ADAPTIVE DYNAMICAL FEEDBACK REGULATION STRATEGIES FOR LINEARIZABLE UNCERTAIN SYSTEMS

In this paper we address the design of adaptive dynamical feedback strategies of the continuous and discontinuous, types for the output stabilization of nonlinear systems. The class of systems considered corresponds to nonlinear controlled systems exhibiting linear parametric uncertainty. Dynamical feedback controllers, ideally achieving output stabilization via exact linearization, are obtained by means of repeated output differentiation and, either, pole placement, or, sliding mode control techniques. The adaptive versions of the dynamical stabilizing controllers are then obtainable through standard, direct, overparamemzed adaptive control strategies available for linearizable systems. Illustrative examples are provided which deal with the regulation of electromechanical systems

Optimal adaptive control of time-delay dynamical systems with known and uncertain dynamics

Delays are found in many industrial pneumatic and hydraulic systems, and as a result, the performance of the overall closed-loop system deteriorates unless they are explicitly accounted. It is also possible that the dynamics of such systems are uncertain. On the other hand, optimal control of time-delay systems in the presence of known and uncertain dynamics by using state and output feedback is of paramount importance. Therefore, in this research, a suite of novel optimal adaptive control (OAC) techniques are undertaken for linear and nonlinear continuous time-delay systems in the presence of uncertain system dynamics using state and/or output feedback. First, the optimal regulation of linear continuous-time systems with state and input delays by utilizing a quadratic cost function over infinite horizon is addressed using state and output feedback. Next, the optimal adaptive regulation is extended to uncertain linear continuous-time systems under a mild assumption that the bounds on system matrices are known. Subsequently, the event-triggered optimal adaptive regulation of partially unknown linear continuous time systems with state-delay is addressed by using integral reinforcement learning (IRL). It is demonstrated that the optimal control policy renders asymptotic stability of the closed-loop system provided the linear time-delayed system is controllable and observable. The proposed event-triggered approach relaxed the need for continuous availability of state vector and proven to be zeno-free. Finally, the OAC using IRL neural network based control of uncertain nonlinear time-delay systems with input and state delays is investigated. An identifier is proposed for nonlinear time-delay systems to approximate the system dynamics and relax the need for the control coefficient matrix in generating the control policy. Lyapunov analysis is utilized to design the optimal adaptive controller, derive parameter/weight tuning law and verify stability of the closed-loop system”--Abstract, page iv

Distributed Adaptive Consensus Control of Nonlinear Output-Feedback Systems on Directed Graphs

This paper deals with consensus control in leader-follower format of a class of network-connected uncertain nonlinear systems by output feedback. Each subsystem is in the nonlinear output feedback form with unknown parameters, and the connection graph among the subsystems is directed. Distributed adaptive control inputs are designed to achieve the consensus control in the sense that the subsystem states asymptotically follow the subsystem at node 0 with no input, which is also known as the leader. The proposed adaptive control only uses relative output measurements and the local information of the connection to each subsystem, and hence the proposed adaptive control is fully distributed. The proposed scheme is different from the consensus output regulation schemes literature, and the leader plays a similar role as a reference model in the classic model reference adaptive control. (C) 2016 Elsevier Ltd. All rights reserved.National Natural Science Foundation of China [61473005, 11332001]; 111 Project [B08015]SCI(E)[email protected]; [email protected]

Adaptive output regulation for a class of nonlinear systems with guaranteed transient performance

This paper is dedicated to adaptive output regulation for a class of nonlinear systems with asymptotic output tracking and guarantee of prescribed transient performance. With the employment of internal model principle, we first transform this problem into a specific adaptive stabilization problem with output constraints. Then, by integrating the time-varying Barrier Lyapunov Function (BLF) technique together with the high gain feedback method, we develop an output-based control law to solve the constrained stabilization problem and consequently confine the output tracking error to a predefined arbitrary region. The output-based control law enables adaptive output regulation in the sense that, under unknown exosystem dynamics, all the closed-loop system signals are bounded whilst the controlled output constraints are not violated. Finally, efficacy of the proposed design is illustrated through a simulation example

