Hybrid Proportional Integral Derivative Research Articles

Control of robot-assisted gait trainer using hybrid proportional integral derivative and iterative learning control

<p><span lang="EN-US">An inexpensive exoskeleton of the lower limb was designed and developed in this study. It can be used as a gait trainer for persons with lower limb problems. It plays an essential role in lower limb rehabilitation and aid for patients, and it can help them improve their physical condition. This paper proposes a hybrid controller for regulating the lower limb exoskeleton of a robot-assisted gait trainer that uses a proportional integral and derivative (PID) controller combined with an iterative learning controller (ILC). The direct current motors at the hip and knee joints are controlled by a microcontroller that uses a preset pattern for the trajectories. It can learn how to monitor a trajectory. If the trajectory or load is changed, it will be able to follow the change. The experiment showed that the PID controller had the smallest overshoot, and settling time, and was responsible for system stability. Even if there are occasional interruptions, the tracking performance improves with the ILC.</span></p>

Globally stable proportional‐integral‐derivative control for spacecraft pose tracking via dual quaternions

Due to the high reliability and simple adjustment, proportional-integral-derivative (PID) control is widely used in practical systems. However, intrinsic non-linearity of the pose dynamics hinders the design of a PID pose controller. Besides, topological constraints on the rigid body configuration space SE(3) prevent a continuous or a memoryless discontinuous state feedback control from achieving robust global stability. For these two issues, this paper develops a dual quaternion-based hybrid PID pose tracking control strategy, where the newly introduced integral term can compensate for constant disturbances, and the hysteretic switching-based hybrid feedback can avoid both unwinding phenomenon and noise sensitivity caused by the topological constraints. In addition, by adjusting the hysteresis width, the proposed hybrid PID controller can also become a continuous or a discontinuous PID controller. The global asymptotic stability in the presence of constant disturbance forces and torques, and robustness to measurement noises are proven by the hybrid system stability theory. Numerical simulations for an earth-orbiting spacecraft pose tracking mission are conducted to illustrate and compare the performances of the proposed hybrid, continuous and discontinuous PID pose tracking control laws.

Stabilized Time Transfer via a 1000-km Optical Fiber Link Using High-Precision Delay Compensation System

Variations in optical fiber length and refractive index are induced by environmental perturbation, resulting in an additional dynamic propagation delay in fiber-based time synchronization systems, which deteriorate their transfer stability. This disadvantage can be significantly reduced by transmitting the time signal in both directions through fiber and constructing a feedback loop to compensate the propagation delay at the remote end of the link. This paper proposes an analog-digital hybrid proportional integral derivative (PID) control compensation system based on the time-frequency phase-locked loop (TF-PLL). The system is designed to keep the merits of wide servo bandwidth, servo accuracy, and a large dynamic delay compensation range up to 1 s, which is much greater than that reported in previous studies. For proving the validity of this proposed scheme, a self-developed optical fiber time synchronization equipment based on the delay compensation system is applied. The delay compensation system is used on a 1100-km long laboratory optical fiber, and the results show that the time synchronization stability in terms of time deviation (TDEV) is less than 5.92 ps/1 s and 2.56 ps/10,000 s. After successful laboratory evaluation, the proposed system is installed on a real 988.48-km line between the Xi’an Lintong branch of the National Time Service Center (NTSC) and Linfen City, Shanxi Province, realizing the time synchronization of 10 stations along the optical fiber link. The experimental results in the 988.48-km link illustrate that the measured time difference with a peak-to-peak value of 176 ps, the standard deviation of 19.3 ps, and a TDEV of less than 10.49 ps/1 s and 2.31 ps/40,000 s is achieved. The high-precision time delay compensation system proposed in this paper is simple, reliable, and accurate; has a wide range of compensation; and opens up a feasible scheme for providing synchronized time signals to multiple users over the long-distance field optical fiber networks.

