Control Of Constrained Nonlinear Systems Research Articles

Adaptive Tracking Control of Constrained Nonlinear Systems and Its Application to Circuit Systems

Adaptive Tracking Control of Constrained Nonlinear Systems and Its Application to Circuit Systems

Self-Organizing Model Predictive Control for Constrained Nonlinear Systems

Self-Organizing Model Predictive Control for Constrained Nonlinear Systems

Observer-Based Adaptive Finite-Time Neural Control for Constrained Nonlinear Systems With Actuator Saturation Compensation

Observer-Based Adaptive Finite-Time Neural Control for Constrained Nonlinear Systems With Actuator Saturation Compensation

Output Feedback Learning Control of Constrained Nonlinear Systems

Output Feedback Learning Control of Constrained Nonlinear Systems

Finite-Time Adaptive Fuzzy Event-Triggered Control of Constrained Nonlinear Systems via Bounded Command Filter

In this note, finite-time adaptive tracking control is studied for nonlinear systems with unmodeled dynamics, asymmetric time-varying output constraints, and uncertain disturbances. Without any growth conditions, fuzzy logic systems are applied to tackle the unknown complicated functions. A novel backstepping approach is developed by combining barrier Lyapunov functions and bounded finite-time command filter. By regulating the threshold parameters online, a new dynamic event-triggered controller is established to ensure that all the signals of the closed-loop system are bounded and the output can follows the preset signal in finite time. Meanwhile, the output is always staying in an asymmetric time-varying interval all the time. The feasibility of the proposed control algorithm is verified with a numerical example and a practical application.

A Safety-Certified Policy Iteration Algorithm for Control of Constrained Nonlinear Systems

This letter designs safe controllers with guaranteed performance for continuous-time systems under state constraints. While existing safe control frameworks mainly ignore the performance of the controller and existing optimal control frameworks ignore the safety of the controller, the presented approach brings the best of both worlds of safe control design and optimal control design together. To this end, a novel safe policy iteration algorithm is presented that iteratively finds an optimal controller while certifying the safety of the improved control policy at each iteration. To avoid solving a Hamilton-Jacobi Bellman (HJB) equality for finding the optimal control policy, which does not guarantee safety, an HJB inequality is solved that can be integrated with barrier certificate to verify the safety of the control policy. Sum-of-Squares program is employed to implement the presented policy iteration algorithm and thus iteratively find a safe control solution with guaranteed performance. A simulation example is provided to verify the effectiveness of the proposed approach.

The Explicit Reference Governor: A General Framework for the Closed-Form Control of Constrained Nonlinear Systems

The systematic design of control laws for nonlinear systems subject to state and input constraints is one of the major challenges in the control of realworld systems. Depending on the application, the desired solution must achieve a satisfactory tradeoff between multiple and often contradicting requirements such as optimality, computational efficiency, robustness, reliability, tuning simplicity, and generality. This article proposes a novel framework for the closed-form control of nonlinear systems subject to constraints on the state and input variables. The proposed strategy differs from most existing methods because it systematically handles both state and input constraints without making use of online optimization.

Sliding Mode Control of Constrained Nonlinear Systems

This technical note introduces the design of sliding mode control algorithms for nonlinear systems in the presence of hard inequality constraints on both control and state variables. Relying on general results on minimum-time higher-order sliding mode for unconstrained systems, a general order control law is formulated to robustly steer the state to the origin, while satisfying all the imposed constraints. Results on minimum-time convergence to the sliding manifold, as well as on the maximization of the domain of attraction, are analytically proved for the first-order and second-order sliding mode cases. A general result is presented regarding the domain of attraction in the general order case, while numerical results on the estimation of the domain of attraction and on minimum-time convergence are discussed for the third-order case, following a procedure applicable to a sliding mode of any order.

