Intelligent PID Controller Research Articles

Learning from NN-based extended PID control for a class of high-order uncertain nonlinear systems

As is well known, traditional PID controllers are the most widely used type of controllers in industrial control due to their simplicity and other advantages. However, the existing research results on PID controllers lack a general theory for addressing the nonlinearity and uncertainty present in practical systems. In the era of intelligent control, it is highly valuable to design an intelligent PID controller with learning capability for complex unknown nonlinear systems. In this paper, we consider an extended form of PID controller based on deterministic learning (DL) for high-order nonlinear systems with unknown dynamics. The introduction of neural networks (NNs) provides an effective tool to compensate for the composite unknown nonlinearities in controlled systems, overcoming the limitations of traditional PID controllers relying solely on feedback mechanisms to combat unknown nonlinearities. Based on the Lyapunov theory, it not only ensures the stability and tracking control of the system but also simplifies the selection of controller parameters. Furthermore, based on the DL theory, the composite unknown nonlinear dynamics can be accurately compensated and approximated, and the acquired knowledge can be stored in a constant-value neural network. When encountering similar control tasks, the acquired knowledge can be quickly accessed to construct a knowledge-based extended PID controller, thereby improving the overall control performance. Simulation results demonstrate the effectiveness of the proposed algorithm.

Reliable identification based intelligent PID tuning for long-period process control under different working conditions

BackgroundChemical process models change frequently with operating conditions. However, the industry mostly adopts an off-line calibration and online fixed value commissioning scheme, which can be difficult to maintain control quality. Therefore, a long-cycle online intelligent PID controller calibration scheme is necessary. MethodsFirstly, a high-order linear dynamic model is used as the identification model, and online recursive identification technology is utilized. Secondly, a comprehensive performance index is selected to ensure both stability and rapidity of dynamic transitions. Finally, the slow rate updated PID parameters are obtained during the process control, and the Levy Memory Particle Swarm Optimization (LMPSO) search is applied to harvest the optimal solution. Significant findingsThis paper has presented an intelligent PID tuning method based on reliable identification technology. The main contributions include: (i) By using Lyapunov theory, the boundedness of identification error using the proposed algorithm is guaranteed; (ii) The LMPSO algorithm is proposed to enhance global search capability and search efficiency; (iii) A novel optimization scheme is proposed for a full-process online closed-loop intelligent PID controller. The scheme aims to improve the industrial controller's performance without altering its structure.

MRAC based intelligent PID controller design technique for a class of dynamical systems

MRAC based intelligent PID controller design technique for a class of dynamical systems

A Proposed Controller for Pitch Angle of Wind Turbine

Wind turbines are complicated non-linear systems with certain random disruptions. The pitch control system is a commonly employed method for regulating the electricity generated by a wind turbine. Many researchers have observed developments in the pitch control field during the last few decades. Traditional PID controllers have the drawback of being slow or imprecise when wind and pitch angles suddenly change. These drawbacks can be solved with artificial intelligent algorithms. However, the algorithms' design and implementation are highly complex. A new pitch-regulated variable-speed control strategy for wind turbines to address their nonlinear properties is presented. To manage the pitch system's control mechanisms with disturbances, this research evolved a mathematical model that illustrates HAWT's pitch angle control system and applied a proposed Simple Optimal Intelligent PID Controller (SOI-PID). Under various operating conditions, the proposed SOI-PID controller was tested with the Traditional PID, Fuzzy Logic Controller (FLC), and Fuzzy-Adaptive-PID controller. For system simulation, the MATLAB/Simulink software was used. According to simulation results, compared to PID, FLC, and Fuzzy-Adaptive-PID controllers, the proposed SOI-PID controller responds faster and has a better rise and settling time. Other benefits of the SOI-PID controller are its simplicity of implementation and design, distinguishing it from other intelligent algorithms.

Intelligent Tuning PID Controller, Simulation and Comparison for a Quadrotor

Intelligent Tuning PID Controller, Simulation and Comparison for a Quadrotor

Design and Control of Hydraulic Power Take-Off System for an Array of Point Absorber Wave Energy Converters

The development of wave energy converter (WEC) arrays is an effective way to reduce the cost of levelized energy and facilitate the commercialization of WECs. This study proposes a hydraulic power take-off (PTO) system for an array of point absorber wave energy converters (PA-WECs) and designs a control system using a novel algorithm called the improved simplified universal intelligent PID (ISUIPID) controller and the adaptive matching controller including an improved artificial gorilla troops optimizer (IGTO) to improve and stabilize the output power of PA-WEC arrays. Simulations under varying irregular wave states have been carried out to verify the validity of the mathematical model and the control system. The results show that the designed IGTO has faster convergence speed and better convergence accuracy in solving the optimal linear damping coefficient of the generator, and the proposed ISUIPID controller provides superior performance in tracking the speed of the hydraulic motor under the changing sea states. In addition, the capture power and output power of the array of PA-WECs are improved and the electrical energy can be output stably under the designed control system. The array of PA-WECs with the proposed control system will become an independent, stable, efficient, and sustainable power supply system.

