Showing papers on "PID controller published in 2020"

Tuning of Digital PID Controllers Using Particle Swarm Optimization Algorithm for a CAN-Based DC Motor Subject to Stochastic Delays

Abstract: In this article, we investigate the tuning problem of digital proportional-integral-derivative (PID) parameters for a dc motor controlled via the controller area network (CAN). First, the model of the dc motor is presented with its parameters being identified with experimental data. By studying the CAN network characteristics, we obtain the CAN-induced delays related to the load rate and the priorities. Then, considering the system model, the network properties, and the digital PID controller, the tuning problem of PID parameters for the CAN-based dc motor is transformed into a design problem of a static-output-feedback controller for a time-delayed system. To solve this problem, particle swarm optimization algorithm and linear-quadratic-regulator method are adopted by incorporating the sufficient condition of time-varying delay system. Finally, the effectiveness of the proposed PID tuning strategy is validated by experimental results.

Responsive Safety in Reinforcement Learning by PID Lagrangian Methods

Abstract: Lagrangian methods are widely used algorithms for constrained optimization problems, but their learning dynamics exhibit oscillations and overshoot which, when applied to safe reinforcement learning, leads to constraint-violating behavior during agent training. We address this shortcoming by proposing a novel Lagrange multiplier update method that utilizes derivatives of the constraint function. We take a controls perspective, wherein the traditional Lagrange multiplier update behaves as \emph{integral} control; our terms introduce \emph{proportional} and \emph{derivative} control, achieving favorable learning dynamics through damping and predictive measures. We apply our PID Lagrangian methods in deep RL, setting a new state of the art in Safety Gym, a safe RL benchmark. Lastly, we introduce a new method to ease controller tuning by providing invariance to the relative numerical scales of reward and cost. Our extensive experiments demonstrate improved performance and hyperparameter robustness, while our algorithms remain nearly as simple to derive and implement as the traditional Lagrangian approach.

Whale optimizer algorithm to tune PID controller for the trajectory tracking control of robot manipulator

Abstract: In this work, we are interested to the PID control of nonlinear systems and more specially the control of a robot manipulator. The idea is to determine the optimal parameters ($$K_{p}, K_{i}$$ and $$K_{d}$$) of the controller using a novel algorithm of optimization called whale optimizer algorithm (WOA). To study the effectiveness of WOA-PID controller, its performance is compared with other controllers such as particle swarm optimization-PID (PSO-PID) and grey wolf optimizer-PID (GWO-PID). The model of robot manipulator and all controllers were tested using Simulink/MATLAB. Simulation results obtained clearly indicate the superiority of WOA-PID controller over the other controllers for trajectory tracking, better settling time, and ITAE errors.

Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control

Abstract: Wireless sensor networks (WSNs) act as a building block of Internet of Things and have been used in various applications to sense environment and transmit data to the Internet. However, WSNs are very vulnerable to congestion problem, resulting in higher packet loss ratio, longer delay and lower throughput. To address this issue, this paper presents a fuzzy sliding mode congestion control algorithm (FSMC) for WSNs. Firstly, by applying the signal-to-noise ratio of wireless channel to TCP model, a new cross-layer congestion control model between transmission layer and MAC layer is proposed. Then, by combining fuzzy control with sliding mode control (SMC), a fuzzy sliding mode controller (FSMC) is designed, which adaptively regulates the queue length of buffer in congested nodes and significantly reduces the impact of external uncertain disturbance. Finally, numerous simulations are implemented in MATLAB/Simulink and NS-2.35 by comparing with traditional control strategies such as fuzzy, PID and SMC, which show that the proposed FSMC effectively adapts to the change of queue length and has good performance, such as rapid convergence, lower average delay, less packet loss ratio and higher throughput.

Self-tuning fuzzy PID-nonsingular fast terminal sliding mode control for robust fault tolerant control of robot manipulators

Abstract: In this work, a new robust controller is developed for robot manipulator based on an integrating between a novel self-tuning fuzzy proportional-integral-derivative (PID)-nonsingular fast terminal sliding mode control (STF-PID-NFTSM) and a time delay estimation (TDE). A sliding surface based on the PID-NFTSM is designed for robot manipulators to get multiple excited features such as faster transient response with finite time convergence, lower error at steady-state and chattering elimination. However, the system characteristics are hugely affected by the selection of the PID gains of the controller. In addition, the design of the controller requires an exact dynamics model of the robot manipulators. In order to obtain effective gains for the PID sliding surface, a fuzzy logic system is employed and in order to get an estimation of the unknown dynamics model, a TDE algorithm is developed. The innovative features of the proposed approach, i.e., STF-PID-NFTSM, is verified when comparing with other up-to-date advanced control techniques on a PUMA560 robot.

