Q-LEARNING, POLICY ITERATION AND ACTOR-CRITIC REINFORCEMENT LEARNING COMBINED WITH METAHEURISTIC ALGORITHMS IN SERVO SYSTEM CONTROL

This paper carries out the performance analysis of three control system structures and approaches, which combine Reinforcement Learning (RL) and Metaheuristic Algorithms (MAs) as representative optimization algorithms. In the first approach, the Gravitational Search Algorithm (GSA) is employed to initialize the parameters (weights and biases) of the Neural Networks (NNs) involved in Deep Q-Learning by replacing the traditional way of initializing the NNs based on random generated values. In the second approach, the Grey Wolf Optimizer (GWO) algorithm is employed to train the policy NN in Policy Iteration RL-based control. In the third approach, the GWO algorithm is employed as a critic in an Actor-Critic framework, and used to evaluate the performance of the actor NN. The goal of this paper is to analyze all three RL-based control approaches, aiming to determine which one represents the best fit for solving the proposed control optimization problem. The performance analysis is based on non-parametric statistical tests conducted on the data obtained from real-time experimental results specific to nonlinear servo system position control

A Unified Anti-Windup Technique for Fuzzy and Sliding Mode Controllers

This paper proposes the unified treatment of an anti-windup technique for fuzzy and sliding mode controllers. A back-calculation and tracking anti-windup scheme is proposed in order to prevent the zero error integrator wind-up in the structures of state feedback fuzzy controllers and sliding mode controllers. The state feedback sliding mode controllers are based on the state feedback-based computation of the switching variable. An example that copes with the position control of an electro-hydraulic servo-system is presented. The conclusions are pointed out on the basis of digital simulation results for the state feedback fuzzy controller

Data-Driven Model-Free Sliding Mode and Fuzzy Control with Experimental Validation

The paper presents the combination of the model-free control technique with two popular nonlinear control techniques, sliding mode control and fuzzy control. Two data-driven model-free sliding mode control structures and one data-driven model-free fuzzy control structure are given. The data-driven model-free sliding mode control structures are built upon a model-free intelligent Proportional-Integral (iPI) control system structure, where an augmented control signal is inserted in the iPI control law to deal with the error dynamics in terms of sliding mode control. The data-driven model-free fuzzy control structure is developed by fuzzifying the PI component of the continuous-time iPI control law. The design approaches of the data-driven model-free control algorithms are offered. The data-driven model-free control algorithms are validated as controllers by real-time experiments conducted on 3D crane system laboratory equipment

Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots

This paper proposes two applications of Grey Wolf Optimizer (GWO) algorithms to a path planning (PaPl) problem and a Proportional-Integral (PI)-fuzzy controller tuning problem. Both optimization problems solved by GWO algorithms are explained in detail. An off-line GWO-based PaPl approach for Nonholonomic Wheeled Mobile Robots (NWMRs) in static environments is proposed. Once the PaPl problem is solved resulting in the reference trajectory of the robots, the paper also suggests a GWO-based approach to tune cost-effective PI-fuzzy controllers in tracking control problem for NWMRs. The experimental results are demonstrated through simple multiagent settings conducted on the nRobotic platform developed at the Politehnica University of Timisoara, Romania, and they prove both the effectiveness of the two GWO-based approaches and major performance improvement

A LOW-COST APPROACH TO DATA-DRIVEN FUZZY CONTROL OF SERVO SYSTEMS

Servo systems become more and more important in control systems applications in various fields as both separate control systems and actuators. Ensuring very good control system performance using few information on the servo system model (viewed as a controlled process) is a challenging task. Starting with authors’ results on data-driven model-free control, fuzzy control and the indirect model-free tuning of fuzzy controllers, this paper suggests a low-cost approach to the data-driven fuzzy control of servo systems. The data-driven fuzzy control approach consists of six steps: (i) open-loop data-driven system identification to produce the process model from input-output data expressed as the system step response, (ii) Proportional-Integral (PI) controller tuning using the Extended Symmetrical Optimum (ESO) method, (iii) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller in terms of the modal equivalence principle, (iv) closed-loop data-driven system identification, (v) PI controller tuning using the ESO method, (vi) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller. The steps (iv), (v) and (vi) are optional. The approach is applied to the position control of a nonlinear servo system. The experimental results obtained on laboratory equipment validate the approach

Haptic Human Interfaces for Robotic Telemanipulation

Abstract -This paper proposes that there are ultimately only two topi cal tactile feedback generation modalities for haptic human interfaces which allow the human operator to handle either (i) temporary VR-based material replicas of the local geometric and/or force profile at the contact areas of an unlimited set of generic objects that could virtually be handled during the manipulation, or (ii) permanent material replicas of a limited set of typical objects. Examples of tactile human interfaces developed by the authors for telerobotic blind tactile exploration of objects, for telerobotic hapto-visual stylus-style tool manipulation are presented to illustrate the proposed approach. A NN architecture allowing for the modelling of the elastic properties of 3D objects from experimental tactile and range imaging data is also presented

Flow-Velocity Measurement for Bulk Granular Solids in Pneumatic Conveyor Pipes Using Random-Data Correlator Architecture

Abstract-This paper discusses a correlative-measurement technique for the evaluation of the flow velocity of bulk granular solids moving through the pneumatic conveyor pipes in both the dense and dilute phases. Flow velocities are recovered from the cross-correlation functions between the pairs of signals produced by the noninvasive capacitive sensors placed in circular layers at a given distance on the conveyor pipe. A random-data correlator architecture is discussed as a cost-effective solution for the real-time computation of the multiple correlation functions used for the estimation of the cross-sectional tomographic model of the flow-velocity profile in the dilute phase. Index Terms-Dense phase, dilute phase, particle flow velocity, pneumatic transport of bulk granular solids, random-data correlator architecture

A CENTER MANIFOLD THEORY-BASED APPROACH TO THE STABILITY ANALYSIS OF STATE FEEDBACK TAKAGI-SUGENO-KANG FUZZY CONTROL SYSTEMS

The aim of this paper is to propose a stability analysis approach based on the application of the center manifold theory and applied to state feedback Takagi-Sugeno-Kang fuzzy control systems. The approach is built upon a similar approach developed for Mamdani fuzzy controllers. It starts with a linearized mathematical model of the process that is accepted to belong to the family of single input second-order nonlinear systems which are linear with respect to the control signal. In addition, smooth right-hand terms of the state-space equations that model the processes are assumed. The paper includes the validation of the approach by application to stable state feedback Takagi-Sugeno-Kang fuzzy control system for the position control of an electro-hydraulic servo-system