and enhancing its market competitiveness.

Journal ArticleDOI
TL;DR: It has been observed that the designed hIFA-PS based fuzzy logic PID controller performs satisfactorily with varied conditions and the superiority of proposed AGC approach over some recently published AGC approaches is also demonstrated.
Abstract: This article deals with frequency control of five area power systems employing a technique which based on the hybridization of improved Firefly optimization Algorithm and Pattern Search technique (hIFA-PS) to tune the parameters of fuzzy aided PID controller. The performance of original firefly algorithm (FA) is improved by adding memory, newborn fireflies and using a new updating formula of fireflies which eliminate the wandering movement of the fireflies during the iteration process. The proposed hIFA-PS technique gets the benefits of FA's global explore capability and local search ability of PS. At first, an interconnected five area thermal power system with appropriate Generation Rate Constraints (GRC) and Dead Bands (DB) is considered and the integral constants are optimized by FA. To demonstrate the superiority of the proposed hIFA-PS algorithm results are compared with other soft computing approaches. To improve the dynamic performance, different controller structures are considered and a comparative study of hIFA-PS optimized I/PI/PID/Fuzzy aided PID is presented. The proposed design method is also applied to a five area ten unit system consisting of diverse generation sources such as thermal, hydro, wind, diesel, gas turbine. Performance analysis of the designed controller has been carried out for different system parameters and loading conditions. It has been observed that the designed hIFA-PS based fuzzy logic PID controller performs satisfactorily with varied conditions. The superiority of proposed AGC approach over some recently published AGC approaches is also demonstrated.

Journal ArticleDOI
TL;DR: This paper develops explicit expressions of the exact delay margin and its upper bounds achievable by a PID controller for low-order delay systems, notably the first- and second-order unstable systems with unknown constant and possibly time-varying delays.
Abstract: This paper concerns the delay margin achievable using proportional-integral-derivative (PID) controllers for linear time-invariant (LTI) systems subject to variable, unknown time delays. The basic issue under investigation addresses the question: What is the largest range of time delay so that there exists a single PID controller to stabilize the delay plants within the entire range? Delay margin is a fundamental measure of robust stabilization against uncertain time delays and poses a fundamental, longstanding problem that remains open except in simple, isolated cases. In this paper, we develop explicit expressions of the exact delay margin and its upper bounds achievable by a PID controller for low-order delay systems, notably the first- and second-order unstable systems with unknown constant and possibly time-varying delays. The effect of nonminimum phase zeros is also examined. PID controllers have been extensively used to control and regulate industrial processes that are typically modeled by first- and second-order dynamics. While furnishing the fundamental limits of delay within which a PID controller may robustly stabilize a delay process, our results should also provide useful guidelines in tuning PID parameters and in the analytical design of PID controllers.

Journal ArticleDOI
TL;DR: The proposed AFPID controller optimized by ISCA is used for the load frequency control of the autonomous power generating system and the results show that the ISCA tunedAFPID controller has superior performance over conventional PID controller.
Abstract: An autonomous power generation system contains numerous autonomous generation units like diesel energy generator, solar photovoltaic units, wind turbine generator, fuel cells along with energy storing units such as the flywheel energy storage system and battery energy storage system. These renewable sources are typically varying in nature. Therefore, the system components either run at lower/higher power output or may turned on/off at different instant of their operation. Due to the above mentioned uncertainties, the conventional controllers are not able to provide desired performance under varied operating conditions. Owing to this challenge, this paper proposes an adaptive fuzzy logic PID controller (AFPID) optimized by improved sine cosine algorithm (ISCA) for the load frequency control (LFC) of an autonomous power generation system. Proposed ISCA algorithm is evaluated using standard test functions and compared with original sine cosine algorithm (SCA) to authenticate the competence of algorithm. It is found from the statistical results that the proposed ISCA algorithm outperform original SCA, Hybrid Improved Firefly-Pattern Search, gravitational search, and grey wolf optimization algorithms. The proposed AFPID controller optimized by ISCA is used for the load frequency control of the autonomous power generating system. The results show that the ISCA tuned AFPID controller has superior performance over conventional PID controller. The proposed AFPID controller is again examined by the sensitivity analysis by introducing different hybrid power system parameters and the robustness of the control approach with the dynamic change of power system parameters is evaluated. Finally, the stability of the proposed control system is tested using Eigen value analysis.

