Improved DBO algorithm tunes fuzzy-PD controller for robot manipulator trajectory tracking

This paper describes the design principle, tracking performance, and stability analysis of a fuzzy proportional-derivative (PD) controller. First, the fuzzy PD controller is derived from the conventional continuous-time linear PD controller. Then, the fuzzification, control-rule base, and defuzzification in the design of the fuzzy PD controller are discussed in detail. The resulting controller is a discrete-time fuzzy version of the conventional PD controller, which has the same linear structure in the proportional and the derivative parts but has nonconstant gains: both the proportional and derivative gains are nonlinear functions of the input signals. The new fuzzy PD controller thus preserves the simple linear structure of the conventional PD controller yet enhances its self-tuning control capability. Computer simulation results have demonstrated this advantage of the fuzzy PD controller, particularly when the process to be controlled is nonlinear. After a detailed stability analysis, where a simple and realistic sufficient condition for the bounded-input/bounded-output stability of the overall feedback control system was derived, several computer simulation results are compared with the conventional PD controller. Although the conventional and fuzzy PD controllers are not exactly comparable, the authors compare them in order to have a sense of how well the fuzzy PD controller performs. For this reason, in the simulations several first-order and second-order linear systems, with or without time-delays, are first used to test the performance of the fuzzy PD controller for step reference inputs: the fuzzy PD control systems show remarkable performance, as well as (if not better than) the conventional PD control systems. Moreover, the fuzzy PD controller is compared to the conventional PD controller for a particular second-order linear system, showing the advantage of the fuzzy PD controller over the conventional one in the sense that in order to obtain the same control performance the conventional PD controller has to employ an extremely large gain while the fuzzy controller uses a reasonably small gain. Finally, in the case of nonlinear systems, the authors provide some examples to show that the fuzzy PD controller can track the set-points satisfactorily but the conventional PD controller cannot. >

A fuzzy proportional-derivative (PD) controller is used to control the speed of a permanent magnet DC motor with a load. The fuzzy PD controller has the same linear structure as a conventional digital PD controller yet improves its self-tuning control capability. The performance of the fuzzy PD controller is tested with an experimental setup of a motor-generator with a load and is then compared to the conventional digital PD controller. This comparison reveals that the fuzzy PD controller performs as well as a conventional digital PD controller, and it reduces steady state error, thus maintaining the speed at set-point. >

This paper endeavors to contribute to the field of optimal control via presenting an optimal fuzzy Proportional Derivative (PD) controller for a RPP (Revolute-Prismatic-Prismatic) robot manipulator based on particle swarm optimization and inverse dynamics. The Denavit-Hartenberg approach and the Jacobi method for each of the arms of the robot are employed in order to gain the kinematic equations of the manipulator. Furthermore, the Lagrange method is utilized to obtain the dynamic equations of motion. Hence, in order to control the dynamics of the robot manipulator, inverse dynamics and a fuzzy PD controller optimized via particle swarm optimization are used in this research study. The obtained results of the optimal fuzzy PD controller based on the inverse dynamics are compared to the outcomes of the PD controller, and it is illustrated that the optimal fuzzy PD controller shows better controlling performance in comparison with other controllers.

Purpose This study aims to present a method for the conceptual design and simulation of an aircraft flight control system. Design/methodology/approach The design methodology is based on particle swarm optimization (PSO). PSO can be used to improve the performance of conventional controllers. The aim of the present study is threefold. First, it attempts to detect and isolate faults in an aircraft model. Second, it is to design a proportional (P) controller, a proportional derivative (PD) controller, a proportional-integral (PI) controller and a fuzzy controller for an aircraft model. Third, it is to design a PD controller for an aircraft using a PSO algorithm. Findings Conventional controllers, an intelligent controller and a PD controller-based PSO were investigated for flight control. It was seen that the P controller, the PI controller and the PD controller-based PSO caused overshoot. These overshoots were 18.5, 87.7 and 2.6 per cent, respectively. Overshoot was not seen using the PD controller or fuzzy controller. Steady state errors were almost zero for all controllers. The PD controller had the best settling time. The fuzzy controller was second best. The PD controller-based PSO was the third best, but the result was close to the others. Originality/value This study shows the implementation of the present algorithm for a specified space mission and also for study regarding variation of performance parameters. This study shows fault detection and isolation procedures and also controller gain choice for a flight control system. A comparison between conventional controllers and PD-based PSO controllers is presented. In this study, sensor fault detection and isolation are carried out, and, also, root locus, time domain analysis and Routh–Hurwitz methods are used to find the conventional controller gains which differ from other studies. A fuzzy controller is created by the trial and error method. Integral of squared time multiplied by squared error is used as a performance function type in PSO.

