Proportional Integral Derivative Control Law Research Articles

Intelligent demolition robot: Structural statics, collision detection, and dynamic control

This paper presents a modified oriented bounding box method and a compound control law for the developed intelligent demolition robot working safely and accurately in a high-radiation environment. Combining statics and kinematics, the modified oriented bounding box method is applied to predict the real-time and accurate collision detection of possible collisions. The Newton–Euler method is then adopted to derive the dynamic model. A compound control law based on dynamic feedforward torque and proportional integral derivative feedback torque is established to achieve high-accuracy control. Furthermore, a simulation system and experiment prototype are developed to verify the functions of collision detection and dynamic control. Simulation results demonstrate that the modified oriented bounding box method predicts the collision detection properly. Compared with the traditional proportional integral derivative control law, experimental results demonstrate that the compound control law realizes higher tracking accuracy within 0.04 m for the intelligent demolition robot.

Intelligent Adaptive PID Control Using Fuzzy Broad Learning System: An Application to Tool-Grinding Servo control Systems

This paper presents an intelligent adaptive proportional-integral-derivative (PID) control method using fuzzy broad learning system (FBLS) and investigates how the method can be applied to control a tool-grinding servo control (TGSC) system. Due to accuracy, quality and geometric errors which are often difficult to capture the dynamics of the controlled plants or systems, fixed-gain PID controllers without good three-term parameters cannot meet the stringent control performance specifications of nonlinear industrial systems and servomechanisms. To accomplish better control, an adaptive PID control strategy based on the FBLS, or abbreviated as FBLS-APPID, is rigorously proposed by integrating an online parameter learning FBLS identifier together with an adaptive predictive PID control law using FBLS, to eliminate tracking error and achieve fast-tracking and disturbance rejection. Numerical simulations on the two existing discrete-time nonlinear time-delay processes are performed to show the merits and superiority of the constructed FBLS-APPID by comparing to three existing adaptive PID methods. Finally, the applicability of the proposed method is well exemplified by conducting comparatively experimental results on a servo control loop of a real TGSC machine with fixed PID gains tuned by the proposed FBLS-APPID method.

Digital PID Control Law Design for Fuel Cell Model Based on FPGA Emulator System

This paper proposes an off-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. The aim of this research is to obtain the best hydrogen partial pressure (PH2) value using FPGA emulator to design and implement a digital PID controller that track the fuel cell output voltage during a variable load current applied. The off-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID controller parameters that improve the dynamic behavior of the closed loop digital control fuel cell system and to achieve the stability of the desired output voltage of fuel cell. The numerical simulation results (MATLAB) package and FPGA emulator experimental work show the performance of the proposed FPGA-PID controller in terms of voltage error reduction and generating optimal value of the (PH2) control action without oscillation in the output and no saturation state when these results are compared with other control methodology.

Real-time estimation of thermal boundary of unsteady heat conduction system using PID algorithm

Based on the principle of the proportional-integral-derivative (PID) control in feedback control theory, an inverse method for unsteady heat conduction process is established in this paper. The inverse problems of unsteady heat conduction are transformed into a kind of PID control problem, and the PID parameters are tuned by the complex optimization method. The deviation signal between the output of the direct heat conduction model and the measured temperature is used to drive the PID controller, and the boundary heat flux of the heat conduction system is estimated in real time through the PID control law. The numerical simulation results and experiment results show that the inverse algorithm has strong anti-interference ability.

Synthesis of a Proportional Integral Derivative control law based on the Meixner-like model

This paper proposes a new Proportional Integral Derivative (PID) control algorithm based on the Meixner-like model. The Meixner-like functions are an extension of Laguerre functions and are convenient when the system has a slow start. The parameters of the PID controller are auto adjusted by the model predictive control (MPC) technique. To improve system performance, the resolution of the problem of saturation of the control signal is proposed. A numerical example of a variable parameter system is given to illustrate the effectiveness of the proposed control approach.

On the Selection of a Control Law for Multiply Connected Inertial Objects

A comparative analysis of the control law applied to complex dynamic objects that exhibit drift and lag is performed. The selection of a proportional–integral–derivative control law based on transient processes with optimum performance indicators is evaluated. An estimate of the external disturbances of the system is given and the advantage of the selection arrived at is proved.

Actuation of Pneumatic Artificial Muscle via Hydrogen Absorption/Desorption of Metal Hydride-LaNi5

This paper presents experimental studies on mechanical actuations of a pneumatic artificial muscle (PAM), which is driven by hydrogen gas based metal hydride (MH). The dynamic performances of hydrogen absorption/desorption, taking place within a MH reactor, are controlled via implementing cooling/heating effects of a thermoelectric module (TEM). Hydrogen pressure is applied as a driving force to commanding work outputs of the PAM as desired mechanical actuations. Due to strong inherent nonlinearity, a conventional proportional integral derivative (PID) control law is not capable of regulating thermodynamic variables of the HM reaction according to desired performances of the PAM. In this study, the fuzzy adaptive PID control is proposed in manipulating the MH reaction via the TEM. This viability of the proposed methodology is confirmed by the fact that the gains of PID control law are adapted by fuzzy rule-based tuning scheme at various operating conditions of the MH reactor. The experimental results show that the proposed control technique is much more effective than a PID control in both transient and steady state performances of the MH reactor for servo mechanical actuation of the PAM.