A nonparametric learning framework for nonlinear robust output regulation

This paper proposes a nonparametric learning solution framework for a generic internal model design of nonlinear robust output regulation. The global robust output regulation problem for a class of nonlinear systems with output feedback subject to a nonlinear exosystem can be tackled by constructing a linear generic internal model, provided that a continuous nonlinear mapping exists. An explicit continuous nonlinear mapping was constructed recently in [1] under the assumption that the steady-state generator is linear in the exogenous signal. We further relax such an assumption to a relaxed assumption that the steady-state generator is polynomial in the exogenous signal. A nonparametric learning framework is proposed to solve a linear time-varying equation to make the nonlinear continuous mapping always exist. With the help of the proposed framework, the nonlinear robust output regulation problem can be converted into a robust non-adaptive stabilization problem for the augmented system with integral Input-to-State Stable (iISS) inverse dynamics. Moreover, a dynamic gain approach can adaptively raise the gain to a sufficiently large constant to achieve stabilization without requiring any a priori knowledge of the uncertainties appearing in the dynamics of the exosystem and the system. We further apply the nonparametric learning framework to globally reconstruct and estimate multiple sinusoidal signals with unknown frequencies without using adaptive techniques. An explicit nonlinear mapping can directly provide the estimated parameters, which will exponentially converge to the unknown frequencies. As a result, a feedforward control design is proposed to solve the output regulation using our nonparametric learning framework.Comment: 15 pages; Nonlinear control; iISS stability; output regulation; parameter estimation; Non-adaptive contro

Robust Output Regulation for Autonomous Robots:self-learning mechanisms, task-space control and multi-agent systems

This thesis focuses on robust output regulation for autonomous robots. The control objective of output regulation is to design a feedback controller to achieve asymptotic tracking and/or disturbance rejection for a class of exogenous reference and/or disturbance while maintaining closed-loop stability. We investigate three research problems that pertain to the constructive design of robust output regulation for fully actuated Euler-Lagrange systems from centralized to distributed fashions. The first one is the global robust output regulation of second-order affine nonlinear systems with input disturbances that encompass the fully-actuated Euler-Lagrange systems. Based on a certainty equivalence principle method, we proposed a novel class of nonlinear internal models taking a cascade interconnection structure with strictly relaxed conditions than before. The second one is the output regulation for robot manipulators working in task-space. An internal model-based adaptive controller is designed to cope with uncertain manipulator kinematic and dynamic parameters, as well as unknown periodic reference trajectories generated by harmonic oscillators. The last one is the formation control of manipulators’ end-effector subject to external disturbances or parameter uncertainties. We present and analyze gradient descent-based distributed formation controllers for end-effectors. Internal models are used to reject external disturbances. Moreover, by introducing an extra integrator and an adaptive estimator for gravitational compensation and stabilization, respectively, we extend the proposed gradient-based design to the case where the plant parameters are not exactly known

Nonlinear control and its application to active tilting-pad bearings

The drawbacks of active magnetic bearings are arousing interest in the adaptation of mechanical bearings for active use. A promising mechanical bearing candidate for active operation is the tilting-pad bearing. In this research, we introduce an active tilting-pad bearing with linear actuators that translate each pad. The use of feedback in determining the actuator forces allows for the automatic, continuous adjustment of the pad position during the machine operation. In this work, we develop the dynamic model of the active bearing system such that the actuator forces are the control inputs. The hydrodynamic force is modeled as a spring/damper-like force with unknown damping and stiffness coefficients. Whereas in the literature, the damping and stiffness effects are normally considered linear, here, motivated by a numerical study based on the Reynolds equation, we use a nonlinear model for the stiffness force. An adaptive controller is designed to asymptotically regulate the rotor to the bearing center. The proposed control design is applicable to both the linear and nonlinear stiffness models. Simulations and experiments show that the active strategy improves the bearing performance in comparison to its traditional passive operation. Further, the experiments indicate the nonlinear stiffness-based controller slightly improves the active bearing regulation performance relative to the linear-based one. To the best of our knowledge, this dissertation is the first to report the experimental demonstration of an active tilting-pad bearing using feedback control. Since the model of the active tilting-pad bearing has a parametric strict-feedback-like form, the second part of this dissertation is dedicated to constructing new nonlinear control tools for this class of systems. Specifically, we consider the regulation and tracking control problems for multi-input/multi-output parametric strict-feedback systems in the presence of additive, exogenous disturbances and parametric uncertainties. For such systems, robust adaptive controllers usually cannot ensure asymptotic tracking or even regulation. In this work, under the assumption the disturbances are C2 with bounded time derivatives; we present a new C0 robust adaptive control construction that guarantees the output/tracking error is asymptotically driven to zero. Numerical examples illustrate the main results, including cases where the disturbances do not satisfy the aforementioned assumptions