Journal ArticleDOI
TL;DR: Fuzzy Particle Swarm Optimization of PID controller PSO-FPIDC used as a Conventional Power System Stabilizer CPSS to improve the dynamic stability performance of generating unit during low frequency oscillations.
Abstract: This article presents Fuzzy Particle Swarm Optimization of PID controller PSO-FPIDC used as a Conventional Power System Stabilizer CPSS to improve the dynamic stability performance of generating unit during low frequency oscillations. Speed deviation Δw and acceleration Δẇ of synchronous generator are taken as input to the PSO-FPIDC controller connected to Single Machine Infinite Busbar SMIB system. This controller examined under different perturbation scenarios. The dynamic performance of the PSO-FPIDC is compared with the Fuzzy Teacher Learner Based Optimization PID TLBO-FPIDC, PSO-PID, TLBO-PID and optimal parameters of convectional Power System Stabilizer CPSS. The results show that the performance of PSO-FPIDC has small overshoot/undershoot and damp out lower frequency oscillations very quickly as compared to other controllers.

Journal ArticleDOI
TL;DR: A new Predictive Functional Modified PID (PFMPID) controller is proposed that the effectiveness of this controller is verified compared to the traditional one and Grasshopper Optimization Algorithm (GOA) is proposed as a suitable solution to optimize and demonstrate the superiority of the proposed control method.

Journal ArticleDOI
TL;DR: The proposed design approach is simple and it provides a convenient method to properly determine the adaptive PI controller parameters and Representative simulation and experimental results are presented and discussed in order to show the effectiveness of the proposed dc-link voltage controller.
Abstract: Conventionally, standard proportional and integral (PI) controllers with constant PI gains are commonly used for the dc-link voltage control of single-phase grid-connected converters (GCCs). For such controllers, the selection of the PI gains will lead to a tradeoff between two control objectives: 1) the reduction of the dc-link voltage fluctuations caused by random swings of the active power drawn by the single-phase GCC; and 2) the reduction of the grid current harmonics mainly caused by the 2 f oscillation of the active power in single-phase applications. To solve this tradeoff, this paper presents a systematic approach for the design of an adaptive PI controller for the dc-link voltage control of single-phase GCCs. The proposed design approach is simple and it provides a convenient method to properly determine the adaptive PI controller parameters. Representative simulation and experimental results are presented and discussed in order to show the effectiveness of the proposed dc-link voltage controller.

Journal ArticleDOI
TL;DR: A novel fuzzy proportional integral derivative (PID) controller with filtered derivative action and fractional order integrator (fuzzy PIλDF controller) is proposed to solve automatic generation control (AGC) problem in power system by cuckoo optimization algorithm (COA).
Abstract: In this article, a novel fuzzy proportional integral derivative (PID) controller with filtered derivative action and fractional order integrator (fuzzy PIλDF controller) is proposed to solve automa...

Journal ArticleDOI
TL;DR: Results indicate that the designed robot system can achieve the waist rehabilitation training and the control algorithm can improve the system performance under the external disturbance.
Abstract: This paper proposes a cable-driven parallel waist rehabilitation robot with two-level control algorithm to assist the patients with waist injuries to do some rehabilitation training. The uniqueness of the robot is that it can accurately implement the relative lateral bending, flexion, extension, and rotation of the waist on the premise of the safety. This is enabled by a mechanism design according to the motion characteristics of the human waist, and it can satisfy the need of different waist injury patients. The kinematics and dynamics of the robot are analyzed. In addition, a two-level controller is introduced to improve the accuracy of the rehabilitation training trajectory on the premise of the safety of patients and reduce the system calculation. The proportion-integration-differentiation (PID) algorithm is adopted to realize the position control in the low-level controller to ensure the accuracy of the robot. In the high-level controller, the fuzzy algorithm is used to adjust the parameters of the low-level controller according to the tension variation of cables, which ensures the patient safety and the stable operation of the robot. Finally, a prototype waist rehabilitation robot is developed for experimental calibration and performance testing. Results indicate that the designed robot system can achieve the waist rehabilitation training and the control algorithm can improve the system performance under the external disturbance.