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

In this study, to improve the accuracy of path tracking in intelligent vehicles, we propose an intelligent vehicle path-tracking control method based on improved model predictive control (MPC) combined with hybrid proportional-integral-derivative (PID) control theory. In the lateral control, a constraint on the side deflection of the front wheel is added based on traditional MPC and a relaxation factor is introduced to improve the stability of vehicle control for the driving stability. In longitudinal control, a hybrid PID controller is designed for different road conditions to improve the accuracy of control of vehicle speed. We present the results of a co-simulation using CarSim and MATLAB/Simulink and a test with a sample vehicle, which show that the proposed path tracking controller can greatly improve the path tracking accuracy and stability of an intelligent vehicle. The model-based prediction, rolling optimization solution, feedback control, and the addition of a constraint on the side deflection of the front wheel as well as a relaxation factor can ensure the lateral driving stability of an intelligent vehicle. The proposed approach achieved a lateral error of less than 1%, and the yaw angle was controlled within 4°. The longitudinal speed control based on hybrid PID controller can improve the response speed of the system and meet the real-time requirements of vehicle driving.

Hybrid adaptive tracking control method for mobile manipulator robot based on Proportional–Integral–Derivative technique

In this research, an adaptive tracking control method for the nonholonomic robot system is addressed based on the hybrid Proportional–Integral–Derivative (PID) technique. The proposed hybrid PID scheme first applies the merits of the traditional PID method, with the online self-learning capability for the PID – gains, to force tracking errors to zero in the presence of uncertainties. Then, in order to improve the tracking performance, an adaptive Fuzzy Neural Networks (FNN) approximator and an adaptive robust controller type-compensator are utilized to relax the uncertainties problems of the robot control system. Moreover, the nonholonomic constraint force stability of the mobile manipulator robot is also considered by an adaptive control scheme. The design of online updating laws for the proposed controllers and FNN approximator are designed by applying the Lyapunov stability theorem. Thus, besides the improvement for tracking control performance, the stability of the proposed control system is also maintained. The effectiveness, robustness and adaptability of the proposed control strategy are verified by comparative numerical simulation results.

Intelligent Trajectory Tracking Behavior of a Multi-Joint Robotic Arm via Genetic-Swarm Optimization for the Inverse Kinematic Solution.

It is necessary to control the movement of a complex multi-joint structure such as a robotic arm in order to reach a target position accurately in various applications. In this paper, a hybrid optimal Genetic–Swarm solution for the Inverse Kinematic (IK) solution of a robotic arm is presented. Each joint is controlled by Proportional–Integral–Derivative (PID) controller optimized with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called Genetic–Swarm Optimization (GSO). GSO solves the IK of each joint while the dynamic model is determined by the Lagrangian. The tuning of the PID is defined as an optimization problem and is solved by PSO for the simulated model in a virtual environment. A Graphical User Interface has been developed as a front-end application. Based on the combination of hybrid optimal GSO and PID control, it is ascertained that the system works efficiently. Finally, we compare the hybrid optimal GSO with conventional optimization methods by statistic analysis.

Controlling a knee CPM machine using PID and iterative learning control algorithm

A conventional continuous passive motion (CPM) machine uses simple controller such as On/Off controller. Some better CPMs use PID controller. These kind of CPMs can not distinguish load different due to the different size of the patient leg. This may cause the CPM no longer follow the trajectory or the angle commands. Meanwhile, each patient may have different scenario of therapy from the others. When progress on the patient exists, the range of the flexion may be increased step by step. Therefore, the treatment can be different in term of the range of flexion from time to time. This paper proposes CPM with hybrid proportional integral derivative (PID) and iterative learning controller (ILC). The system has capability in learning the trajectory tracking. Therefore, the CPM will be able to follow any load or trajectory changes applied to it. The more accurate CPM machine can follow the trajectory command, the better its performance for the treatment. The experiment showed that the system was stable due to the PID controller. The tracking performance also improved with the ILC even there exist some disturbances .

