Control strategies for crane systems: A comprehensive review

https://isiarticles.com/bundles/Article/pre/pdf/46311.pdf

A review on interval type-2 fuzzy logic applications in intelligent control

In this paper, a fixed-time dynamic surface high-order sliding mode control approach is presented for chaos suppression and voltage stabilization in three-bus power system via design of current source converter-based static synchronous compensator controller. The proposed control strategy constructs two high-order sliding mode surfaces to achieve control objective. By combining backstepping idea with dynamic surface control (DSC) technique, high-order sliding mode controller is designed and the inherent problem of “explosion of complexity” in backstepping design is avoided. Further, a new stability concept is introduced into DSC design to achieve semi-global uniform ultimate boundedness of the signals in high-order sliding mode system within finite time independent of initial condition. In addition, stability analysis is provided to show that the proposed control scheme can achieve semi-globally fixed-timely uniformly ultimately bounded stabilization. Finally, simulation results are given to demonstrate the effectiveness of the proposed control scheme and the superior performance over conventional DSC.

Fixed-time dynamic surface high-order sliding mode control for chaotic oscillation in power system

This study investigates adaptive neural network (NN) state feedback control and robust observation for an active suspension system that considers parametric uncertainties, road disturbances and actuator saturation. An adaptive radial basis function neural network is adopted to approximate uncertain non-linear functions in the dynamic system. An auxiliary system is designed and presented to deal with the effects of actuator saturation. In addition, since it is difficult to obtain accurate states in practice, an NN observer is developed to provide state estimation using the measured input and output data of the system. The state observer-based feedback control parameters with saturated inputs are optimised by the particle swarm optimisation scheme. Furthermore, the uniformly ultimately boundedness of all the closed-loop signals is guaranteed through rigorous Lyapunov analysis. The simulation results further demonstrate that the proposed controller can effectively suppress car body vibrations and offers superior control performance despite the existence of non-linear dynamics and control input constraints.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cta.2015.1317

Adaptive neural network control for active suspension system with actuator saturation

Path-tracking of autonomous vehicles using a novel adaptive robust exponential-like-sliding-mode fuzzy type-2 neural network controller

This paper presents a novel interval type-2 fuzzy-neural network indirect adaptive sliding mode control (SMC) approach (T2FNNAS) for a suspension system including actuator dynamics. It is equipped with a novel moving sliding surface in which the slope and intercept of sliding surface are simultaneity adjusted by adaptive interval T2FNN to improve controller robustness against system uncertainties and unknown disturbances. As a result, the drawbacks of the conventional SMC, such as chattering effect and a required priori knowledge of the bounds of uncertainties, are removed. Based on the Lyapunov synthesis approach, the free parameters of the adaptive FNN are tuned on-line. One advantage of the proposed approach is that, by incorporating the Lyapunov design approach and SMC method into the adaptive fuzzy-neural control scheme to derive the control law, the proposed approach not only assures closed-loop stability but also achieves a good performance for the overall system. Another advantage of the proposed method is that to relax the requirement for the bound of approximation error, and an estimation mechanism is also employed to observe the bound of it real time. Design of the control system consists of two interior loops. The inner loop is a force control of the hydraulic actuator, while the outer loop is position controller that use T2FNNAS. Finally, a comparison between the proposed approach and a robust model reference adaptive control approach is provided. Simulation results confirm that the proposed approach effectively improves both the passenger comfort and the ride quality of the car.

Interval type-2 fuzzy-neural network indirect adaptive sliding mode control for an active suspension system

This paper presents a novel active control approach for a hydraulic suspension system subject to road disturbances. A novel impedance model is used as a model reference in a particular robust adaptive control which is applied for the first time to the hydraulic suspension system. A scheme is introduced for selecting the impedance parameters. The impedance model prescribes a desired behaviour of the active suspension system in a wide range of different road conditions. Moreover, performance of the control system is improved by applying a particle swarm optimisation algorithm for optimising control design parameters. Design of the control system consists of two interior loops. The inner loop is a force control of the hydraulic actuator, while the outer loop is a robust model reference adaptive control (MRAC). This type of MRAC has been applied for uncertain linear systems. As another novelty, despite nonlinearity of the hydraulic actuator, the suspension system and the force loop together are presented as a...

Adaptive impedance control of a hydraulic suspension system using particle swarm optimisation

Type-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type-2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller (IT2FLC) to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization (PSO). A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller (T1FLC) is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.

/pdf/type-2-fuzzy-control-for-a-flexible-joint-robot-using-1jwnt08500.pdf

Type-2 Fuzzy Control for a Flexible- joint Robot Using Voltage Control Strategy

Direct Adaptive Function Approximation Techniques Based Control of Robot Manipulators

This paper is devoted to designing higher-order adaptive PID controllers as a new generation of PID controllers for chaos synchronization, in which second order integration and second-order derivative terms to the PID controller (PII2DD2) are employed. The five PII2DD2 control gains are updated online with a stable adaptation law driven by Lyapunov’s stability theory. This is the unique advantage of the proposed approach. Furthermore, it is equipped with a novel robust control term to improve controller robustness against system uncertainties and unknown disturbances. An important feature of the proposed approach is that it is a model-free controller. In addition, to determine the control design parameters and avoid trial and error, the Teaching–learning-based optimization algorithm (TLBO) is employed to regulate these parameters and enhance the performance of the proposed controller. Based on the Lyapunov stability theory, it is proven that the proposed control scheme can guarantee the synchronization and the stability of closed-loop control system. The case study is the Duffing–Holmes oscillator. Comparative simulation results are presented which confirm the superiority of the proposed approach.

Majid Moradi Zirkohi

Papers

Interval type-2 fuzzy-neural network indirect adaptive sliding mode control for an active suspension system

Adaptive impedance control of a hydraulic suspension system using particle swarm optimisation

Type-2 Fuzzy Control for a Flexible- joint Robot Using Voltage Control Strategy

Direct Adaptive Function Approximation Techniques Based Control of Robot Manipulators

Chaos synchronization using higher-order adaptive PID controller