A framework to develop and test a model-free motion control system for a forestry crane

This article has the objective of presenting our method to develop and test a motion control system for a heavy-duty hydraulically actuated manipulator, which is part of a newly developed prototype featuring a fully-autonomous unmanned forestry machine. This control algorithm is based on functional analysis and differential algebra, under the concepts of a new type of approach known as model-free intelligent PID control (iPID). As it can be unsafe to test this form of control directly on real hardware, our main contribution is to introduce a framework for developing and testing control software. This framework incorporates a desktop-size mockup crane equipped with comparable hardware as the real one, which we design and manufactured using 3D-printing. This downscaled mechatronic system allows to safely test the implementation of control software in real-time hardware directly on our desks, prior to the actual testing on the real machine. The results demonstrate that this development framework is useful to safely test control software for heavy-duty systems, and it helped us present the first experiments with the world’s first unmanned forestry machine capable of performing fully autonomous forestry tasks.

Shifting asymmetric time-varying BLF-based model-free hybrid force/position control for 3-DOF SEA-based manipulator with random initial error

In this work, a shifting asymmetric time-varying barrier Lyapunov function (BLF)-based model-free hybrid force/position controller (SABMHC) is proposed for the endpoint of the 3-DOF series elastic actuator (SEA)-based manipulator which can be used for machining of large curved surfaces. The proposed SABMHC contains a force sub-control loop and a position sub-control loop. The force sub-control loop converts the force control into position control based on the idea of impedance control, and realizes the hybrid force/position control through the position sub-control. Then, in order to achieve accurate trajectory tracking control of the position sub-control loop, a shifting asymmetric time-varying BLF-based model-free controller is proposed, which includes the error shifting function, asymmetric time varying BLF, time-delay estimation (TDE) technique, and adaptive compensation term. To prevent the random initial error from being so large that it exceeds the pre-set boundary and affects the steady-state response, an error shifting function is introduced. The error shifting function can make the shifting error independent of the tracking error before reaching the pre-set shifting time, so as to achieve better control performance by setting a small pre-set boundary for the shifting error. With the Lyapunov function, the designed SABMHC can ensure that the stability and the shifting error converges within the pre-set boundary in a finite time. Finally, the effectiveness and benefits of the proposed SABMHC are analyzed by comparison with the TDE-based intelligent PID (TDE-iPD) and BLF-based sliding-mode controller (BLF-SMC).

Comparative Performance Analysis of Intelligent PID Controllers for a Mechatronic System

Abstract Applications of systems engineering in the automotive field have grown over the past two decades. This paper aims at a Model-Free Control (MFC) strategy for this type of application. It is a control method in the Data Driven Control (DDC) category that is suitable for systems with complex dynamics, uncertainties or dynamics that are difficult to model or with an unavailable model. Considering these reasons, MFC is an appropriate control method applicable to the complex dynamics of automotive systems. This paper presents a comparative analysis of two types of MFC controllers: intelligent proportional (iP) and intelligent proportional derivative (iPD) for a mechatronic system application. The system takes into account the longitudinal speed control of the vehicle. First, some authors results on the design of iP and iPD controllers are reviewed. Then, the intelligent controllers are tested in simulation considering the requirement to maintain the desired vehicle speed under different traffic conditions such as urban and extra-urban driving cycles. The control performances of the iP and iPD controllers are compared using different performance indices, with a classical PID controller added to the comparison.

