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This paper considers the global output feedback stabilization of a class of upper-triangular nonlinear systems with a single input and multiple output. It is shown that under a structural condition, the output feedback stabilization problem can be solved for this class of systems by coupling with a finite-time convergent observer and a saturated homogeneous stabilizer. More general results are also...
AFM imaging requires precision positioning of the AFM probe relative to the sample in all x-y-z axes, especially the vertical z-axis direction. Recently, the current-cycle-feedback iterative-learning-control (CCF-ILC) approach is proposed for high-speed AFM imaging. The CCF-ILC feedforward-feedback 2 degree-of-freedom (DOF) controller design has been successfully implemented for iteratively imaging...
This paper presents an iterative learning controller (ILC) design method based on a frequency domain linear matrix inequality (LMI). It modifies the LMI method for lifted system representation. In lifted system representation, the size of matrices grows as the number of samples in the system increase. In the proposed approach, the size of matrices in LMI is independent of the number of samples in...
Iterative learning control (ILC) is one application area for the class of 2D linear systems known as repetitive processes where recently experimental evidence to support the performance possible by this approach has been reported. In this paper, we extend the ILC approach to the class of 2D linear systems that often arise from discretization of partial differential equations and, in particular, spatio-temporal...
By introducing the approximators, an indirect adaptive control method is presented for the control of a class of nonlinear systems. The proposed scheme can overcome the drawbacks of existing adaptive nonlinear control techniques which can only control minimum phase nonlinear systems and may easily result in a large control effort. The stability of closed-loop system was proved, and the convergence...
This paper presents a sliding mode control (SMC) design based on radial basis function neural network (RBFNN) to robust stabilization and disturbance rejection of the synchronous reluctance motor (SynRM) drive system. This method utilizes Lyapunov function and the steep descent rule to guarantee the convergence of the SynRM system asymptotically. Finally, we employ the experiments to validate the...
This paper proposes a method to suboptimally tune the control parameters in a conventional Lyapunov-based method which shares the same concept of control design with sliding mode approach as applied to the robot manipulators. Optimal tuning of such parameters involves handling of nonlinearities in system dynamics and cost functions, which makes the problem challenging. We propose a step-by-step numerical...
In this paper, it is introduced that the system structure and compensation principle of static Var compensator (SVC), as well as the basic principle of adaptive inverse control. In order to improve response rapidity without decreasing accuracy, the strategy of adaptive inverse control is applied to design the SVC controller. The simulation model and SVC experiment system has been built in this foundation...
This paper addresses the problem of coordination path following control of multiple autonomous vehicles. Stated briefly, the problem consists in steering a group of vehicles along a specified paths, while holding a desired inter-ship formation pattern. Path-following for each vehicle amounts to reducing an appropriately defined geometric error to zero. We first show a passivity property for the path...
Aiming at the serious saturation and dead zone nonlinearity, time-variability and time lag existing in pump-controlled cylinder electro-hydraulic servo system droved by SRM (Switched Reluctance Motor) directly, the ILC (iterative learning control) method was put forward to conduct the position servo control for hydraulic machine slider in this thesis. The mathematical model of SRM direct-drive electro-hydraulic...
The paper presents a new shuffled frog leaping algorithm (SFLA) for optimal design of fuzzy controllers. The SFLA is a meta-heuristic iterative search method inspired from the memetic evolution of a group of frogs when seeking for food. It consists of a frog leaping rule for local search and a memetic shuffling rule for global information exchange. In this paper, a new frog leaping rule with exponentially...
This paper apply genetic algorithm (GA) to design weighting matrices in linear quadratic regulator (LQR) for an inverted pendulum system (IPS). Feedback gain settings of the system are obtained by minimizing the performance index using GA to optimize the weight matrices of LQR. The divisional of the searching space mechanism and the dynamic crossover and mutation rate are used to insure getting the...
A new intelligent global terminal backstepping design method is proposed for a class of constrained input systems. It has several advantages as the following. Firstly, the differential bomb problem is avoided because of the no time derivative design concept. So the control law is simplified and the system becomes more stable and robust. Secondly, a quantitative analysis about the convergence time...
This paper proposes a simulator-based foresight control approach for nonlinear plant based on system identification. In the proposed method, as a main part of the simulator-based foresight control system, a simulator of an exact plant model of non-affine nonlinear plant is first constructed. If relative degree of disturbance is smaller than control input, servo type filter will be then installed in...
In this paper, a new robust Iterative Learning Control (ILC) algorithm has been proposed for linear systems in the presence of iteration-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem using a quadratic performance criterion subject to constraints of the control input update. An upper bound of the maximization problem is derived, then, the solution of the...
In this paper, interconnected iterative learning control loops are used to drive the output of general multi-input-multi-output nonlinear systems with general input uncertainties to the desired output. New convergence conditions are obtained to ensure the convergence of the overall system. A simulation example shows the effectiveness of the proposed method.
This paper proposes a novel method to synthesize robust proportional-integral-derivative (PID) controllers using particle swarm optimization (PSO). Robust control is well known for its ability in dealing with system uncertainties and disturbances. Standard robust control design, however, can result in controllers that are high-order and complicated and can be difficult to implement in practical applications...
In this paper, stabilization problem of parametric strict feedback class of discrete time chaotic systems is addressed. The methodology followed here utilizes recursive procedure based on contraction framework and explicit structure of controller is derived for a class of nonlinear discrete time systems. Recursive design of controller using Lyapunov based analysis are well established but contraction...
Motion control systems have found their application in various industry products. Common issues encountered when designing this type of systems are nonlinearities and uncertainties (e.g., unknown parameters, unmodeled dynamics, and disturbances). In this paper, we study controller design for a class of motion systems with rotary components. The systems are assumed to have unknown parameters which...
In this paper we overcome the restriction of conventional integrators and present a unified framework for general integral control (GIC). The necessary conditions and basic principles for designing a general integrator are proposed. All asymptotically stable controllers in conjunction with the limited conditions described can be directly used as integrators and all techniques for designing these controllers...
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