This paper presents the stability analysis of a fuzzy proportional-derivative (PD) controller. The proposed fuzzy PD controller has the same simple linear structure as the conventional PD controller in the proportional and derivative parts, but the two fuzzy control gains are nonlinear functions of the input error signals and they both have self-tuning control capability. The fuzzy PD controller has the same performance as the conventional PD controller for linear processes, yet improves the control capability for nonlinear and uncertain processes. We employ the small gain theorem to analyze the bounded-input/bounded-output stability of a general nonlinear fuzzy PD control system, deriving a simple and realistic sufficient condition for the stability of the system. >

A hybrid control technique combining both proportional derivative (PD) and adaptive integral backstepping (AIB) control strategies is proposed for position control within a building-generated wind wake. Position hold performance for a quadrotor (, ) within windfields obtained from large-eddy simulations are used to compare control strategies. Simulated results show that both the PD and AIB control approaches can outperform each other depending on position within the wake. Over multiple positions within a wake, simulations show the hybrid approach reduces the average volume within which the vehicle remains by approximately 50% over both an exclusively PD or AIB control approach. Simulations also show the hybrid approach can hold the quadrotor to within an average of approximately one body length of the desired position compared to one to two body lengths using a PD control and two body lengths using an AIB control. Flight tests performed using PD and hybrid approaches confirm the improved position hold ability of the hybrid controller in building wake environments. Flight data show the hybrid controller reduces the average volume within which the vehicle remains bounded by approximately 50% compared to the volume obtained using the PD controller.

PurposeThe aim of this paper is to propose a robust robot fuzzy logic proportional-derivative (PD) controller for trajectory tracking of autonomous nonholonomic differential drive wheeled mobile robot (WMR) of the type Quanser Qbot.Design/methodology/approachFuzzy robot control approach is used for developing a robust fuzzy PD controller for trajectory tracking of a nonholonomic differential drive WMR. The linear/angular velocity of the differential drive mobile robot are formulated such that the tracking errors between the robot’s trajectory and the reference path converge asymptotically to zero. Here, a new controller zero-order Takagy–Sugeno trajectory tracking (ZTS-TT) controller is deduced for robot’s speed regulation based on the fuzzy PD controller. The WMR used for the experimental implementation is Quanser Qbot which has two differential drive wheels; therefore, the right/left wheel velocity of the differential wheels of the robot are worked out using inverse kinematics model. The controller is implemented using MATLAB Simulink with QUARC framework, downloaded and compiled into executable (.exe) on the robot based on the WIFI TCP/IP connection.FindingsCompared to other fuzzy proportional-integral-derivative (PID) controllers, the proposed fuzzy PD controller was found to be robust, stable and consuming less resources on the robot. The comparative results of the proposed ZTS-TT controller and the conventional PD controller demonstrated clearly that the proposed ZTS-TT controller provides better tracking performances, flexibility, robustness and stability for the WMR.Practical implicationsThe proposed fuzzy PD controller can be improved using hybrid techniques. The proposed approach can be developed for obstacle detection and collision avoidance in combination with trajectory tracking for use in environments with obstacles.Originality/valueA robust fuzzy logic PD is developed and its performances are compared to the existing fuzzy PID controller. A ZTS-TT controller is deduced for trajectory tracking of an autonomous nonholonomic differential drive mobile robot (i.e. Quanser Qbot).

Comparing with the serial ones, parallel manipulators have potential advantages in terms of high stiffness, accuracy and speed (Merlet, 2001). Especially the high accuracy and speed performances make the parallel manipulators widely applied to the following fields, like the pick-and-place operation in food, medicine, electronic industry and so on. At present, the key issues are the ways to meet the demand of high accuracy in moving process under the condition of high speed. In order to realize the high speed and accuracy motion, it’s very important to design efficient control strategies for parallel manipulators. In literatures, there are two basic control strategies for parallel manipulators (Zhang et.al., 2007): kinematic control strategies and dynamic control strategies. In the kinematic control strategies, parallel manipulators are decoupled into a group of single axis control systems, so they can be controlled by a group of individual controllers. Proportional-derivative (PD) control(Ghorbel et.al., 2000; Wu et.al., 2002), nonlinear PD (NPD) control (Ouyang et.al., 2002; Su et.al., 2004), and fuzzy control (Su et.al., 2005) all belong to this type of control strategies. These controllers do not always produce high control performance, and there is no guarantee of stability at the high speed. Unlike the kinematic control strategies, full dynamic model of parallel manipulators is taken into account in the dynamic control strategies. So the nonlinear dynamics of parallel manipulators can be compensated and better performance can be achieved with the dynamic strategies. The traditional dynamic control strategies of parallel manipulators are the augmented PD (APD) control and the computed-torque (CT) control (Li & Wu, 2004; Cheng et.al., 2003; Paccot et.al., 2009). In the APD controller (Cheng et.al., 2003), the control law contains the tracking control term and the feed-forward compensation term. The tracking control term is realized by the PD control algorithm. The feed-forward compensation term contains the dynamic compensation calculated by the desired velocity and desired acceleration on the basis of the dynamic model. Compared with the simple PD controller, the APD controller is a tracking control method. However, the feed-forward compensation can not restrain the trajectory disturbance effectively, thus the tracking accuracy of the APD controller will be decreased. In order to solve this problem, the CT controller including the velocity feed-back is proposed based on the PD controller (Paccot et.al., 2009). The CT control method yields a controller that suppresses disturbance and tracks desired trajectories uniformly in all configurations of the manipulators. Both the APD controller and the CT controller contain two parts including the PD control term and the dynamic compensation term. For the

This paper presents new circuits for realizing both current-mode and voltage-mode proportional-integral- derivative (PID), proportional-derivative (PD) and proportional-integral (PI) controllers employing second- generation current conveyors (CCIIs) as active elements. All of the proposed PID, PI and PD controllers have grounded passive elements and adjustable parameters. The controllers employ reduced number of active and passive components with respect to the traditional op-amp-based PID, PI and PD controllers. A closed loop control system using the proposed PID controller is designed and simulated with SPICE. I. INTRODUCTION The proportional-integral-derivative (PID), proportional- integral (PI) and proportional-derivative (PD) controllers with adjustable parameters are widely used in many industrial control systems, and usually have been implemented with operational amplifiers (op-amps). PID controller realizations using op-amps employ excessive number of active and passive components. Current conveyors, especially plus-type second-generation current conveyors (CCII+s) have larger bandwidth and dynamic range, and also greater linearity than op-amps (1). Employing only CCII+s simplifies the configuration thus gyrator (2), synthetic floating inductances (3-4), voltage- mode filter (5) and transfer function synthesis (6-7) employing only CCII+s have been reported in the literature. Despite of several circuits such as derivative, integral, proportion and summer constructed with op-amps (8-9) that can be used to implement PID, PI and PD controllers, current conveyors have not been used to realize PID, PD and PI controllers in the literature so far. In this paper, novel current-mode and voltage-mode PID, PI and PD controllers employing CCII+s as active elements are proposed. In the proposed current-mode controllers, output currents are taken from high impedance terminals of the CCII+s. Also input voltages are applied to the Y-terminals of the CCII+s in the presented voltage- mode PID, PI and PD controllers. These properties make the proposed circuit ideal for using in cascaded systems. All the proposed controller realizations employ only grounded passive components, and do not require passive element matching so the circuits are suitable for integration. The PID, PI and PD parameters can be selected independently. Also simulations are performed using SPICE program to verify the theoretical analyzes.