Hybrid Dynamic Neural Network and PID Control of Pneumatic Artificial Muscle Using the PSO Algorithm

Pneumatic artificial muscles (PAM) have been recently considered as a prominent challenge regarding pneumatic actuators specifically for rehabilitation and medical applications. Since accomplishing accurate control of the PAM is comparatively complicated due to time-varying behavior, elasticity and ambiguous characteristics, a high performance and efficient control approach should be adopted. Besides of the mentioned challenges, limited course length is another predicament with the PAM control. In this regard, this paper proposes a new hybrid dynamic neural network (DNN) and proportional integral derivative (PID) controller for the position of the PAM. In order to enhance the proficiency of the controller, the problem under study is designed in the form of an optimization trend. Considering the potential of particle swarm optimization, it has been applied to optimally tune the PID-DNN parameters. To verify the performance of the proposed controller, it has been implemented on a real-time system and compared to a conventional sliding mode controller. Simulation and experimental results show the effectiveness of the proposed controller in tracking the reference signals in the entire course of the PAM.

Optimal PID controller of a brushless dc motor using genetic algorithm

Brushless DC (BLDC) motor is commonly employed for many industrial applications due to their high torque and efficiency. This article produces an optimal designed controller of Brushless DC motor speed control depending on the genetic algorithm (GA). The optimization method is used for searching of the ideal Proportional–Integral-Derivative (PID) factors. The controller design methods of brushless DC motor includes three kinds: trial and error PID design, auto-tuning PID design and genetic algorithm based controller design. A PID controller is utilizing by conducted Integral absolute error criterion (IAE) and integral squared error (ISE) error criterion for BLDC motor control system. A GA-PID controller is designed to enhance the system performance by means of genetic algorithm. PID controller coefficients are calculated by GA to produce optimal PID as hybrid PID with GA controller .The closed loop speed response of PID controller is experimented for IAE and ISE error criteria. The suggested controller GA_PID is planned, modeled and simulated by MATLAB/ software program. A comparison output system performance monitored for every controller schemes. The results display that the time characteristics performance of GA-PID controller based on ISE objective function has the optimal performance (rise time, settling time, percentage overshoot) with other techniques.

A hybrid PID control scheme for flexible joint manipulators and a comparison with sliding mode control

Abstract The article focuses on the issues of dynamic modelling and control of manipulators which have elastic joints. An adaptive hybrid PID (proportional, integral, derivative) control scheme is proposed. For each joint two PID controllers have been implemented. The first is to cope with the joint elasticity and the second is used to deal with the rigid links motion. Simple adaptive mechanism is introduced to eliminate the need of continual tuning the PID gains and a comparison is made with a previously published sliding mode controller (SMC). For both controllers, the desired joint trajectories are computed online. This approach is necessary to overcome the influence of joint flexibility on the tracking performance. The proposed hybrid PID controller and the SMC are compared based on simulation tests. Results demonstrate the effectiveness and usefulness of the proposed hybrid PID control method.

Navigation of an Autonomous Wheeled Robot in Unknown Environments Based on Evolutionary Fuzzy Control

Navigation of a wheeled robot in unknown environments is proposed in this paper. The approach may be applied to navigating an autonomous vehicle in unknown environments, such as parking lots. The navigation consists of three parts: obstacle avoidance behavior, target seeking behavior, and a behavior supervisor. The obstacle avoidance behavior is achieved by controlling the robot to move along an obstacle boundary through evolutionary fuzzy control. In the evolutionary fuzzy control approach, a Pareto set of fuzzy controllers (FCs) is found though a multi-objective continuous ant colony optimization algorithm. Target seeking behavior is achieved by controlling the robot through hybrid proportional–integral–derivative (PID) controllers. The behavior supervisor determines the switching between obstacle avoidance and target seeking behaviors, where the dead-cycle problem is considered. Simulations and experiments were performed to verify the effectiveness of the proposed navigation scheme.