Network-Based Predictive Control for Constrained Nonlinear Systems With Two-Channel Packet Dropouts

This paper investigates the predictive control scheme and the associated stability issue for the constrained nonlinear networked control systems (NCSs), where both the sensor-to-controller packet dropout and the controller-to-actuator packet dropout are considered simultaneously. The model predictive control based framework is proposed to compensate for the two-channel packet dropouts. This framework consists of two main aspects: 1) to design the control packets by solving a constrained optimization problem and 2) to synthesize an efficient packet transmission and compensation mechanism based on the Transmission Control Protocol. To study the stability of the resultant nonlinear NCS, we propose a novel Lyapunov function, based on which the conditions for ensuring the regional input-to-state practical stability are developed. Finally, the proposed control strategy is applied to a spring-and-cart system to demonstrate the applicability and effectiveness.

Adaptive Model Predictive Control for Constrained Nonlinear Systems

A true adaptive nonlinear model predictive control (MPC) algorithm must address the issue of robustness to model uncertainty while the estimator is evolving. Unfortunately, this may not be achieved without introducing extra degree of conservativeness and/or computational complexity in the controller calculations. To attenuate this problem, we employ a finite time identifier and propose an adaptive predictive control structure that reduces to a nominal MPC problem when exact parameter estimates are obtained. The adaptive MPC is formulated in such a way that useful excitation is automatically injected into the closed loop system to decrease the identification period.

Norm-bounded robust MPC strategies for constrained control of nonlinear systems

This paper considers nonlinear predictive control via embedding strategies. A novel robust MPC algorithm for input-saturated uncertain discrete-time linear systems subject to norm-bounded (LFR) model uncertainties is presented and contrasted with direct nonlinear and robust polytopic MPC methods in two final nonlinear examples: a mildly nonlinear two-tanks hydraulic system and a highly nonlinear CSTR plant. From the examples it results that the embedding strategies compare favourably with direct nonlinear MPC in the mildly nonlinear system whereas their performance may degrade, often considerably though exceptions are not rare, in highly nonlinear plants. However, the numerical savings achievable by adopting embedding approaches are remarkable, especially for the proposed solution.

Constrained nonlinear MPC using hammerstein and wiener models: PLS framework

AbstractMethods of implementing an input‐constrained, nonlinear, model‐predictive controller in latent spaces using partial‐least‐squares (PLS)‐based Hammerstein and Wiener models are discussed. For multiple‐input, multiple‐output (MIMO) systems, the PLS flamework presents a viable alternative for identification and controller synthesis using Hammerstein and Wiener structures. The constraint mappings, which have to be taken into account during controller design in the PLS flamework, are highlighted. PLS‐based Wiener models are well suited for constrained control of nonlinear systems. The use of PLS‐based Hammerstein models for control involves solution of a nonlinear program as a result of the constraint mapping. The proposed approach is demonstrated on a simulated pH‐level control of an acid base neutralization process.

Constrained Controllability of Nonlinear Systems

In this paper infinite-dimensional dynamical systems described by nonlinear abstract differential equations are considered. Using the generalized open mapping theorem sufficient conditions for constrained exact local controllability are formulated and proved. It is generally assumed that the values of controls are in a convex and closed cone with vertex at zero. As an illustrative example a constrained exact local controllability problem for a nonlinear delayed dynamical system is solved in detail. Some remarks and comments on the existing results for controllability of nonlinear dynamical systems are also presented.

Robustness of MPC-Based Schemes for Constrained Control of Nonlinear Systems

An efficient method for the robustness analysis of model predictive control (MPC) based techniques for constrained nonlinear control is proposed. Performance properties of nonlinear MPC (NLMPC) and MPC with feedback linearization (FL) are measured in terms of an induced norm. The robust ("worst case") performance is evaluated by solving a dynamical optimization problem with a newly developed power algorithm. The efficiency of the proposed method is demonstrated on two test plants, where the robustness properties of MPC+FL and NLMPC are compared.

Constrained controllability of nonlinear systems

In the paper, infinite-dimensional dynamical systems described by nonlinear abstract differential equations are considered. Using a generalized open-mapping theorem sufficient conditions for constrained exact local controllability are formulated and proved. It is generally assumed that the values of controls are in a convex and closed cone with the vertex at zero. As an illustrative example. the constrained exact local controllability problem for nonlinear delayed dynamical system is solved in detail. Some remarks and comments on the existing results for controllability of nonlinear dynamical systems are also presented.