Fuzzy Model-Reference Adaptive Control Method For An Underwater Robotic Manipulator

Pengendali robotik dalam air (URM) adalah berbeza jika dibandingkan dengan pengendali robotik biasa atau yg berada di permukaan. Dinamiknya mempunyai ketidakpastian yang besar bergantung kepada daya apungan, daya yang dihasilkan oleh jisim tambahan/momen luas kedua dan daya geseran. Tambahan lagi, ia juga dipengaruhi oleh gangguan luaran yang penting seperti arus dan ombak. The underwater robotic manipulators (URMs) are different with the ordinary or landbased robotic manipulators. Its dynamics have large uncertainties owing to the buoyancy, force induced by the added mass/moment of inertia and the drag force. Moreover, they are also affected by the crucial external disturbances such as currents and waves

Optimal Control of Unknown Nonlinear System From Inputoutput Data

Optimal control designers usually require a plant model to design a controller. The problem is the controller\u27s performance heavily depends on the accuracy of the plant model. However, in many situations, it is very time-consuming to implement the system identification procedure and an accurate structure of a plant model is very difficult to obtain. On the other hand, neuro-fuzzy models with product inference engine, singleton fuzzifier, center average defuzzifier, and Gaussian membership functions can be easily trained by many well-established learning algorithms based on given input-output data pairs. Therefore, this kind of model is used in the current optimal controller design. Two approaches of designing optimal controllers of unknown nonlinear systems based on neuro-fuzzy models are presented in the thesis. The first approach first utilizes neuro-fuzzy models to approximate the unknown nonlinear systems, and then the feasible-direction algorithm is used to achieve the numerical solution of the Euler-Lagrange equations of the formulated optimal control problem. This algorithm uses the steepest descent to find the search direction and then apply a one-dimensional search routine to find the best step length. Finally several nonlinear optimal control problems are simulated and the results show that the performance of the proposed approach is quite similar to that of optimal control to the system represented by an explicit mathematical model. However, due to the limitation of the feasible-direction algorithm, this method cannot be applied to highly nonlinear and dimensional plants. Therefore, another approach that can overcome these drawbacks is proposed. This method utilizes Takagi-Sugeno (TS) fuzzy models to design the optimal controller. TS fuzzy models are first derived from the direct linearization of the neuro-fuzzy models, which is close to the local linearization of the nonlinear dynamic systems. The operating points are chosen so that the TS fuzzy model is a good approximation of the neuro-fuzzy model. Based on the TS fuzzy model, the optimal control is implemented for a nonlinear two-link flexible robot and a rigid asymmetric spacecraft, thus providing the possibility of implementing the well-established optimal control method on unknown nonlinear dynamic systems

Fuzzy Controllers

Trying to meet the requirements in the field, present book treats different fuzzy control architectures both in terms of the theoretical design and in terms of comparative validation studies in various applications, numerically simulated or experimentally developed. Through the subject matter and through the inter and multidisciplinary content, this book is addressed mainly to the researchers, doctoral students and students interested in developing new applications of intelligent control, but also to the people who want to become familiar with the control concepts based on fuzzy techniques. Bibliographic resources used to perform the work includes books and articles of present interest in the field, published in prestigious journals and publishing houses, and websites dedicated to various applications of fuzzy control. Its structure and the presented studies include the book in the category of those who make a direct connection between theoretical developments and practical applications, thereby constituting a real support for the specialists in artificial intelligence, modelling and control fields

