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PID (proportional+integral+derivative) controller is well known as a simple and easy-to-implement controller. However, the design procedure is not straightforward for multi-input multi-output (MIMO) systems. It is even more complicated when robustness criterion must be handled. In this paper, a stable robust PID controller for anti-swing control of automatic gantry crane is proposed. The proposed method employs an automatic tuning using DE (differential evolution) to search for a set of PID controller gains that satisfy Kharitonovs polynomials robust stability criterion. This robust stability criterion is used to deal with parametric uncertainty occurs in gantry crane model. The simulation results show that a satisfactory robust PID control performance can be achieved. The PID controller is able to quickly move the cart of the crane while suppressing the swing of the payload for various conditions, i.e. payload mass and cable length variations.

Anti-swing control is a well-known term in gantry crane control. It is designed to move the payload of gantry crane as fast as possible while the payload swing angle should be kept as small as possible at the final position. A number of studies have proposed anti-swing control using the well-known proportional, integral, derivative (PID) control method. However, PID controllers cannot always effectively control systems with changing parameters. Some studies have also proposed intelligent-based control including fuzzy control. However, the designers often have to face the problem of tuning many parameters during the design to obtain optimum performance. Thus, a lot of effort has to be taken in the design stage. In this paper Fuzzy-tuned PID controller design for anti-swing gantry crane control is presented. The objective is to design a practical anti-swing control which is simple in the design and also robust. The proposed Fuzzy-tuned PID utilizes fuzzy system as PID gain tuners to achieve robust performance to parameters’ variations in the gantry crane. A complex dynamic analysis of the system is not needed. PID controller is firstly optimized in MATLAB using a rough model dynamic of the system which is identified by conducting a simple open-loop experiment. Then, the PID gains are used to guide the range of the fuzzy outputs of the Fuzzy-tuned PID controllers. The experimental results show that the proposed anti-swing controller has satisfactory performance. In addition, the proposed method is straightforward in the design.

This paper presents a robust feedback controller design for parametric uncertainty systems via constrained optimization. The proposed controller design employs modern optimization tools (Particle Swarm Optimization and Differential Evolution) in a single objective constrained optimization. The objective of the optimization is to search for a set of robust controller gains such that the stability radius of the closed-loop system is maximized. The constraint of the optimization is a closed-loop poles region which is directly related to the desired time-domain control performance. The proposed controller design is applied to control of pendulum-like systems (gantry crane, flexible joint and inverted pendulum). The results show the robust performance of the designed controller.

Computational intelligence has been successfully applied to many engineering applications including control engineering problems. In this paper, a robust state feedback control design using particle swarm optimizer based constrained optimization is proposed. The feedback controller is optimized based on state space model of the plant with structured uncertainty such that the closed-loop system would have maximum stability radius. A wedge region is assigned as a constraint to locate the desired closed-loop poles, which correspond to time-domain control system performance. The proposed controller design is applied to anti-swing control for a gantry crane system. The experimental result is shown, and comparison with that of conventional linear quadratic regulator based controller and H ∞ loop shaping controller are made. The proposed method achieves a satisfactory robust performance based on the structured singular value analysis and the experimental results.

PID (proportional+integral+derivative) control is known as simple and easy-to-implement controller. However, the robustness performance is often not satisfactory when dealing with parameter variations of the plant. In addition, its design procedure is not straightforward for the system which is non-SISO (Singe Input Single Output) system like a gantry crane. In this paper, a stable robust PID controller for anti-swing control of automatic gantry crane is proposed. The proposed method employs Genetic Algorithm (GA) in min-max optimization to find the stable robust PID. In the optimization, the robustness of the controller is tested using Kharitonov's polynomials robust stability criterion to deal with parametric uncertainty appears in gantry crane model. For practicality, the model is identified by conducting simple open-loop experiment in the beginning. The experimental results show that a satisfactory robust PID control performance can be achieved. The controller is able to effectively move the trolley of the crane in short time while canceling the swing of the payload for different conditions of payload mass and cable length variations.

The main objective of this paper is to design a state feedback controller for stabilizing and tracking control of self-erecting inverted pendulum employing intelligent method using particle swarm optimization (PSO). This is motivated by the fact that one has to face trial and error approach in conventional feedback control design by pole placement method or linear quadratic regulator (LQR) method via Riccati equation. The simulation results show the effectiveness of the proposed method. The proposed state feedback controller works jointly with swing-up controller in the self-erecting inverted pendulum system.

In this paper, a novel method for tuning PID controller of automatic gantry crane control using particle swarm optimization (PSO) is proposed. PSO is one of the most recent optimization techniques based on evolutionary algorithm. PSO is also known as computationally efficient method. This work presents in detail how to apply PSO method in finding the optimal PID gains of gantry crane system in the fashion of min-max optimization. The simulation results show that with proper tuning a satisfactory PID control performance can be achieved to drive nonlinear plant. The controller is able to effectively move the trolley of the crane in short time while canceling the swing angle of the payload hanging on the trolley at the end position. The robustness of the controller is also tested.

This paper discusses fuzzy-tuned PID controller design for anti-swing gantry crane control. The objective is employing simple structure of PID control by utilizing fuzzy system as gain tuners to improve its robustness to cope with parameters variations in the crane system. The proposed fuzzy-tuned PID system has simple structure. Instead of fixed PID gains, the gains are determined by means of a fuzzy inference system. Weighting factors are also added to fuzzy output. The simulation result shows that the proposed Fuzzy-tuned PID controllers has similar performance with PID controllers with advantage of robustness to parameters variations for anti-swing gantry crane control.

In this paper, a robust state feedback control design using particle swarm optimisation-based constrained optimisation is proposed. The feedback controller is designed based on state space model of the plant with structured uncertainty such that the closed-loop system would have maximum stability radius. A wedge region is assigned as a constraint to locate desired closed loop poles. The proposed method is applied into design of anti-swing control of an automatic gantry crane experiment. A comparison with that of LQR-based controller is made. The result shows that the proposed method effectively locates the closed loop poles within the prescribed wedge region and its robust performance is guaranteed.

The aim of this research is to design a PID Controller using PSO algorithm. The model of a DC motor is used as a plant in this paper. The conventional gain tuning of PID controller (such as Ziegler-Nichols (ZN) method) usually produces a big overshoot, and therefore modern heuristics approach such as genetic algorithm (GA) and particle swarm optimization (PSO) are employed to enhance the capability of traditional techniques. However, due to the computational efficiency, only PSO will be used in this paper. The comparison between PSO-based PID (PSO-PID) performance and the ZN-PID is presented. The results show the advantage of the PID tuning using PSO-based optimization approach.