Real-Time Hybrid PID/ILC Control of Two-Link Flexible Manipulators

This paper investigates the development of a hybrid proportional-integral-derivative (PID) with iterative learning control (ILC) for input tracking and vibration control of a two-link flexible manipulator (TLFM). The TLFM was modelled using Lagrange’s formalism and assumed mode method (AMM). Three part apparatus was designed and implemented: FLEXISIM - TLFM mathematical model; FLEXIBOT: a real-world TLFM; and FLEXICON software: applications of PID control and hybrid PID with ILC schemes to the real-world TLFM. Each of the control schemes was investigated for tracking of sinusoidal waves and vibration suppression. It was found that the PID-based controller was incapable of acceptable control of the FLEXIBOT. The PID-ILC control scheme achieved acceptable tracking, with the former demonstrating better stability and energy efficiency. The effect of payload variations on the performance of the proposed controller is also studied. Experimental results show the effectiveness and robustness of the proposed controller.

Convergence performance comparisons of PID, MRAC, and PID + MRAC hybrid controller

This study proposes a hybrid controller by combining a proportional-integral-derivative (PID) control and a model reference adaptive control (MRAC), which named as PID + MRAC controller. The convergence performances of the PID control, MRAC, and hybrid PID + MRAC are also compared. Through the simulation in Matlab, the results show that the convergence speed and performance of the MRAC and the PID + MRAC controller are better than those of the PID controller. In addition, the convergence performance of the hybrid control is better than that of the MRAC control.

Hybrid PID and PSO-based control for electric power assist steering system for electric vehicle

Electric power assist steering (EPAS) system provides an important significance in enhancing the driving performance of a vehicle with its energy-conserving features. This paper presents a hybrid PID (Proportional-Integral-Derivative) and particle swarm optimization (PSO) based control scheme to minimize energy consumption for EPAS. This single objective optimization scheme is realized using the PSO technique in searching for best gain parameters of the PID controller. The fast tuning feature of this optimum PID controller produced high-quality solutions. Simulation results show the performance and effectiveness of the hybrid PSO-PID based controller as opposed to the conventional PID controller.

Active sway control of a gantry crane using hybrid input shaping and PID control schemes

This project presents investigations into the development of hybrid input-shaping and PID control schemes for active sway control of a gantry crane system. The application of positive input shaping involves a technique that can reduce the sway by creating a common signal that cancels its own vibration and used as a feed-forward control which is for controlling the sway angle of the pendulum, while the proportional integral derivative (PID) controller is used as a feedback control which is for controlling the crane position. The PID controller was tuned using Ziegler-Nichols method to get the best performance of the system. The hybrid input-shaping and PID control schemes guarantee a fast input tracking capability, precise payload positioning and very minimal sway motion. The modeling of gantry crane is used to simulate the system using MATLAB/SIMULINK software. The results of the response with the controllers are presented in time domains and frequency domains. The performances of control schemes are examined in terms of level of input tracking capability, sway angle reduction and time response specification.

Hybrid fuzzy proportional-integral plus conventional derivative control of linear and nonlinear systems

This paper presents a new approach toward the optimal design of a hybrid proportional-integral-derivative (PID) controller applicable for controlling linear as well as nonlinear systems using genetic algorithms (GAs). The proposed hybrid PID controller is derived by replacing the conventional PI controller by a two-input normalized linear fuzzy logic controller (FLC) and executing the conventional D controller in an incremental form. The salient features of the proposed controller are as follows: (1) the linearly defined FLC can generate nonlinear output so that high nonlinearities of complex systems can be handled; (2) only one well-defined linear fuzzy control space is required for both linear and nonlinear systems; (3) optimal tuning of the controller gains is carried out by using a GA; and (4) it is simple and easy to implement. Simulation results on a temperature control system (linear system) and a missile model (nonlinear system) demonstrate the effectiveness and robustness of the proposed controller.