Global Feed-Forward Adaptive Fuzzy Control of Uncertain MIMO Nonlinear Systems

This study proposes a novel adaptive control approach using a feedforward Takagi-Sugeno (TS) fuzzy approximator for a class of highly unknown multi-input multi-output (MIMO) nonlinear plants. First of all, the design concept, namely, feedforward fuzzy approximator (FFA) based control, is introduced to compensate the unknown feedforward terms required during steady state via a forward TS fuzzy system which takes the desired commands as the input variables. Different from the traditional fuzzy approximation approaches, this scheme allows easier implementation and drops the boundedness assumption on fuzzy universal approximation errors. Furthermore, the controller is synthesized to assure either the disturbance attenuation or the attenuation of both disturbances and estimated fuzzy parameter errors or globally asymptotic stable tracking. In addition, all the stability is guaranteed from a feasible gain solution of the derived linear matrix inequality (LMI). Meanwhile, the highly uncertain holonomic constrained systems are taken as applications with either guaranteed robust tracking performances or asymptotic stability in a global sense. It is demonstrated that the proposed adaptive control is easily and straightforwardly extended to the robust TS FFA-based motion/force tracking controller. Finally, two planar robots transporting a common object is taken as an application example to show the expected performance. The comparison between the proposed and traditional adaptive fuzzy control schemes is also performed in numerical simulations. Keywords: Adaptive control; Takagi-Sugeno (TS) fuzzy system; holonomic systems; motion/force control

Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

Robot manipulator has become an exciting topic for many researchers during several decades. They have investigated the advanced algorithms such as sliding mode control, neural network, or genetic scheme to implement these developments. However, they lacked the integration of these algorithms to explore many potential expansions. Simultaneously, the complicated system requires a lot of computational costs, which is not always supported. Therefore, this paper presents a novel design of switching mechanisms to control the robot manipulator. This investigation is expected to achieve superior performance by flexibly adjusting various strategies for better selection. The Proportional-Integral-Derivative (PID) scheme is well-known, easy to implement, and ensures rapid computation while it might not have much control effect. The advanced interval type-2 fuzzy sliding mode control properly deals with nonlinear factors and disturbances. Consequently, the PID scheme is switched when the tracking error is less than the threshold or is far from the target. Otherwise, the interval type-2 fuzzy sliding mode control scheme is activated to cope with unknown factors. The main contributions of this paper are (i) the recommendation of a suitable switching mechanism to drive the robot manipulator, (ii) the successful integration of the interval type-2 fuzzy sliding mode control to track the desired trajectory, and (iii) the launching of several tests to validate the proposed controller with robot model. From these achievements, it would be stated that the proposed approach is effective in tracking performance, robust in disturbance-rejection, and feasible in practical implementation

Intelligent systems for active vibration control in flexible engineering structures / by Ji Xiaoxu.

Vibration arises in almost all moving structures. Vibration control is important to many applications such as robotic arms, aircraft wings, buildings in wind, vehicle transmission systems,to name but a few. The objective of this thesis is to develop more efficient intelligent controllers for vibration suppression, mainly for time-varying flexible structures. At first, based on TSO and TSl fuzzy models, novel neural-fuzzy (NF) controllers are developed for active vibration control of the flexible structures. The NF control paradigms are intended to integrate the advantages from both fuzzy logic and neural networks while overcoming their respective limitations. The control reasoning is undertaken by fuzzy logic whereas the fuzzy control system is optimized by neural network related training algorithms. A new strategy is suggested to simplify the architectures of the classical NF controllers so as to make the control process computationally efficient for real-time applications. A recurrent identification network (RIN) is developed to adaptively identify system dynamics of the timevarying flexible structures. When system dynamics (e.g., mass, stiffness, and damping) varies, the proposed RIN and NF controller can effectively recognize the system’s new dynamics and perform corresponding control operations. A novel hybrid training technique based on real time recurrent learning (RTRL) and least square estimate (LSE) is suggested for real-time training of the RIN scheme to optimize its nonlinear input-output mapping. The effectiveness of the developed intelligent controllers and the related techniques has been verified by online experimental tests of corresponding fixed and time-varying dynamic conditions. Test results have shown that the developed adaptive NF controller outperforms the classical controllers (e.g., PD) and other related intelligent control strategies

Systematic Design of Type-2 Fuzzy Logic Systems for Modeling and Control with Applications to Modular and Reconfigurable Robots