Adaptive Neural Network Global Fractional Order Fast Terminal Sliding Mode Model-Free Intelligent PID Control for Hypersonic Vehicle’s Ground Thermal Environment

In this paper, an adaptive neural network global fractional order fast terminal sliding mode model-free intelligent PID control strategy (termed as TDE-ANNGFOFTSMC-MFIPIDC) is proposed for the hypersonic vehicle ground thermal environment simulation test device (GTESTD). Firstly, the mathematical model of the GTESTD is transformed into an ultra-local model to ensure that the control strategy design process does not rely on the potentially inaccurate dynamic GTESTD model. Meanwhile, time delay estimation (TDE) is employed to estimate the unknown terms of the ultra-local model. Next, a global fractional-order fast terminal sliding mode surface (GFOFTSMS) is introduced to effectively reduce the estimation error generated by TDE. It also eliminates arrival time, accelerates the convergence speed of the sliding phase, guarantees finite time arrival, avoids the singularity phenomenon, and bolsters robustness. Then, as the upper bound of the disturbance error is unknown, an adaptive neural network (ANN) control is designed to approximate the upper bound of the estimation error closely and mitigate the chattering phenomenon. Furthermore, the stability of the control system and the convergence time are proven by the Lyapunov stability theorem and are calculated, respectively. Finally, simulation results are conducted to validate the efficacy of the proposed control strategy.

Intelligent Control of a Space Manipulator Ground Unfold Experiment System with Lagging Compensation

In ground testing of space manipulators, gravity compensation is a critical testing requirement. The objective of this paper was to design a space manipulator gravity compensation test platform for ground tests and solve the problems of force control oscillation and precision degradation caused by the execution lag encountered in the development process. An intelligent PID controller was designed for this active-suspension gravity compensation experimental mechanism of a space manipulator on the ground, and a specially designed second-order method was used to solve the problem of the execution lag in this mechanism. The intelligent controller was developed based on adaptive dynamic programming and redesigned to improve its transient performance. The simulation was carried out, and its results were compared with the results on a real machine to demonstrate the effectiveness of this set of experimental controllers. This paper compares in detail the results of the designed method on system input and output and shows the effectiveness of this method in dealing with the execution lag of the mechanism. In conclusion, in this work, we successfully designed and implemented an intelligent PID controller for an active-suspension gravity compensation experimental mechanism of a space manipulator on the ground, and the experimental results demonstrate the effectiveness of the proposed method.

Intelligent PID control of an industrial electro-hydraulic system

In this study, an intelligent PID (i-PID) controller is designed for position control of a nonlinear electro-hydraulic system with uncertain valve characteristics and supply pressure variations. The proposed controller uses estimation of ultra-local model of the system. To exhibit the capability of the proposed method, the controller parameters are optimized via the particle swarm optimization method through a nominal nonlinear model of the system. Then, the performance of the i-PID controller, parameters of which are optimized by using the nominal model, is examined under uncertainties caused by valve characteristics and supply pressure variations. Moreover, the friction between the piston and the hydraulic cylinder is also considered in experiments. A PID controller whose parameters are also optimized based on the same performance criteria, is used for comparison purposes with i-PID control both in simulations and experiments. Performance metrics of the controllers are examined and presented by employing two separate reference signals: Sine wave and ramp. The results show that the i-PID controller shows significantly better results than the classical PID controller in tracking the test signals under various supply pressures and valve modes.

Design of an intelligent wavelet-based fuzzy adaptive PID control for brushless motor

Design of an intelligent wavelet-based fuzzy adaptive PID control for brushless motor

Dynamic Parameter Identification for Intelligent PID Control

In this paper, an intelligent PID control structure using dynamic parameter is designed to solve the problem that the parameters of the aircraft model change, which results in controller performance degradation in real physical systems. The system dynamics parameters are identified by the deep neural network, and the parameters of the PID controller are adaptively scheduled based on the parameter identification results, so that the control system has the best matching with the system dynamics and the control performance is the best.

Intelligent Adaptive PID Control for the Shaft Speed of a Marine Electric Propulsion System Based on the Evidential Reasoning Rule

To precisely and timely control the shaft speed for a marine electric propulsion system under normal sea conditions, a new shaft speed control technique combining the evidential reasoning rule with the traditional PID controller was proposed in this study. First, an intelligent adaptive PID controller based on the evidential reasoning rule was designed for a marine electric propulsion system to obtain the PID parameters KP, KI, and KD. Then, a local iterative optimization strategy for model parameters was proposed. Furthermore, the parameters of the adaptive PID controller model were optimized in real time by using the sequential linear programming algorithm, which enabled the adaptive adjustment of KP, KI, and KD. Finally, the performance of the adaptive PID controller regarding the shaft speed control was compared with that of other controllers. The results showed that the adaptive PID controller designed in this study had better control performance, and the shaft speed control method based on the adaptive PID controller could better control the shaft speed of the marine electric propulsion system.

