Adaptive self-recurrent wavelet neural network and sliding mode controller/observer for a slider crank mechanism

In this paper, a novel control strategy based on an adaptive Self-Recurrent Wavelet Neural Network (SRWNN) and a sliding mode controller/observer for a slider crank mechanism is proposed. The aim is to reduce the tracking error of the linear displacement of this mechanism while following a specified trajectory. The controller design consists of two parts. The first one is a sliding mode control strategy and the second part is an SRWNN controller. This controller is trained offline first, and then the SRWNN weights are updated online by the adaptive control law. Apart from the hybrid control strategy proposed in this paper, a velocity observer is implemented to replace the use of velocity sensors. The outcomes obtained in the numerical experiment section prove that the smallest tracking error is obtained for the linear and angular displacements in comparison with other strategies found in literature due to the uncertainty and disturbance rejection properties of the sliding mode and the self-recurrent wavelet neural network controllers.Peer ReviewedPostprint (author's final draft

Robust Multi-Objective Control of Power System Stabilizer Using Mixed H2/H∞ and µ Analysis

In order to study the dynamic stability of the system, having a precise dynamic model including the energy generation units such as generators, excitation system and turbine is necessary. The aim of this paper is to design a power stabilizer for Mashhad power plant and assess its effects on the electromechanical fluctuations. Due to lack of certainty in the system, designing an optimized robust controller is crucial. In this paper, the establishment of balance between the nominal and robust performance is done by the weight functions. In the frequencies where the uncertainty is high, in order to achieve to the robust performance of the controller, μ analysis is more profound, otherwise, in order to achieve to nominal performance, robust stability, noise reduction and decrease of controlling signal, the impact of the controller H2/H∞ is more profound. The results of the simulation studies represent the advantages and effectiveness of the suggested method

Coexistence of generalized synchronization and inverse generalized synchronization between chaotic and hyperchaotic systems

In this paper, we present new schemes to synchronize different dimensional chaotic and hyperchaotic systems. Based on coexistence of generalized synchronization (GS) and inverse generalized synchronization (IGS), a new type of hybrid chaos synchronization is constructed. Using Lyapunov stability theory and stability theory of linear continuous-time systems, some sufficient conditions are derived to prove the coexistence of generalized synchronization and inverse generalized synchronization between 3D master chaotic system and 4D slave hyperchaotic system. Finally, two numerical examples are illustrated with the aim to show the effectiveness of the approaches developed herein

A Robust Controller Design for Simple Robotic Human Arm

Nowadays, the manipulator of two degrees of freedom (2DOF) has many applications. One is a human arm that may be utilized in robotic rehabilitation. The 2DOF controlled robot manipulator usually acts like human arms. This paper aims to design a robust, stable controller for the upper limb robotic model. A sliding mode control (SMC) approach is proposed to realize stability, tracing accuracy, and robustness for 2DOF robotic manipulator. Based on the general manipulator equation of motion, two SMCs are designed. The first is designed according to the input–output stability constraints. The second is designed according to the adaptive law. Not only the trajectory tracking is guaranteed but also stability is ensured. The stability of the controllers is examined based on Lyapunov stability criteria. The controllers and the robotic arm are formulated analytically. The MATLAB platform is adopted to examine and validate the proposed controller’s performance. The addition of adaptation law in the SMC scheme improves the results for the two designed controllers and shows remarkable trajectory tracking and system stability as well. The improvement rate shows an enhancement of 40.5% and 36.7% for manipulator joints 1 and 2, respectively

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme

Sliding mode control for DC electric drive

The paper deals with the construction of multi-loop control system of DC electric drive. Suggested system is robust, invariant to parametric and coordinate disturbance, and asymptotically stable due to using first order sliding mode control algorithms. Contrary to well-known sliding mode algorithms we propose to use a sliding mode for both goals. At first, we offer to transform dynamic of control object into Brunovsky form by using sliding mode controller. Such transformation allows us to construct a robust and invariant closed loop control system and to form dynamic of the control object. Then, we suggest using sliding mode controller for control of the transformed object. Such approach permits to construct a precise and an asymptotically stable control system. We use the proposed approach for construction current and speed controllers for DC electric drive and proving of our method's benefits by simulation of the constructed system

Design of sliding-mode observer for a class of uncertain neutral stochastic systems

© 2016 Informa UK Limited, trading as Taylor & Francis Group. The problem of robust H∞ control for a class of uncertain neutral stochastic systems (NSS) is investigated by utilising the sliding-mode observer (SMO) technique. This paper presents a novel observer and integral-type sliding-surface design, based on which a new sufficient condition guaranteeing the resultant sliding-mode dynamics (SMDs) to be mean-square exponentially stable with a prescribed level of H∞ performance is derived. Then, an adaptive reaching motion controller is synthesised to lead the system to the predesigned sliding surface in finite-time almost surely. Finally, two illustrative examples are exhibited to verify the validity and superiority of the developed scheme