Finite-horizon optimal control of linear and a class of nonlinear systems

Traditionally, optimal control of dynamical systems with known system dynamics is obtained in a backward-in-time and offline manner either by using Riccati or Hamilton-Jacobi-Bellman (HJB) equation. In contrast, in this dissertation, finite-horizon optimal regulation has been investigated for both linear and nonlinear systems in a forward-in-time manner when system dynamics are uncertain. Value and policy iterations are not used while the value function (or Q-function for linear systems) and control input are updated once a sampling interval consistent with standard adaptive control. First, the optimal adaptive control of linear discrete-time systems with unknown system dynamics is presented in Paper I by using Q-learning and Bellman equation while satisfying the terminal constraint. A novel update law that uses history information of the cost to go is derived. Paper II considers the design of the linear quadratic regulator in the presence of state and input quantization. Quantization errors are eliminated via a dynamic quantizer design and the parameter update law is redesigned from Paper I. Furthermore, an optimal adaptive state feedback controller is developed in Paper III for the general nonlinear discrete-time systems in affine form without the knowledge of system dynamics. In Paper IV, a NN-based observer is proposed to reconstruct the state vector and identify the dynamics so that the control scheme from Paper III is extended to output feedback. Finally, the optimal regulation of quantized nonlinear systems with input constraint is considered in Paper V by introducing a non-quadratic cost functional. Closed-loop stability is demonstrated for all the controller designs developed in this dissertation by using Lyapunov analysis while all the proposed schemes function in an online and forward-in-time manner so that they are practically viable --Abstract, page iv

Feedback Linearization of Nonlinear Systems: Robustness and Adaptive Control.

Feedback linearization provides an effective means of designing nonlinear control systems. This method permits one to have an exactly equivalent linear system by using a coordinate transformation and state feedback. Once the nonlinear system is transformed to a linear system, one can proceed with well developed control technologies for linear systems. Feedback linearization is based on a model of the real system. If there is mismatch between the model and the real plant, feedback linearization does not yield an exactly linear system. The question of robustness then arises: will a controller based on the model be stable when applied to the real plant? We have developed a theoretical approach to analyze robustness of feedback linearization of SISO (Single-Input Single-Output) systems. We have also considered the dimensional reduction of a high dimensional model which is not a standard singularly perturbed system. Specifically we have found sufficient conditions for boundedness and convergence of the system trajectories when feedback linearization based on a nominal mathematical model is applied to an uncertain real plant which may have parametric and structural uncertainties as well as unmodeled dynamics. The developed approach does not require the restrictive conditions which are commonly used in the previously developed methods of robustness analysis. Furthermore, for parametric uncertainties a nonlinear adaptive control of feedback linearizable processes is proposed. The main feature of the proposed nonlinear adaptive control system is that it is relatively straightforward and simple. For this adaptive control system we have found sufficient conditions for stability of the output regulation and tracking of feedback linearizable systems using the second method of Lyapunov. Examples of the robustness analysis and the adaptive control for unstable chemical and biochemical reactors are given

Optimal Adaptive Output Regulation of Uncertain Nonlinear Discrete-Time Systems using Lifelong Concurrent Learning

This Paper Addresses Neural Network (NN) based Optimal Adaptive Regulation of Uncertain Nonlinear Discrete-Time Systems in Affine Form using Output Feedback Via Lifelong Concurrent Learning. First, an Adaptive NN Observer is Introduced to Estimate Both the State Vector and Control Coefficient Matrix, and its NN Weights Are Adjusted using Both Output Error and Concurrent Learning Term to Relax the Persistency Excitation (PE) Condition. Next, by Utilizing an Actor-Critic Framework for Estimating the Value Functional and Control Policy, the Critic Network Weights Are Tuned Via Both Temporal Different Error and Concurrent Learning Schemes through a Replay Buffer. the Actor NN Weights Are Tuned using Control Policy Errors. to Attain Lifelong Learning for Performing Effectively during Multiple Tasks, an Elastic Weight Consolidation Term is Added to the Critic NN Weight Tuning Law. the State Estimation, Regulation, and the Weight Estimation Errors of the Observer, Actor and Critic NNs Are Demonstrated to Be Bounded When Performing Tasks by using Lyapunov Analysis. Simulation Results Are Carried Out to Verify the Effectiveness of the Proposed Approach on a Vander Pol Oscillator. Finally, Extension to Optimal Tracking is Given Briefly