This paper proposes a fuzzy proportional-integral-derivative (PID) control for the planar switched reluctance motor (PSRM) to realize precision positioning. The mathematical model of the PSRM is first given. Then a fuzzy proportional-derivative (PD) controller is designed for the PSRM, which features strong robustness to parametric variation, uncertainty, and interference. The fuzzy PD control of the PSRM is carried out via simulation based on MATLAB. Compared with the PD controller, the control performance of the PSRM with the fuzzy PD controller is superior. The effectiveness of the proposed control is verified through the simulation results.

This paper proposes a conventional proportional derivative (PD) controller for a partial feedback linearized model of a single machine infinite bus (SMIB) system. The linearized model of the proposed system able to provide independent operating points through the feedback linearizing approach. The conventional PD controller is applied to stabilize the partial feedback linearized model of the SMIB system based on the loop shape and |H| ∞ value of sensitivity and complementary sensitivity function. Due to the linearized system stability issues, open-loop and closed-loop stability analysis have been investigated without PD controller and also with the PD controller. Finally, the performance of proposed partial feedback linearizing PD (PFBL-PD) controller has compared with existing power system stabilizer (PSS) under a three-phase short-circuit fault in EMT simulation platform as MATLAB/SIMULINK.

Although the fuzzy logic controller is superior to the proportional integral derivative (PID) controller in motor control, the gain tuning of the fuzzy logic controller is more complicated than that of the PID controller. Using mathematical analysis of the proportional derivative (PD) and fuzzy logic controller, this study proposed a design method of a fuzzy logic controller that has the same characteristics as the PD controller in the beginning. Then a design method of a fuzzy logic controller was proposed that has superior performance to the PD controller. This fuzzy logic controller was designed by changing the envelope of the input of the of the fuzzy logic controller to nonlinear, because the fuzzy logic controller has more degree of freedom to select the control gain than the PD controller. By designing the fuzzy logic controller using the proposed method, it simplified the design of fuzzy logic controller, and it simplified the comparison of these two controllers.

A fuzzy logic-based precompensation approach for controlling systems with deadzones is proposed. The control structure consists of a fuzzy logic-based precompensator followed by a conventional proportional derivative (PD) controller. The authors' proposed control scheme shows superior transient and steady-state performance compared to conventional PD and proportional integral derivative (PID) controllers. The scheme is robust to variations in deadzone nonlinearities, as well as the steady-state gain of the plant. The effectiveness of the scheme is illustrated using computer simulation examples. >

Brushless DC (BLDC) motor is one type of dc electric motor that has various advantages such as higher of efficiency, torque under lows peed range, and power density, and lower maintenance because this motor does not use the brush in the commutation process. BLDC drive involves a complex process, so it takes the right control method to control its speed. This paper proposed Self-Tuning Regulator (STR) of Proportional Derivative (PD) controller where the parameters are adapted using fuzzy-logic. STR PD controller also can follow fixed reference faster than PD controller. It can follow speed reference change up and overcome the interference from the outside of a momentary disturbance, and STR PD controller technique had better performance than the PD controller, mainly when the motor was working at speed reference change down, $it$ can minimize of undershoot that caused by PD controller.

This paper proposes the use Fuzzy logic controller and a Particle Swarm Optimization (PSO) based Linear Quadratic Regulator (LQR) controller for the balance of an electric unicycle. It also examines dynamics of the electric unicycle's schematic design, which has a backing seat for the passenger and is analogous to the Segway device equipped with a handle shaft for direction. A proportional derivative controller incorporated with a Fuzzy logic controller was designed for the suggested model of the electric unicycle for speed and position control (Fuzzy controller), and a Proportional Derivative (PD) controller was utilized to control the angle rate and angle of the unicycle. Comparison of the Fuzzy logic controller with a Particle Swarm Optimization (PSO) Linear Quadratic Regulator (LQR) controller was carried out based on error deviation and time response simulations. Command tracking or regulation of some initial state condition were examined and demonstrated by time based simulations of the modelled system. The results evidently indicate that the Fuzzy Proportional Derivative (PD) controller can effectively attain attitude equilibrium for the two wheeled unicycle and preventing it from falling down.