Fuzzy logic systems (FLSs) are well known in the literature for their ability to model linguistics and system uncertainties. Due to this ability, FLSs have been successfully used in modeling and control applications such as medicine, finance, communications, and operations research. Moreover, the ability of higher order fuzzy systems to handle system uncertainty has become an interesting topic of research in the field. In particular, type-2 FLSs (T2 FLSs), systems consisting of fuzzy sets with fuzzy grades of membership, a feature that type-1 (T1) does not offer, are most well-known for this capability. The structure of T2 FLSs allows for the incorporation of uncertainty in the input membership grades, a common situation in reasoning with physical systems. General T2 FLSs have a complex structure, thus making them difficult to adopt on a large scale. As a result, interval T2 FLSs (IT2 FLSs), a special class of T2 FLSs, have recently shown great potential in various applications with input-output (I/O) system uncertainties. Due to the sophisticated mathematical structure of IT2 FLSs, little to no systematic analysis has been reported in the literature to use such systems in control design. Moreover, to date, designers have distanced themselves from adopting such systems on a wide scale because of their design complexity. Furthermore, the very few existing control methods utilizing IT2 fuzzy logic control systems (IT2 FLCSs) do not guarantee the stability of their system. Therefore, this thesis presents a systematic method for designing stable IT2 Takagi-Sugeno-Kang (IT2 TSK) fuzzy systems when antecedents are T2 fuzzy sets and consequents are crisp numbers (A2-C0). Five new inference mechanisms are proposed that have closed-form I/O mappings, making them more feasible for FLCS stability analysis. The thesis focuses on control applications for when (a) both plant and controller use A2-C0 TSK models, and (b) the plant uses T1 Takagi-Sugeno (T1 TS) and the controller uses IT2 TS models. In both cases, sufficient stability conditions for the stability of the closed-loop system are derived. Furthermore, novel linear matrix inequality-based algorithms are developed for satisfying the stability conditions. Numerical analyses are included to validate the effectiveness of the new inference methods. Case studies reveal that a well-tuned IT2 TS FLCS using the proposed inference engine can potentially outperform its T1 TSK counterpart, a result of IT2 having greater structural flexibility than T1. Moreover, due to the simple nature of the proposed inference engine, it is easy to implement in real-time control systems. In addition, a novel design methodology is proposed for IT2 TSK FLC for modular and reconfigurable robot (MRR) manipulators with uncertain dynamic parameters. A mathematical framework for the design of IT2 TSK FLCs is developed for tracking purposes that can be effectively used in real-time applications. To verify the effectiveness of the proposed controller, experiments are performed on an MRR with two degrees of freedom which exhibits dynamic coupling behavior. Results show that the developed controller can outperform some well-known linear and nonlinear controllers for different configurations. Therefore, the proposed structure can be adopted for the position control of MRRs with unknown dynamic parameters in trajectory-tracking applications. Finally, a rigorous mathematical analysis of the robustness of FLSs (both T1 and IT2) is presented in the thesis and entails a formulation of the robustness of FLSs as a constraint multi-objective optimization problem. Consequently, a procedure is proposed for the design of robust IT2 FLSs. Several examples are presented to demonstrate the effectiveness of the proposed methodologies. It was concluded that both T1 and IT2 FLSs can be designed to achieve robust behavior in various applications. IT2 FLSs, having a more flexible structure than T1 FLSs, exhibited relatively small approximation errors in the several examples investigated. The rigorous methodologies presented in this thesis lay the mathematical foundations for analyzing the stability and facilitating the design of stabilizing IT2 FLCSs. In addition, the proposed control technique for tracking purposes of MRRs will provide control engineers with tools to control dynamic systems with uncertainty and changing parameters. Finally, the systematic approach developed for the analysis and design of robust T1 and IT2 FLSs is of great practical value in various modeling and control applications

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance