Design Artificial Intelligent Parallel Feedback Linearization of PID Control with Application to Continuum Robot

Refer to this paper, an intelligent-fuzzy feed- forward computed torque estimator for Proportional- Integral-Derivative (PID) controller is proposed for highly nonlinear continuum robot manipulator. In the absence of robot knowledge, PID may be the best controller, because it is model-free, and its parameters can be adjusted easily and separately and it is the most used in robot manipulators. In order to remove steady- state error caused by uncertainties and noise, the integrator gain has to be increased. This leads to worse transient performance, even destroys the stability. The integrator in a PID controller also reduces the bandwidth of the closed-loop system. Model-based compensation for PD control is an alternative method to substitute PID control. Computed torque compensation is one of the nonlinear compensator. The main problem of the pure computed torque compensator (CTC) was highly nonlinear dynamic parameters which related to system's dynamic parameters in certain and uncertain systems. The nonlinear equivalent dynamic problem in uncertain system is solved by using feed-forward fuzzy inference system. To eliminate the continuum robot manipulator system's dynamic; Mamdani fuzzy inference system is design and applied to CTC. This methodology is based on design feed-forward fuzzy inference system and applied to CTC. The results demonstrate that the model base feed-forward fuzzy CTC estimator works well to compensate linear PID controller in presence of partly uncertainty system (e.g., continuum robot).

In order to improve the pressure tracking response of an artificial ventilator system, a novel proportional integral derivative (PID) controller is designed in the present work by utilizing an optimal rule-based fuzzy inference system (FIS) with a reshaped class-topper optimization algorithm (RCTO), which is named as (Fuzzy-PID). Firstly, a patient-hose blower-driven artificial ventilator model is considered, and the transfer function model is established. The ventilator is assumed to operate in pressure control mode. Then, a fuzzy-PID control structure is formulated such that the error and change in error between the desired airway pressure and actual airway pressure of the ventilator are set as inputs to the FIS. The gains of the PID controller (proportional gain, derivative gain, and integral gain) are set as outputs of the FIS. A reshaped class topper optimization algorithm (RCTO) is developed to optimize rules of the FIS to establish optimal coordination among the input and output variables of the FIS. Finally, the optimized Fuzzy-PID controller is examined for the ventilator under different scenarios such as parametric uncertainties, external disturbances, sensor noise, and a time-varying breathing pattern. In addition, the stability analysis of the system is carried out using the Nyquist stability method, and the sensitivity of the optimal Fuzzy-PID is examined for different blower parameters. The simulation results showed satisfactory results in terms of peak time, overshoot, and settling time for all cases, which were also compared with existing results. It is observed in the simulation results that the overshoot in the pressure profile is improved by 16% with the proposed optimal rule based fuzzy-PID as compared with randomly selected rules for the system. Settling time and peak time are also improved 60–80% compared to the existing method. The control signal generated by the proposed controller is also improved in magnitude by 80–90% compared to the existing method. With a lower magnitude, the control signal can also avoid actuator saturation problems.

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

This paper presents the effect of Variable Damping Ratio on design of Proportional Integral Derivative (PID) Controller. This novel method is very useful because of their successful practical application, robustness and simplicity. The most popular and widely used controllers in industries are PID controllers. To find appropriate parameters, many design methods have been proposed for PID controller with having its own merits and demerits. However, it is still not easy to find appropriate parameters for the PID controllers. In this paper, PID having variable Damping ratio is developed to improve the performance of the controller. By using this method there is a 60% saving in settling time as compared to the method proposed by Ziegler Nicholas and 84.6% saving settling time as compared to technique proposed by Salem. Our method is independent of maximum overshoot which is distinguishable advantage which increases the performance of system. Based on the mathematical model of the system, controller is designed and is estimated by the MATLAB version 2013. Data for model estimation is taken from an experimental works, the performance has improved significantly of the PID controller having variable damping ratio compare to conventional PID controller.

