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

Quadrotor is a key technology widely used in national defence, electric power and agriculture. In order to solve the problems of response hysteresis and sensitivity to sudden input changes of the conventional Proportional-Integral-Derivative (PID) control algorithm in quadrotor control, a filter is added to the outer differential term based on the conventional serial PID control to form an incomplete differential algorithm, which improves the dynamic characteristics of the system and reduces the sensitivity to sudden signal changes; at the same time, a filter is added based on the conventional serial PID control algorithm to form an incomplete differential algorithm, which improves the dynamic characteristics of the system and reduces the sensitivity to sudden signal changes. A filter is added based on the conventional series PID control algorithm to form an incomplete differential algorithm, which improves the dynamic characteristics of the system and reduces the sensitivity to abrupt signal changes; at the same time, a filter is added based on the conventional series PID control algorithm to achieve a smooth controller response curve. Simulation results show that compared with the conventional PID controller, the improved PID control algorithm reduces the maximum overshoot of the system step response from the original 8% to 3%, and its robustness and reliability are greatly improved.

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.

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.

A fractional-order fuzzy proportional integral derivative (PID) controller is a controller that combines the benefits of fractional calculus and fuzzy logic with the conventional PID controller. In this paper, a four-stage optimization algorithm is proposed for the design of a Type-2 Fuzzy fractional-order PID controller based on the Fourier Series Method (FSM). Three distinct control structures are introduced: Type-2 fuzzy fractional PD + fractional PI controller, Type-2 fuzzy fractional PID, and Type-2 fuzzy fractional PD + Type-2 fuzzy fractional PI controller. In addition to a modified multi-performance criterion cost function, four integral performance criteria are employed as cost functions for each stage. The suggested algorithm avoids the utilization of the approximation equivalent for the fractional-order system and instead employs FSM. Furthermore, the approach optimizes the nonlinearity within the upper membership function (UMF) and the uncertainty range through the lower membership function, as opposed to arbitrary selection. By considering variations in the membership functions, the outcomes exhibit a superior response compared to previous investigations. The results of the three control structures are compared with the traditional PID controller, and simulation results demonstrate the feasibility of this technique. The findings suggest that by optimizing different integral performance criteria using this design technique, controllers for both integer and fractional-order plants can yield favorable step responses. The proposed algorithm is validated by comparing its step response performance with that of previous research, followed by a discussion on sensitivity analysis and computational requirements.

Superheaters are heat exchangers that transfer energy from flue gas to superheated steam. Controlling of steam temperature at the superheater outlet is a very difficult task in thermal power plant. A superheater model and an appropriate optimal control strategy are the essential tools for improving the accuracy of this control system. Superheater is simulated as a unit of a control loop that generates steam of desired state values. To simulate the steam superheater on the computer, the heat exchanger assembly is described by sets of non-linear partial differential equations (PDE). The equations are then solved by modified finite difference method. A linearized model of the superheater is identified using system identification tool box in order to design optimal control strategies for Superheated Steam (SHS) temperature system. Then to tune Proportional Integral Derivative (PID) controller parameters optimally, different tuning schemes were applied to superheated steam temperature system. However, due to new challenges in keeping up with rapid changes in load demand and the transients, performance of the PID controller is far from being optimal. Hence a Model Predictive Controller (MPC) is designed for the superheated steam temperature regulation in a supercritical coal-fired power plant. Compared with the performance of the plant using a conventional PID controller, the steam temperature controlled by the MPC is found to be more optimal which leads to more efficient plant operation and energy saving.

Headbox is a critical sub-process of paper machine. Headbox is a two input and two output process with significant loop interaction. Quality of the paper produced depends on its operation. Hence, its precise control is required to produce good quality paper. This paper presents design of proportional integral derivative (PID) controller for headbox using particle swarm optimization (PSO) technique. Performance and robustness of PSO - PID controller have been compared with conventional PID controllers on the basis of time response, frequency response and performance indices (ISE, IAE, ITSE and ITAE).

In this paper, an optimal PID (proportional-integral-derivative) controller is designed for controlling the non-minimum phase system with time delay and performed greater than the conventional control approaches. When there is an increment in time delay then complications related to NMP system determines the undershoot response due to presence of zeros in the right half plane. Furthermore, Ziegler-Nichols, Internal Model Control (IMC) and Chien-Hrones & Reswick tuning approaches are developed for the conventional PID controller. The time-domain characteristics obtained are unable to persuade the requirements of the system. In order to improve the performance criteria, Smith’s predictor is used for controlling the NMP system. Smith’s predictor is better than the conventional PID controller techniques for tuning the parameters but has the incapability to achieve all the design requirements. For improving the performance requirements, the optimal PID controller is designed with the employment of Linear Quadratic Regulator (LQR). The tuning parameters of PID controller are optimal values using popular technique that is Linear Quadratic Regulator (LQR). In order to minimize integral time absolute error (ITAE) and integral time square error (ITSE), the tuning parameters are optimal and give precise values of rising time, peak time, % overshoot and settling time. The simulation results are obtained and according to that LQR gives better results than Smith Predictor and other conventional PID controller techniques.KeywordsNon-minimum phase systemPID controllerSmith predictorConventional tuning methodsLinear quadratic regulator

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.