State Damping Control: A Novel Simple Method of Rotor UAV With High Performance

Abstract: Sliding Mode Control and Adaptive Control are widely studied in the area of Rotor UAV in recent years. Although the performance of Rotor UAV with these controllers show high command tracking ability and good robustness, they are limited by model accuracy so that they cannot take place PID. In this paper, a novel method named State damping control is proposed to be a candidate for the traditional PID method. Our proposed State Damping Control is inspired by the format of air resistance. The method is based on the general idea that resistance will make a system easy to stabilize. State damping control is independent of model accuracy and just uses three parameters to control attitude, so it is easy to realize. Krasovskii Theorem is used to give the evidence that State damping control is asymptotic stable in our considered state space. Finally, simulations are implemented in C++ on VS2017, it demonstrates that State damping control is easy to be tuned and robust to wind attack and inertial parameters. Compared with PID, our proposed method is robust to wind disturbances obviously.

PSO-GWO Optimized Fractional Order PID Based Hybrid Shunt Active Power Filter for Power Quality Improvements

Abstract: This paper presents a Hybrid Shunt Active Power Filter (HSAPF) optimized by hybrid Particle Swarm Optimization-Grey Wolf Optimization (PSO-GWO) and Fractional Order Proportional-Integral-Derivative Controller (FOPIDC) for reactive power and harmonic compensation under balance and unbalance loading conditions. Here, the parameters of FOPID controller are tuned by PSO-GWO technique to mitigate the harmonics. Comparing Passive with Active Filters, the former is tested to be bulky and design is complex and the later is not cost effective for high rating. Hence, a hybrid structure of shunt active and passive filter is designed using MATLAB/Simulink and in real time experimental set up. The compensation process for shunt active filter is different from predictable methods such as (p-q) or (i d -i q ) theory, in which only the source current is to be sensed. The performance of the proposed controller is tested under different operating conditions such as steady and transient states and indices like Total Harmonic Distortion (THD), Input Power Factor (IPF), Real Power (P) and Reactive Power (Q) are estimated and compared with that of other controllers. The parameters of FOPIDC and Conventional PID Controller (CPIDC) are optimized by the techniques such as PSO, GWO and hybrid PSO-GWO. The comparative simulation/experiment results reflect the better performance of PSO-GWO optimized FOPIDC based HSAPF with respect to PSO/GWO optimized FOPIDC/CPIDC based HSAPF under different operating conditions.

Frequency and voltage stabilisation in combined load frequency control and automatic voltage regulation of multiarea system with hybrid generation utilities by AC/DC links

Abstract: This article investigates the combined analysis of load frequency control (LFC) and Automatic voltage regulation (AVR) for two-area hybrid system. Classical PID controller is habituated as secondar...

Robust PID control of quadrotors with power reduction analysis.

Abstract: This paper presents a novel robust controller applied to a quadrotor vehicle for regulation and trajectory tracking tasks. In the proposed scheme, the quadrotor position is controlled by a proportional integral derivative (PID) controller, while the orientation control is achieved through a model-based controller. The proposed controller is combined with a power reduction methodology, which includes a controller-gains tuning stage using the cuckoo search algorithm, and a minimum jerk trajectory design stage. The performance of the new controller is assessed in a free-disturbance case and under the effect of parametric uncertainty and aero-dynamical disturbances. The new controller is compared against two linear PID controllers and a nonlinear sliding mode-based controller. Numerical simulations demonstrate the superiority of the proposed scheme as well as its robustness against different types of perturbations. Also, it is proven that the power demanded by any controller is reduced when using the power reduction methodology.

A Hankel Matrix Based Reduced Order Model for Stability Analysis of Hybrid Power System Using PSO-GSA Optimized Cascade PI-PD Controller for Automatic Load Frequency Control

Abstract: This paper presents the automatic load frequency control (ALFC) of two-area multisource hybrid power system (HPS). The interconnected HPS model consists of conventional and renewable energy sources operating in disparate combinations to balance the generation and load demand of the system. In the proffered work, the stability analysis of nonlinear dynamic HPS model was analyzed using the Hankel method of model order reduction. Also, an attempt was made to apply cascade proportional integral - proportional derivative (PI-PD) control for HPS. The gains of the controller were optimized by minimizing the integral absolute error (IAE) of area control error using particle swarm optimization-gravitational search algorithm (PSO-GSA) optimization technique. The performance of cascade control was compared with other classical controllers and the efficiency of this approach was studied for various cases of HPS model. The result shows that the cascade control produced better transient and steady state performances than those of the other classical controllers. The robustness analysis also reveals that the system overshoots/undershoots in frequency response pertaining to random change in wind power generation and load perturbations were significantly reduced by the proposed cascade control. In addition, the sensitivity analysis of the system was performed, with the variation in step load perturbation (SLP) of 1% to 5%, system loading and inertia of the system by ±25% of nominal values to prove the efficiency of the controller. Furthermore, to prove the efficiency of PSO-GSA tuned cascade control, the results were compared with other artificial intelligence (AI) methods presented in the literature. Further, the stability of the system was analyzed in frequency domain for different operating cases.

On the characteristics of ADRC: a PID interpretation

Abstract: Dear editor, This study reports the discovery of active disturbance rejection control (ADRC). Second-order linear ADRC (LADRC), the basic and most popular ADRC, can be interpreted as a modified proportional-integral-derivative (PID) control. LADRC filters the PID feedback compensator with a second-order low-pass filter; it also adds a pre-filter. Simultaneously, each given PID controller can be viewed as a special case of a secondorder LADRC feedback compensator whose observer bandwidth is positive infinity.

An adaptive deep reinforcement learning approach for MIMO PID control of mobile robots.

Abstract: Intelligent control systems are being developed for the control of plants with complex dynamics. However, the simplicity of the PID (proportional-integrative-derivative) controller makes it still widely used in industrial applications and robotics. This paper proposes an intelligent control system based on a deep reinforcement learning approach for self-adaptive multiple PID controllers for mobile robots. The proposed hybrid control strategy uses an actor-critic structure and it only receives low-level dynamic information as input and simultaneously estimates the multiple parameters or gains of the PID controllers. The proposed approach was tested in several simulated environments and in a real time robotic platform showing the feasibility of the approach for the low-level control of mobile robots. From the simulation and experimental results, our proposed approach demonstrated that it can be of aid by providing with behavior that can compensate or even adapt to changes in the uncertain environments providing a model free unsupervised solution. Also, a comparative study against other adaptive methods for multiple PIDs tuning is presented, showing a successful performance of the approach.

Jaya optimization algorithm for transient response and stability enhancement of a fractional-order PID based automatic voltage regulator system

Abstract: Considering the higher flexibility in tuning process and finer control action of the fractional-order proportional integral derivative (FOPID) controller over the conventional proportional integral derivative (PID) controller, this paper explores its application in the automatic voltage regulator system. FOPID contains five tuning parameters as compared to three in the conventional PID controller. The additional tuning knobs in FOPID provide increased control flexibility and precise control action, however, their inclusion makes the tuning process more complex and tedious. Thus, the intelligence of an artificial intelligence (AI) technique called jaya optimization algorithm (JOA) is utilized in order to obtain an optimal combination of FOPID gains which further led to the optimal transient response and improved stability of the considered AVR system. To validate the performance superiority of the proposed approach its corresponding system’s dynamic response is compared with that of the other well-known AI-based approaches explored in recent literature. Furthermore, the stability study of the proposed AVR system is carried out by evaluating its pole/zero and bode maps. Finally, the robustness of the proposed optimized AVR system against the system’s parameter variation is evaluated by varying the time constants of all the four components of AVR (generator, exciter, amplifier and sensor) from −50% to +50% independently. The proposed algorithm based FOPID tuning technique provides 59.82%, 56.09%, 14.94%, 34.24%, 35.70%, 21.64%, 12.0%, 41.33%, 14.84% and 15.17% reduced overshoot than that of differential evolution (DE), particle swarm optimization (PSO), Artificial Bee Colony (ABC), Bibliography Based Optimization (BBO), Grasshopper Optimization Algorithm (GOA), Pattern Search Algorithm (PSA), Improved Kidney Inspired Algorithm (IKA), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA) and Local Unimodal Sampling (LUS) algorithm respectively, thus validates its competence and effectiveness.

Load mitigation of a class of 5-MW wind turbine with RBF neural network based fractional-order PID controller.

Abstract: In variable-pitch wind turbines, pitch angle control is implemented to regulate the rotor speed and power production. However, mechanical loads of the wind turbines are affected by the pitch angle adjustment. To improve the performance and at the same time alleviate the mechanical loads, a gain-scheduling fractional-order PID (FOPID), where a trained RBF neural network chooses its parameters is proposed. The database, which the RBF neural network is trained based on, is created via optimization of a FOPID in several wind speeds with chaotic differential evolution (CDE) algorithm. The simulation results are compared to an RBF based PID controller that is designed via the same method, a conventional gain-scheduling baseline PI controller developed by NREL, an optimal RBF based PI controller, and a FOPI controller. The simulations indicate that the RBF based FOPID improves the control performance of the benchmark wind turbine in comparison to the other controllers, while the applied loads to the structure are mitigated. To validate the performance and robustness, all controllers are implemented on FAST wind turbine simulator. The superiority of the proposed FOPID controller is depicted in comparison to the other controllers.

Energy-Storage-Based Intelligent Frequency Control of Microgrid With Stochastic Model Uncertainties

Abstract: With the increasing proportion of renewable power generations, the frequency control of microgrid becomes more challenging due to stochastic power generations and dynamic uncertainties. The energy storage system (ESS) is usually used in microgrid since it can provide flexible options to store or release power energy. In this paper, an intelligent control strategy completely based on the adaptive dynamic programming (ADP) is developed for the frequency stability, which is designed to adjust the power outputs of micro-turbine and ESS when photovoltaic (PV) power generation is connected into the microgrid. Further, considering the changes of PV power and load demand in a day, the full utilization of PV power and the recycling of energy storage are realized through the proposed regulation strategy. Numerical simulation results validate the energy-storage-based intelligent frequency control strategy for the microgird with stochastic model uncertainties, and comparative studies based on PID, LQR and fuzzy logic control illustrate the superiority of the proposed control strategy.

A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization

Abstract: The design of a swarm optimization-based fractional control for engineering application is an active research topic in the optimization analysis. This work offers the analysis, design, and simulation of a new neural network- (NN) based nonlinear fractional control structure. With suitable arrangements of the hidden layer neurons using nonlinear and linear activation functions in the hidden and output layers, respectively, and with appropriate connection weights between different hidden layer neurons, a new class of nonlinear neural fractional-order proportional integral derivative (NNFOPID) controller is proposed and designed. It is obtained by approximating the fractional derivative and integral actions of the FOPID controller and applied to the motion control of nonholonomic differential drive mobile robot (DDMR). The proposed NNFOPID controller’s parameters consist of derivative, integral, and proportional gains in addition to fractional integral and fractional derivative orders. The tuning of these parameters makes the design of such a controller much more difficult than the classical PID one. To tackle this problem, a new swarm optimization algorithm, namely, MAPSO-EFFO algorithm, has been proposed by hybridization of the modified adaptive particle swarm optimization (MAPSO) and the enhanced fruit fly optimization (EFFO) to tune the parameters of the NNFOPID controller. Firstly, we developed a modified adaptive particle swarm optimization (MAPSO) algorithm by adding an initial run phase with a massive number of particles. Secondly, the conventional fruit fly optimization (FFO) algorithm has been modified by increasing the randomness in the initialization values of the algorithm to cover wider searching space and then implementing a variable searching radius during the update phase by starting with a large radius which decreases gradually during the searching phase. The tuning of the parameters of the proposed NNFOPID controller is carried out by reducing the MS error of 0.000059, whereas the MSE of the nonlinear neural system (NNPID) is equivalent to 0.00079. The NNFOPID controller also decreased control signals that drive DDMR motors by approximately 45 percent compared to NNPID and thus reduced energy consumption in circular trajectories. The numerical simulations revealed the excellent performance of the designed NNFOPID controller by comparing its performance with that of nonlinear neural (NNPID) controllers on the trajectory tracking of the DDMR with different trajectories as study cases.