The increasing use of battery-powered electronic devices necessitates efficient and fast charging solutions. This research aims to optimize battery charging at a charging station using the Maximum Power Point Tracking (MPPT) method integrated with Proportional Integral Derivative (PID) control. This method is chosen to address overshoot issues and speed up the stabilization process. The study begins with a literature review to understand the basic theory of MPPT optimization and PID control. Subsequently, hardware and software for the battery charging station were designed and developed. An Arduino ATMega328 microcontroller was used as the main controller, with an LCD to display charging information. Tests were conducted on 12V batteries with capacities of 5Ah and 9Ah. The results showed that using PID control in MPPT optimization successfully increased battery charging efficiency. The fastest charging duration was achieved for a 12V 5Ah battery with parameters Kp=6, Ki=9, and Kd=4, where the battery was fully charged in 19 minutes, and for a 12V 9Ah battery with parameters Kp=5, Ki=3, Kd=1, where the battery was fully charged in 27 minutes. Conversely, the longest charging duration occurred for a 12V 5Ah battery with parameters Kp=5, Ki=3, Kd=1, where the battery was fully charged in 34 minutes, and for a 12V 9Ah battery with parameters Kp=2, Ki=5, Kd=1, with a charging time of 38 minutes. The designed system was able to reduce input current and increase input voltage to reach the 14.5V set point, ensuring that there was no excessive voltage and current that could prolong battery life. This research demonstrates that the PID method in MPPT optimization is effective in accelerating and optimizing battery charging. Future research is expected to further develop this method to improve battery charging efficiency and duration. Keywords: MPPT, PID, Optimization, Battery Charger, Optimal, PWM ABSTRAK Penggunaan perangkat elektronik yang bertenaga baterai semakin meningkat memerlukan solusi pengisian yang efisien dan cepat. Penelitian ini bertujuan untuk mengoptimalkan pengisian baterai pada stasiun pengisian dengan menggunakan metode Maximum Power Point Tracking (MPPT) yang diintegrasikan dengan kontrol Proportional Integral Derivative (PID). Metode ini dipilih untuk mengatasi masalah overshoot dan mempercepat waktu menuju keadaan stabil. Penelitian dimulai dengan studi literatur untuk memahami teori dasar optimasi MPPT dan kontrol PID. Selanjutnya dirancang dan dibuat perangkat keras serta perangkat lunak untuk stasiun pengisian baterai. Mikrokontroler Arduino ATMega328 digunakan sebagai pengendali utama dengan LCD untuk menampilkan informasi pengisian. Pengujian dilakukan pada baterai aki 12V dengan kapasitas 5Ah dan 9Ah. Hasil pengujian menunjukkan bahwa penggunaan kontrol PID pada optimasi MPPT berhasil meningkatkan efisiensi pengisian baterai. Durasi pengisian yang paling cepat dicapai baterai 12V 5ah dengan parameter Kp=6 Ki=9 dan Kd=4 dimana baterai terisi penuh dalam waktu 19 menit dan baterai 12V 9ah dengan parameter Kp=5, Ki=3, Kd=1 dimana baterai terisi penuh dalam waktu 27 menit. Sebaliknya durasi pengisian terlama terjadi pada baterai 12V 5ah dengan parameter Kp=5, Ki=3, Kd=1 baterai terisi penuh dalam waktu 34 menit dan baterai 12V 9ah dengan parameter Kp=2 Ki=5, Kd=1 dengan waktu pengisian 38 menit. Sistem yang dirancang mampu mengurangi arus input dan meningkatkan tegangan input untuk mencapai set point 14.5V menjaga agar tidak terjadi tegangan dan arus berlebih yang dapat memperpanjang umur baterai. Penelitian ini menunjukkan bahwa metode PID pada optimasi MPPT efektif dalam mempercepat dan mengoptimalkan pengisian baterai. Penelitian selanjutnya diharapkan dapat mengembangkan lebih lanjut metode ini untuk meningkatkan efisiensi dan durasi pengisian baterai. Kata kunci: MPPT, PID, Optimasi, Charger Baterai, Optimal, PWM

This chapter examines the proportional integral derivative (PID) controller design. It introduces the idea of disturbance estimation that leads to an equivalent PI control system. The mathematical model used in the chapter is a first order model. For systems with higher order transfer functions, approximation is required. To design a PID controller using the estimation based approach, a second order transfer function is considered. The chapter presents the MATLAB tutorial for the implementation of disturbance observer‐based PID controller. This implementation contains the anti‐windup mechanism when the control signal reaches its maximum or minimum values. The embedded PID controller via estimation is used in the simulation studies within the Simulink environment. The chapter also explains resonant controller design and implementation with anti‐windup mechanism using the disturbance estimation approach. It presents an example to illustrate the disturbance observer‐based resonant controller design.

