Generalized Predictive Control Law Research Articles

A GPC-based Multi-variable PID Control Algorithm and Its Application in Anti-swing Control and Accurate Positioning Control for Bridge Cranes

It is one of the key tasks for the bridge crane to achieve anti-swing control of the hook and the accurate positioning of the body to work efficiently, safely and automatically. Based on Lagrange equation, this paper is to propose a dynamic model of the crane motion system for designing controller. In the controller design, Proportional-Integral-Derivative (PID), the most widely used controller in engineering, is adopted and a new parameter tuning algorithm for a multi-variable PID controller based on generalized predictive control (GPC) is given. It is found that the multi-variable PID controller shares the same structural mathematical expressions with the GPC law, which makes for the transfer and calculation of the three parameters P, I and D, and that the new algorithm enables the traditional PID controller to perform as brilliantly as the GPC. The results of both the simulation and real-time control experiments show that the newly-proposed PID controller can effectively eliminate the swing of the hook and control the bridge cranes moving position accurately.

Predictive IP controller for robust position control of linear servo system

Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme.

Closed-Loop Modulation of the Pathological Disorders of the Basal Ganglia Network.

A generalized predictive closed-loop control strategy to improve the basal ganglia activity patterns in Parkinson's disease (PD) is explored in this paper. Based on system identification, an input-output model is established to reveal the relationship between external stimulation and neuronal responses. The model contributes to the implementation of the generalized predictive control (GPC) algorithm that generates the optimal stimulation waveform to modulate the activities of neuronal nuclei. By analyzing the roles of two critical control parameters within the GPC law, optimal closed-loop control that has the capability of restoring the normal relay reliability of the thalamus with the least stimulation energy expenditure can be achieved. In comparison with open-loop deep brain stimulation and traditional static control schemes, the generalized predictive closed-loop control strategy can optimize the stimulation waveform without requiring any particular knowledge of the physiological properties of the system. This type of closed-loop control strategy generates an adaptive stimulation waveform with low energy expenditure with the potential to improve the treatments for PD.

Adaptive PIF Control for Permanent Magnet Synchronous Motors Based on GPC

To enhance the control performance of permanent magnet synchronous motors (PMSMs), a generalized predictive control (GPC)-based proportional integral feedforward (PIF) controller is proposed for the speed control system. In this new approach, firstly, based on the online identification of controlled model parameters, a simplified GPC law supplies the PIF controller with suitable control parameters according to the uncertainties in the operating conditions. Secondly, the speed reference curve for PMSMs is usually required to be continuous and continuously differentiable according to the general servo system design requirements, so the adaptation of the speed reference is discussed in details in this paper. Hence, the performance of the speed control system using a GPC-based PIF controller is improved for tracking some specified signals. The main motivation of this paper is the extension of GPC law to replace the traditional PI or PIF controllers in industrial applications. The efficacy and usefulness of the proposed controller are verified through experimental results.

Computationally Efficient Predictive Robot Control

Conventional linear controllers (PID) are not really suitable for the control of robot manipulators due to the highly nonlinear behavior of the latter. Over the last decades, several control methods have been proposed to circumvent this limitation. This paper presents an approach to the control of manipulators using a computationally-efficient-model-based predictive control scheme. First, a general predictive control law is derived for position tracking and velocity control, taking into account the dynamic model of the robot, the prediction and control horizons, and also the constraints. However, the main contribution of this paper is the derivation of an analytical expression for the optimal control to be applied that does not involve a numerical procedure, as opposed to most predictive control schemes. In the last part of the paper, the effectiveness of the approach for the control of a nonlinear plant is illustrated using a direct-drive pendulum, and then, the approach is validated and compared to a PID controller using an experimental implementation on a 6-DOF cable-driven parallel manipulator.

FUTURE REFERENCE TRAJECTORY IMPROVEMENT IN SELF-TUNING I-PD CONTROLLER BASED ON GENERALIZED PREDICTIVE CONTROL LAW

In this paper a new design method of a self-tuning I-PD controller is proposed. The I-PD controller is designed based on a generalized predictive control (GPC) law. By using a conventional design method of the I-PD controller based on the GPC law, it is impossible to introduce future reference trajectory into the I-PD controller. The future reference trajectory of the GPC approaches a set-point value gradually from a present output, that is one of the features of the GPC. The proposed method can introduce the advantage of the GPC into the I-PD controller exactly because the proposed method expresses the future reference trajectory suitably when the set-point value is given as a step type signal. This paper shows that the structure of a PID controller based on the GPC law becomes an I-PD type by using the proposed method and that a design parameter of the GPC which adjusts the future reference trajectory influences only an integral time of the I-PD controller.In order to illustrate the effectiveness of the newly proposed method, numerical examples are shown.

Fast algorithms for generalized predictive control

Fast algorithms for generalized predictive control (GPC) are derived by adopting an approach whereby dynamic programming and a polynomial formulation are jointly exploited. They consist of a set of coupled linear polynomial recursions by which the dynamic output feedback GPC law is recursively computed wwith only O( Nn) computations for an n- th order plant and N- steps prediction horizon.