Generalized Homogeneity in Systems and Control

Abstract: This monograph introduces the theory of generalized homogeneous systems governed by differential equations in both Euclidean (finite-dimensional) and Banach/Hilbert (infinite-dimensional) spaces. It develops methods of stability and robustness analysis, control design, state estimation and discretization of homogeneous control systems. Generalized Homogeneity in Systems and Control is structured in two parts. Part I discusses various models of control systems and related tools for their analysis, including Lyapunov functions. Part II deals with the analysis and design of homogeneous control systems. Some of the key features of the text include: - mathematical models of dynamical systems in finite-dimensional and infinite-dimensional spaces; - the theory of linear dilations in Banach spaces; - homogeneous control and estimation; - simple methods for an "upgrade" of existing linear control laws; - numerical schemes for a consistent digital implementation of homogeneous algorithms; and - experiments confirming an improvement of PID controllers. The advanced mathematical material will be of interest to researchers, mathematicians working in control theory and mathematically oriented control engineers.

An Explicit Nonlinear Model Predictive ABS Controller for Electro-Hydraulic Braking Systems

Abstract: This paper addresses the development and Hardware-in-the-Loop (HiL) testing of an explicit nonlinear model predictive controller (eNMPC) for an antilock braking system (ABS) for passenger cars, actuated using an electro-hydraulic braking unit. The control structure includes a compensation strategy to guard against the performance degradation due to actuation dead times, identified by experimental tests. The eNMPC is run on an automotive rapid control prototyping unit, which shows its real-time capability with comfortable margin. A validated high-fidelity vehicle simulation model is used for the assessment of the ABS on a HiL rig equipped with the braking system hardware. The eNMPC is tested in seven emergency braking scenarios, and its performance is benchmarked against a proportional-integral-derivative (PID) controller. The eNMPC results show: 1) the control system robustness with respect to the variations in tire-road friction condition and initial vehicle speed; and 2) consistent and significant improvement of the stopping distance and wheel slip reference tracking, with respect to the vehicle with the PID ABS.

An Adaptive Super Twisting Nonlinear Fractional Order PID Sliding Mode Control of Permanent Magnet Synchronous Motor Speed Regulation System Based on Extended State Observer

Abstract: In this article, a novel adaptive super-twisting nonlinear Fractional-order PID sliding mode control (ASTNLFOPIDSMC) strategy using extended state observer (ESO) for the speed operation of permanent magnet synchronous motor (PMSM) is proposed. Firstly, this paper proposes a novel nonlinear Fractional-order PID (NLFOPID) sliding surface with nonlinear proportion term, nonlinear integral term and nonlinear differential term. Secondly, the novel NLFOPID switching manifold and an adaptive super-twisting reaching law (ASTRL) are applied to obtain excellent control performance in the sliding mode phase and the reaching phase, respectively. The novel ASTNLFOPIDSMC strategy is constructed by the ASTRL and the NLFOPID sliding surface. Due to the utilization of NLFOPID switching manifold, the characteristics of fast convergence, good robustness and small steady state error can be ensured in the sliding mode phase. Due to the utilization of ASTRL, the chattering phenomenon can be weakened, and the characteristics of high accuracy and strong robustness can be obtained in the reaching phase. Further, an ESO is designed to achieve dynamic feedback compensation for external disturbance. Furthermore, Lyapunov stability theorem and Fractional calculus are used to prove the stability of the system. Finally, comparison results under different controllers demonstrate that the proposed control strategy not only achieves good stability and dynamic properties, but also is robust to external disturbance.

An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems

Abstract: Multi-area power systems inhere complicated nonlinear response, which results in degraded performance due to the insufficient damping. The main causes of the damping problems are the stochastic behavior of the renewable energy sources, loading conditions, and the variations of system parameters. The load frequency control (LFC) represents an essential element for controlling multi-area power systems. Therefore, the proper design of the controllers is mandatory for preserving reliable, stable and high-quality electrical power. The controller has to suppress the deviations of the area frequency in addition to the tie-line power. Therefore, this paper proposes a new frequency regulation method based on employing the hybrid fractional order controller for the LFC side in coordination with the fractional order proportional integral derivative (FOPID) controller for the superconducting energy storage system (SMES) side. The hybrid controller is designed based on combining the FOPID and the tilt integral derivative (TID) controllers. In addition, the controller parameters are optimized through a new application of the manta ray foraging optimization algorithm (MRFO) for determining the optimum parameters of the LFC system and the SMES controllers. The optimally-designed controllers have operated cooperatively and hence the deviations of the area frequency and tie-line power are efficiently suppressed. The robustness of the proposed controllers is investigated against the variation of the power system parameters in addition to the location and/or magnitude of random/step load disturbances.

PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator.

Abstract: A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.