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

The development of modern technology has made automation an important part of life. Proportion Integral Derivative (PID) control is a basic control theory of automation which has many applications because of its simple construction and great performance. This article introduces the application of PID control technology in the automotive field. This paper mainly introduces the following aspects: the basic principles and theories of PID control, the challenges of automotive systems and their control, some solutions for PID control in automotive systems, and the improvement and optimization of control. The limitation of the application of traditional control theory in new automotive control have led to the emergence of more optimization algorithms, which can improve the system in more ways. In the future, automotive control systems will continue to be based on PID control as the framework and combined with some optimized new algorithms for practical application. Therefore, understanding PID control is of great significance to clarify the future development of automotive control technology.

This paper presents a comparison of optimization for vehicle steering controls system simulation using several Artificial Intelligence (AI) for optimizing Proportional Integral Derivative (PID) control parameters to suppress errors on lateral motion and the yaw motion of vehicles. This paper compares five kinds of tuning methods of parameter for PID controller, among other are Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA) and Imperialist Competitive Algorithm (ICA). The vehicles are represented in the model vehicle with 10 degrees of Freedom of vehicle dynamics system. The simulation results show that the PID control tuned by AI in the vehicle steering control system can adjust the plant output to the desired trajectory so that the stability of the vehicle is maintained. Vehicle yaw error and lateral error can be reduced by using ICA to determine PID parameter. The main advantage of proposed optimization is faster and more accurate compared with PID controller. So the error of the controller is reduced too. The results obtained are of vehicle motion can be maintained in accordance with the desired trajectory with smaller error and was able to achieve higher speeds than with the control system using optimized without parameters. This paper only deals with software simulation to proof the effect of AI optimization. The hardware implementation will be investigated in the next future.

Remote areas need to prioritize the pumping of water for agricultural and drinking purposes. Water pumping is a promising and advantageous application of renewable energy sources, such as hybrid energy sources that combine photovoltaic and wind power. A novel approach to effective flow control in hybrid PV and wind-powered water pumping systems is presented in this research. To optimize a Proportional Integral Derivative (PID) controller that governs motor speed, the proposed approach combines an optimized control algorithm, Unified Power Flow Controller (UPFC), and Fuzzy Q-learning. The main goal is to use UPFC to identify and mitigate Power Quality (PQ) issues. To achieve optimal UPFC operation under a range of solar and wind energy conditions, the Kookaburra Optimization Algorithm (KOA) is utilized. Fuzzy Q-learning provides adaptive parameter tuning based on real-time feedback and system dynamics, enabling the PID controller to be tuned online. Moreover, the detailed models of wind and photovoltaic systems highlight their influence on water pumping technology. A PID controller controlled by the KOA algorithm powers the UPFC, a crucial part of the system. The KOA is applied to optimize PID parameters for the UPFC, resulting in a unique contribution that emulates the hunting behavior of kookaburras for both local and global exploration. The PID controller can be easily adjusted online by the Fuzzy Q-Learning algorithm, which improves system stability when reacting to changing energy and environmental conditions. The combination of the Kookaburra Optimization Algorithm, PID control, and UPFC offers a sophisticated and flexible approach to the best possible operation of water pumping systems. Total Harmonic Distortion (THD) is successfully decreased by the proposed method, indicating better waveform quality and fewer harmonic components in the electrical system. The system’s ability to maintain a high-quality power supply is reflected in the THD value of 3.92% at 50Hz.

The aim of this research is to develop PID (proportional integral derivative) controllers for a variable load two-area intelligent power system (multi-area intelligent power system) in the context of healthcare data analysis. PID controllers are used in the present healthcare business to improve the healthcare industry. It is still debatable how the settings should be changed to create a PID controller that is dependable, adaptable, and effective. In this research, a better PID gain is demonstrated using the particle swarm optimization (PSO) algorithm. The automatic generation control (AGC) for a two-area intelligent power system in healthcare industry has been built using a PID controller and a PSO-based method. To improve frequency response and settling time of healthcare industry equipment in accident and emergency cases (A and E), a PSO-based PID controller generates and transmits the two control signals to the two regions for the algorithm convergence in emergency cases as per MATLAB/Simulink result.

Mobile robots experience a lot of challenges with path tracking because of the significant problem with DC motor speed control. The particle swarm optimization (PSO) tuned proportional integral derivative (PID) controller is commonly used to control DC motor speed. However, it fails to reach the optimal solution quickly. Therefore, this paper demonstrates the application of the artificial bee colony (ABC) algorithm to tune a PID controller for speed regulation of a DC motor. The proposed approach was used to obtain the optimal PID controller gains (proportional, integral, and derivative) by minimizing the integral of time absolute error (ITAE) as the objective function. The analysis of statistical tests, transients, and frequency responses was conducted to test the rapid convergence of the proposed approach. Also, the performance of the proposed ABC-tuned PID controller has been compared with three variants of the PSO-tuned PID controller. The conducted simulation results and comparison with the proposed ABC controller and variant PSO controllers have shown that the proposed ABC-PID controller is more effective and robust than the variant PSO-PID controllers for the speed control of a DC motor.

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

Now -a-days PID (proportional-integral-derivative) controller is used in every type of small or large industries. For this the digital PID controller is so popular. But it is necessary to design and tune the PID controller in a proper method for applying the plant. The paper represents a method of tuning a digital proportional-integral-derivative (PID) controller. The name of the method is Steepest Gradient Descent Method (SDGM). It is an iterative method. The SDGM method is very simple, easy to use and each iteration is very fast. It is also a guaranteed method to find the minima through numerous times of iterations as long as it exists. By applying this optimization method PID controller is tuned both of digital and analog process. In the paper by applying the method of some plant the comparison result of analog and digital are shown. From the comparison it is found that the digital scheme is preferable than analog scheme. Index Terms—PID controller, SDGM. I. INTRODUCTION The fi rst controllers with proportional, integral, and derivative (PID) feedback control action became commercially available during the 1930s. The 1940s saw widespread acceptance in industry of pneumatic PID controllers, and their electronic counterparts entered the market in the 1950s. Digital hardware has been routinely used since the 1980s with significant impact on process control. Even several decades after three-mode controllers were introduced; the vast majority of controllers used in the chemical process industry are based on PI/PID models (1). The popularity of these controllers has led to research on tuning methods, resulting in hundreds of publications on this topic. Ziegler-Nichols tuning relations and Cohen-Coon tuning rules are among the earliest published methods. Tuning relations based on error criteria are more recent model-based tuning rules, which offer improvements over earlier tuning methods. Tuning rules also exist for unstable processes as well as for tuning in the presence of plant model mismatch (2). Despite the numerous approaches available for controller tuning, surveys indicate that poorly tuned control loops are abundant in industry. In general, the gain scheduling of the PID controllers is an optimization problem. The method of steepest descent (SDGM) is also known as The Gradient Descent, which is basically an optimization algorithm to find the local minimum of a function. By applying the SDGM to the PID controller, the controller is tuned and applied to some conventional plant then the digital and analog results are compared to the paper.

This paper investigates the integration of Fuzzy Logic with PID (Proportional, Integral, Derivative) controllers to enhance the performance of temperature control systems. Traditional PID controllers, widely used in various applications, often encounter difficulties with nonlinear, time-varying, and uncertain systems, resulting in performance issues like oscillations and overshoots. To mitigate these challenges, the adoption of Fuzzy PID control, which improves adaptability, robustness, and responsiveness by incorporating fuzzy logic into the control mechanism. The study involves simulations using Simulink to compare three distinct control models: conventional PID, standalone Fuzzy Control, and the hybrid Fuzzy PID Control across standardized control scenarios. Our results show that Fuzzy PID controllers significantly outperform traditional methods, especially in complex environments lacking precise mathematical modeling. The enhanced system response times and stability demonstrated by Fuzzy PID controllers suggest substantial potential for broader application across diverse industries, promising considerable improvements in operational efficiency and reliability.