Designs of Particle-Swarm-Optimization-Based Intelligent PID Controllers and DC/DC Buck Converters for PEM Fuel-Cell-Powered Four-Wheeled Automated Guided Vehicle

For automatic guided vehicles (AGVs), maximizing the operating time with maximum energy efficiency is the most important factor that increases work efficiency. In this study, the fuel-cell-powered AGV (FCAGV) system was modeled and optimized control and design were carried out to obtain high tracking performance with minimum power consumption. Firstly, a full mathematical model of FCAGV, which involves the AGV, the fuel cell, DC/DC converters and motors, was obtained. Then, particle swarm optimization (PSO)-based intelligent PID and I controllers were developed for maximizing the route-tracking performance of AGV and voltage-tracking performance of the DC/DC converter with reduced power consumption. PSO was used to determine the optimal parameters of controllers and the values of DC/DC converters’ components. The performance of the full AGV system was analyzed for different paths. The results show that the sufficient path-tracking and voltage-tracking performance was obtained for AGV and DC/DC converters, respectively. The average tracking errors according to global coordinate system are 0.0061 m at the x axis, 0.0572 m at the y axis and 0.0228 rad at rotational axis. The obtained average voltage-tracking errors for each DC/DC converters were approximately 0.8033 V. These results indicate that the developed controllers with optimal coefficients work successfully with small voltage and path-tracking errors. During this motion, the average consumed power from the fuel cell was observed as 58.2675 W. These results show that the designed optimized intelligent controllers have sufficient performance with high energy efficiency and maximum route tracking.

A Proposed Controller for an Autonomous Vehicles Embedded System

Many research have observable development in the automated vehicle driving field during the last few decades. This research proposed a simple optimum Intelligent PID (SO PID) controller to simplify the automated vehicle motion control. Control of an autonomous vehicle’s steering routines plays an essential key role. Several steering control procedures are proposed that improve automated vehicle performance. The design of secure embedded control systems must overcome the difficulties associated with designing both computing and control systems. Also, this research introduces a model of the autonomous car prototype controlled via an Arduino microcontroller board and the GPS Module to receive the car coordinates. The car moves safely, and autonomously consequently avoiding the risk of human faults. Several algorithms such as angle and distance calculations to the waypoint and obstacle detection are combined to control the car movement.

Hardware-in-the-loop testing of simple and intelligent MPPT control algorithm for an electric vehicle charging power by photovoltaic system

In this paper, an electric vehicle (EV) charging station powered by a photovoltaic (PV) system has been created to charge EVs. Furthermore, the maximum available power from the PV system was extracted using a new algorithm called the simplified universal intelligent PID (SUIPID) controller, which has the advantages of simplicity in design and intelligence. The SUIPID controller was compared to the artificial neural networks (ANNs), the fuzzy logic controller (FLC) based on linear membership functions, and the FLC based on particle swarm optimization (PSO) to optimize its parameters under various conditions. The system simulation was first performed using MATLAB software, then an experimental set up was conducted to confirm the theoretical work. The proposed SUIPID responds 28.5%, 44.4%, and 61.5% faster than the PSO-FLC, ANN, and FLC, respectively, whereas the ITAE error was reduced by 27.3%, 52.9%, and 65.2%, respectively. Also, the charging procedure of the EV battery is precisely steady under different atmospheric conditions. The SUIPID controller has several advantages over other intelligent algorithms, most notably its ease of design and implementation.

Modeling and Hybrid PSO-WOA-Based Intelligent PID and State-Feedback Control for Ball and Beam Systems

Modeling and Hybrid PSO-WOA-Based Intelligent PID and State-Feedback Control for Ball and Beam Systems

Experimental Modeling of a New Multi-Degree-of-Freedom Fuzzy Controller Based Maximum Power Point Tracking from a Photovoltaic System

Conventional control methods, which follow the maximum power point (MPP), suffer from being slow or inaccurate during sudden changes in irradiance and temperature. These problems can be solved using artificial intelligence algorithms. The current study proposes a new multi-degree-of-freedom (MDOF) fuzzy logic controller (FLC) for maximizing the overall output performance of a photovoltaic system. The MDOF-FLC was compared to the simplified universal intelligent PID controller (SUI-PID) using the MDOF concept and the normal FLC. Simulation and experimental results show that the proposed MDOF-FLC controller has a 37.8% and 58.1% faster response with a better rise time compared to the SUIPID controller and the normal FLC, respectively. At the same time, the error, measured by the integral time absolute error (ITAE), was 29.4% and 62.5% lower, respectively.