Neural Adaptive Backstepping Control of a Robotic Manipulator With Prescribed Performance Constraint

IEEE This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom manipulator driven by an electrohydraulic actuator. To restrict the system output in a prescribed performance constraint, a weighted performance function is designed to guarantee the dynamic and steady tracking errors of joint angle in a required accuracy. Then, a radial-basis-function NN is constructed to train the unknown model dynamics of a manipulator by traditional backstepping control (TBC) and obtain the preliminary estimated model, which can replace the preknown dynamics in the backstepping iteration. Furthermore, an adaptive estimation law is adopted to self-tune every trained-node weight, and the estimated model is online optimized to enhance the robustness of the NN controller. The effectiveness of the proposed control is verified by comparative simulation and experimental results with Proportional-integral-derivative and TBC methods

Asymmetric bounded neural control for an uncertain robot by state feedback and output feedback

In this paper, an adaptive neural bounded control scheme is proposed for an n-link rigid robotic manipulator with unknown dynamics. With the combination of the neural approximation and backstepping technique, an adaptive neural network control policy is developed to guarantee the tracking performance of the robot. Different from the existing results, the bounds of the designed controller are known a priori, and they are determined by controller gains, making them applicable within actuator limitations. Furthermore, the designed controller is also able to compensate the effect of unknown robotic dynamics. Via the Lyapunov stability theory, it can be proved that all the signals are uniformly ultimately bounded. Simulations are carried out to verify the effectiveness of the proposed scheme

Nonlinear Model-Based Control for Neuromuscular Electrical Stimulation

Neuromuscular electrical stimulation (NMES) is a technology where skeletal muscles are externally stimulated by electrodes to help restore functionality to human limbs with motor neuron disorder. This dissertation is concerned with the model-based feedback control of the NMES quadriceps muscle group-knee joint dynamics. A class of nonlinear controllers is presented based on various levels of model structures and uncertainties. The two main control techniques used throughout this work are backstepping control and Lyapunov stability theory. In the first control strategy, we design a model-based nonlinear control law for the system with the exactly known passive mechanical that ensures asymptotical tracking. This first design is used as a stepping stone for the other control strategies in which we consider that uncertainties exist. In the next four control strategies, techniques for adaptive control of nonlinearly parameterized systems are applied to handle the unknown physical constant parameters that appear nonlinearly in the model. By exploiting the Lipschitzian nature or the concavity/convexity of the nonlinearly parameterized functions in the model, we design two adaptive controllers and two robust adaptive controllers that ensure practical tracking. The next set of controllers are based on a NMES model that includes the uncertain muscle contractile mechanics. In this case, neural network-based controllers are designed to deal with this uncertainty. We consider here voltage inputs without and with saturation. For the latter, the Nussbaum gain is applied to handle the input saturation. The last two control strategies are based on a more refined NMES model that accounts for the muscle activation dynamics. The main challenge here is that the activation state is unmeasurable. In the first design, we design a model-based observer that directly estimates the unmeasured state for a certain activation model. The second design introduces a nonlinear filter with an adaptive control law to handle parametric uncertainty in the activation dynamics. Both the observer- and filter-based, partial-state feedback controllers ensure asymptotical tracking. Throughout this dissertation, the performance of the proposed control schemes are illustrated via computer simulations

Finite-Time Tracking Control for a Class of MIMO Nonlinear Systems with Unknown Asymmetric Saturations

This paper addresses the problem of finite-time tracking control for multiple-input and multiple-output (MIMO) nonlinear systems with asymmetric saturations. A systematic approach is proposed to eliminate the effects of unmeasured external disturbances and unknown asymmetric saturations. In the proposed control strategy, a terminal sliding mode disturbance observer is provided to estimate the augmented disturbance (which contains the unknown asymmetric input saturation and external disturbance). The approximation error of the augmented disturbance can converge to zero in a fixed finite-time interval. Furthermore, a novel finite-time tracking control algorithm is developed to guarantee fast convergence of the tracking error. Compared with the existing results on finite-time tracking control, the chattering problem and the input saturation problem can be solved in a unified framework. Several simulations are given to demonstrate the effectiveness of the proposed approach

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

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

COOPERATIVE AND CONSENSUS-BASED CONTROL FOR A TEAM OF MULTI-AGENT SYSTEMS

Cooperative control has attracted a noticeable interest in control systems community due to its numerous applications in areas such as formation flying of unmanned aerial vehicles, cooperative attitude control of spacecraft, rendezvous of mobile robots, unmanned underwater vehicles, traffic control, data network congestion control and routing. Generally, in any cooperative control of multi-agent systems one can find a set of locally sensed information, a communication network with limited bandwidth, a decision making algorithm, and a distributed computational capability. The ultimate goal of cooperative systems is to achieve consensus or synchronization throughout the team members while meeting all communication and computational constraints. The consensus problem involves convergence of outputs or states of all agents to a common value and it is more challenging when the agents are subjected to disturbances, measurement noise, model uncertainties or they are faulty. This dissertation deals with the above mentioned challenges and has developed methods to design distributed cooperative control and fault recovery strategies in multi-agent systems. Towards this end, we first proposed a transformation for Linear Time Invariant (LTI) multi-agent systems that facilitates a systematic control design procedure and make it possible to use powerful Lyapunov stability analysis tool to guarantee its consensus achievement. Moreover, Lyapunov stability analysis techniques for switched systems are investigated and a novel method is introduced which is well suited for designing consensus algorithms for switching topology multi-agent systems. This method also makes it possible to deal with disturbances with limited root mean square (RMS) intensities. In order to decrease controller design complexity, a iii method is presented which uses algebraic connectivity of the communication network to decouple augmented dynamics of the team into lower dimensional parts, which allows one to design the consensus algorithm based on the solution to an algebraic Riccati equation with the same order as that of agent. Although our proposed decoupling method is a powerful approach to reduce the complexity of the controller design, it is possible to apply classical pole placement methods to the transformed dynamics of the team to develop and obtain controller gains. The effects of actuator faults in consensus achievement of multi-agent systems is investigated. We proposed a framework to quantitatively study actuator loss-of-effectiveness effects in multi-agent systems. A fault index is defined based on information on fault severities of agents and communication network topology, and sufficient conditions for consensus achievement of the team are derived. It is shown that the stability of the cooperative controller is linked to the fault index. An optimization problem is formulated to minimize the team fault index that leads to improvements in the performance of the team. A numerical optimization algorithm is used to obtain the solutions to the optimal problem and based on the solutions a fault recovery strategy is proposed for both actuator saturation and loss-of-effectiveness fault types. Finally, to make our proposed methodology more suitable for real life scenarios, the consensus achievement of a multi-agent team in presence of measurement noise and model uncertainties is investigated. Towards this end, first a team of LTI agents with measurement noise is considered and an observer based consensus algorithm is proposed and shown that the team can achieve H∞ output consensus in presence of both bounded RMS disturbance input and measurement noise. In the next step a multi-agent team with both linear and Lipschitz nonlinearity uncertainties is studied and a cooperative control algorithm is developed. An observer based approach is also developed to tackle consensus achievement problem in presence of both measurement noise and model uncertainties