Synthesis of a control system using a fuzzy inference is proposed for switching regulators. A computer simulation result is given, as an example, where an output voltage of a Cuk converter is regulated by using the proposed fuzzy inference. The simulated waveforms of the output voltage at start-up and the responses to disturbances (variations of the load resistance and of the input voltage) are studied, as compared with those of a conventional proportional integral derivative (PID) control. The variation of the output voltage is smaller and the output voltage settles faster than that using the PID control. >

A novel quasi‐oppositional‐based salp swarm algorithm (QSSA) is used to obtain the optimal values of controllers for automatic generation control of the two‐area, assorted source of the generation‐based power system. Thermal, hydro, and gas generating units are considered in the power system. Optimal values of proportional–integral–derivative (PID) controllers obtained using SSA and QSSA are compared with that of PID controllers based on differential evolution, teaching learning‐based optimisation, and imperialist competitive algorithm. Observing the superiority with a QSSA‐based PID controller, the study is protracted to obtain the optimal values of two‐degree‐of‐freedom conventional PID (2DOF‐PID) and 2DOF fractional order PID (2DOF‐FOPID) controller with SSA and QSSA techniques. It is evidenced that QSSA outperforms SSA and also the QSSA‐based 2DOF‐FOPID controller establishes a better dynamic response than other controllers. 2DOF‐FOPID controller is also employed for the system with generation rate constraint (GRC). The complete analysis is carried out by applying a step load disturbance of 1 p.u. in area‐1. Robustness of the controller is verified by varying the systems' parameters and a randomly varying loading pattern in both areas with and without GRC. In both cases, the proposed QSSA‐based 2DOF‐FOPID controller is found firmly robust.

The high-quality operation of a superheated steam temperature (SST) system is a core fact of the safety, economy, and stability of thermal power units. How to improve the control performance of an SST system under large-varying operating conditions is becoming a research hotspot. To solve this challenge, this paper proposes a proportional integral derivative (PID) control strategy based on integral gain scheduling. Based on the introduction of the SST system and classical model under typical operating conditions, the control difficulties of the SST system are analyzed theoretically. Then, a PID control strategy, based on integral gain scheduling, is introduced for the cascade control structure, and the stability of the proposed control strategy is analyzed by calculating the PID stability region. Finally, the effectiveness of the proposed method is verified under nominal and uncertain conditions, where the proposed method could obtain satisfactory tracking and disturbance rejection control performance. Simulation results show the valuable application prospects of the proposed method.

Since conventional PID (Proportional–Integral–Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.

Superheat steam temperature control is critical to the normal and optimal operation of a power plant. Usually, cascade proportional integral derivative (PID) control system is introduced to regulate the superheat temperature with the PID parameters fixed constant. However, Due to the gains and time constants of the controlled plant change significantly with disturbances, the control effects of the conventional PID are not ideal. In this paper, a new adaptive control strategy is proposed to improve conventional superheat steam temperature control. Based on cascade PID control system, recursive least squares (RLS) method is used to identify the plant parameters and then genetic algorithm (GA) is applied to adjust the parameters of the PID controllers. MATLAB simulation investigation indicates that, compared to the parameter fixed PID system, GA-self-tuned adaptive control system is able to reject endogenous and exogenous disturbances more effectively and rapidly, thus has better self-adaptation capability and robustness.

This paper presents the designing and simulation for vertical moving control of Unmanned Air Vehicles (UAV) system using Proportional Integral Derivative (PID) control method, Linear Quadratic Regulator (LQR) control method and fuzzy logic control technique.The UAV system pose a challenger control problem that in it precise model of system is non-linear. The conventional PID controller, the LQR controller, Fuzzy Logic Controller (FLC) and Self-Tuning Fuzzy PID controller are designned based on the dynamic modeling of the system is derived from a suitable mathematical model to describe the vertical motion of a UAV. Simulation results in Matlab and Simulink show that by using self-tuning Fuzzy PID, the performance of vertical moving control system is improved significantly compared to Other controllers. DOI: http://dx.doi.org/10.11591/ijece.v3i5.3440

Aiming at balancing and positioning of a new electro-hydraulic servo system with iso-actuation configuration, an extended state observer–based fractional order proportional–integral–derivative controller is proposed in this study. To meet the lightweight requirements of heavy barrel weapons with large diameters, an electro-hydraulic servo system with a three-chamber hydraulic cylinder is especially designed. In the electro-hydraulic servo system, the balance chamber of the hydraulic cylinder is used to realize active balancing of the unbalanced forces, while the driving chambers consisting of the upper and lower chambers are adopted for barrel positioning and dynamic compensation of external disturbances. Compared with conventional proportional–integral–derivative controllers, the fractional order proportional–integral–derivative possesses another two adjustable parameters by expanding integer order to arbitrary order calculus, resulting in more flexibility and stronger robustness of the control system. To better compensate for strong external disturbances and system nonlinearities, the extended state observer strategy is further introduced to the fractional order proportional–integral–derivative control system. Numerical simulation and bench test indicate that the extended state observer–based fractional order proportional–integral–derivative significantly outperforms proportional–integral–derivative and fractional order proportional–integral–derivative control systems with better control accuracy and higher system robustness, well demonstrating the feasibility and effectiveness of the proposed extended state observer–based fractional order proportional–integral–derivative control strategy.