PID and LQ Regulation of a Multirotor Attitude: Mathematical Modelling, Simulations and Experimental Results

Multirotors have been recognized to be particularly interesting for surveillance, search and rescue, imaging and more, because of their several advantages as the high maneuverability and the vertical take off and landing capability. This paper presents the experimental results of the application of a Proportional Integral Derivative control law and a Linear Quadratic Regulation technique to the attitude of a multirotor. The reader is driven through all the main steps required to reach this goal, starting from the derivation of the mathematical model and the control laws, to the simulations and the experimental tests.

Trajectory Tracking Error Using PID Control Law for Two-Link Robot Manipulator via Adaptive Neural Networks

This paper presents the application of adaptive neural networks to robot manipulator control. The main methodologies, on which the approach is based, are recurrent neural networks and Lyapunov functions methodology and Proportional-Integral-Derivative (PID) control for nonlinear systems. The proposed controller structure is composed of a neural identifier and a control law defined by using the PID approach. The proposed new control scheme is applied via simulations to control a robot manipulator two-link model. Experimental results in two degrees of freedom of the robot arm shown the usefulness of the proposed approach. To verify the analytical results, an example of dynamical network is simulated and a theorem is proposed to ensure the tracking of the nonlinear system.

Precise Position Synchronous Control of Two Axes Rotating Systems by Cooperative Control

This paper deals with a precise position synchronous control by a cooperative control method of two axes rotating systems. First, the system's dynamics including motor drives described by a motor circuit equation and Newton's kinetic formulation about rotating system. Next, based on conventional PID(Proportional, Integral, Derivative) control law, current and speed controller are designed very simply to follow up reference speed correctly under some disturbances. Also, position synchronous controller designed to minimize position errors according to integration of speed errors between two motors. Then, the proposed control enables the distributed drives by a software control algorithm to behave in a way as if they are mechanically hard coupled in axes. Further, the stabilities and robustness or the proposed system are investigated. Finally, the proposed system presented here is shown to be more precise position synchronous motion than conventional systems through some simulations and experiments.

Fuzzy PID controller: Design, performance evaluation, and stability analysis

This paper presents a design for a new fuzzy logic proportional-integral-derivative (PID) controller. The main motivation for this design was to control some known nonlinear systems, such as robotic manipulators, which violate the conventional assumption of the linear PID controller. This controller is developed by first describing the discrete-time linear PID control law and then progressively deriving the steps necessary to incorporate a fuzzy logic control mechanism into the modifications of the PID structure. The final version of this new fuzzy PID controller is a computationally efficient analytic scheme suitable for implementation in a real-time closed-loop digital control. Numerous computer simulations are included to demonstrate the effectiveness of the controller for both linear and nonlinear systems. Finally, a brief analysis is presented to prove that the controller has bounded-input/bounded-output (BIBO) stability.

A Novel Proportional−Integral-Derivative Control Configuration with Application to the Control of Batch Distillation

The aim of this paper is to propose a novel proportional-integral-derivative (PID) control configuration based on an observer structure. Batch distillation is used as the base case study where the regulated output is the distillate composition. The proposed PID control law is derived in the framework of robust nonlinear control with modeling error compensation techniques. A reduced-order observer is proposed to estimate both the derivative of the regulated output and the underlying modeling error. These observations are subsequently used in a control loop to feedback variations of distillate composition (derivative feedback) and to counteract the effects of modeling errors. It is shown that, under certain conditions, the resulting control law is equivalent to a classical PID controller with an antireset windup scheme. Moreover, the tuning of the controller is performed very easily in terms of a prescribed closed-loop time constant and an estimation time constant. Numerical results are provided for binary and multicomponent separations. Sampled/delayed measurements and several sources of uncertainties are considered in order to provide a realistic test scenario for the proposed control design procedure.

Predictive PID

A new stochastic, predictive, proportional-integral-derivative (PID) control law is proposed which is mathematically equivalent to generalized predictive control (GPC) with a steady state weighting term. The main motivation of this paper is the extension of the classical PID algorithm on industrial computers to do advanced control without employing specialized software. The predictive PID constants and the internal model are chosen by equating the discrete PID control law with the linear form of GPC. The result is a long range predictive control law with a model based PID structure. Use of a first order model yields a PI controller while a second order plant results in a PID structure. The process model order is restricted to a maximum of two although there is no restriction on the choice of GPC tuning parameters. Performance of the predictive PID scheme is shown, via simulation, to be identical to GPC. Results from the use of the predictive PID algorithm for the control of an industrial heat exchanger are also presented.

Minimum delay PID control of interpolated joint trajectories of robot manipulators

The digital implementation of an optimal PID (proportional integral derivative) controller of linearly interpolated joint trajectories is presented. The controller obtains optimal performance by reformulating the PID control law to minimize the time delay between the position transducer reading and the application of the corrective torque. Compared with the PID controller that is computed in a straightforward fashion, this formulation reduces the time delay by a factor of three. The superior performance of the proposed minimum-delay PID (MD PID) is demonstrated by experiments on a robot manipulator. Other practical issues associated with a digital PID control of joint trajectories of a robot manipulator, such as integer overflow and compensation for dynamics and joint-friction, are discussed. >