Showing papers on "Robust control published in 2003"

Continuous finite-time control for robotic manipulators with terminal sliding modes

Abstract: A continuous finite-time control scheme for rigid robotic manipulators is proposed using a new form of terminal sliding modes. The robustness of the controller is established using the Lyapunov stability theory. Theoretical analysis and simulation results show that faster and high-precision tracking performance is obtained compared with the conventional continuous sliding mode control method.

A robust, real-time control scheme for multifunction myoelectric control

Abstract: This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.

Immersion and invariance: a new tool for stabilization and adaptive control of nonlinear systems

Abstract: A new method to design asymptotically stabilizing and adaptive control laws for nonlinear systems is presented. The method relies upon the notions of system immersion and manifold invariance and, in principle, does not require the knowledge of a (control) Lyapunov function. The construction of the stabilizing control laws resembles the procedure used in nonlinear regulator theory to derive the (invariant) output zeroing manifold and its friend. The method is well suited in situations where we know a stabilizing controller of a nominal reduced order model, which we would like to robustify with respect to higher order dynamics. This is achieved by designing a control law that asymptotically immerses the full system dynamics into the reduced order one. We also show that in adaptive control problems the method yields stabilizing schemes that counter the effect of the uncertain parameters adopting a robustness perspective. Our construction does not invoke certainty equivalence, nor requires a linear parameterization, furthermore, viewed from a Lyapunov perspective, it provides a procedure to add cross terms between the parameter estimates and the plant states. Finally, it is shown that the proposed approach is directly applicable to systems in feedback and feedforward form, yielding new stabilizing control laws. We illustrate the method with several academic and practical examples, including a mechanical system with flexibility modes, an electromechanical system with parasitic actuator dynamics and an adaptive nonlinearly parameterized visual servoing application.

Min-max control of constrained uncertain discrete-time linear systems

Abstract: For discrete-time uncertain linear systems with constraints on inputs and states, we develop an approach to determine state feedback controllers based on a min-max control formulation. Robustness is achieved against additive norm-bounded input disturbances and/or polyhedral parametric uncertainties in the state-space matrices. We show that the finite-horizon robust optimal control law is a continuous piecewise affine function of the state vector and can be calculated by solving a sequence of multiparametric linear programs. When the optimal control law is implemented in a receding horizon scheme, only a piecewise affine function needs to be evaluated on line at each time step. The technique computes the robust optimal feedback controller for a rather general class of systems with modest computational effort without needing to resort to gridding of the state-space.

Robust load frequency control using genetic algorithms and linear matrix inequalities

Abstract: In this paper, two robust decentralized control design methodologies for load frequency control (LFC) are proposed. The first one is based on H/sub /spl infin// control design using linear matrix inequalities (LMI) technique in order to obtain robustness against uncertainties. The second controller has a simpler structure, which is more appealing from an implementation point of view, and it is tuned by a proposed novel robust control design algorithm to achieve the same robust performance as the first one. More specifically, genetic algorithms (GAs) optimization is used to tune the control parameters of the proportional-integral (PI) controller subject to the H/sub /spl infin// constraints in terms of LMI. Hence, the second control design is called GALMI. Both proposed controllers are tested on a three-area power system with three scenarios of load disturbances to demonstrate their robust performances.

Robust adaptive tracking for time-varying uncertain nonlinear systems with unknown control coefficients

Abstract: Presents a robust adaptive control approach for a class of time-varying uncertain nonlinear systems in the strict feedback form with completely unknown time-varying virtual control coefficients, uncertain time-varying parameters and unknown time-varying bounded disturbances. The proposed design method does not require any a priori knowledge of the unknown coefficients except for their bounds. It is proved that the proposed robust adaptive scheme can guarantee the global uniform ultimate boundedness of the closed-loop system signals and disturbance attenuation.

Fuzzy adaptive sliding-mode control for MIMO nonlinear systems

Abstract: A stable adaptive fuzzy sliding-mode controller is developed for nonlinear multivariable systems with unavailable states. When the system states are not available, the estimated states from a semi-high gain observer are used to construct the output feedback fuzzy controller by incorporating the dynamic sliding mode. It is proved that uniformly asymptotic output feedback stabilization can be achieved with the tracking error approaching to zero. A nonlinear system simulation example is presented to verify the effectiveness of the proposed controller.

Robust stability analysis and fuzzy-scheduling control for nonlinear systems subject to actuator saturation

Abstract: Takagi-Sugeno (TS) fuzzy models can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input-output submodels. In this paper, the TS fuzzy modeling approach is utilized to carry out the stability analysis and control design for nonlinear systems with actuator saturation. The TS fuzzy representation of a nonlinear system subject to actuator saturation is presented. In our TS fuzzy representation, the modeling error is also captured by norm-bounded uncertainties. A set invariance condition for the system in the TS fuzzy representation is first established. Based on this set invariance condition, the problem of estimating the domain of attraction of a TS fuzzy system under a constant state feedback law is formulated and solved as a linear matrix inequality (LMI) optimization problem. By viewing the state feedback gain as an extra free parameter in the LMI optimization problem, we arrive at a method for designing state feedback gain that maximizes the domain of attraction. A fuzzy scheduling control design method is also introduced to further enlarge the domain of attraction. An inverted pendulum is used to show the effectiveness of the proposed fuzzy controller.

Adaptive neural network control of nonlinear systems with unknown time delays

Abstract: In this note, adaptive neural control is presented for a class of strict-feedback nonlinear systems with unknown time delays. Using appropriate Lyapunov-Krasovskii functionals, the uncertainties of unknown time delays are compensated for such that iterative backstepping design can be carried out. In addition, controller singularity problems are solved by using the integral Lyapunov function and employing practical robust neural network control. The feasibility of neural network approximation of unknown system functions is guaranteed over practical compact sets. It is proved that the proposed systematic backstepping design method is able to guarantee semiglobally uniformly ultimate boundedness of all the signals in the closed-loop system and the tracking error is proven to converge to a small neighborhood of the origin.

Asymptotic stabilization via output feedback for lower triangular systems with output dependent incremental rate

Abstract: We study the global asymptotic stabilization by output feedback for systems whose dynamics are in a feedback form and where the nonlinear terms admit an incremental rate depending only on the measured output. The output feedback we consider is of the observer-controller type where the design of the controller follows from standard robust backstepping. The novelty is in the observer which is high-gain such as with a gain coming from a Riccati equation.

Robust sliding-mode control for uncertain time-delay systems: an LMI approach

Abstract: This note is devoted to robust sliding-mode control for time-delay systems with mismatched parametric uncertainties. A delay-independent sufficient condition for the existence of linear sliding surfaces is given in terms of linear matrix inequalities, based on which the corresponding reaching motion controller is also developed. The results are illustrated by an example.

Gain-scheduled wheel slip control in automotive brake systems

Abstract: A wheel slip controller is developed and experimentally tested in a car equipped with electromechanical brake actuators and a brake-by-wire system. A gain scheduling approach is taken, where the vehicle speed is viewed as a slowly time-varying parameter and the model is linearized about the nominal wheel slip. Gain matrices for the different operating conditions are designed using an LQR approach. The stability and robustness of the controller are studied via Lyapunov theory, frequency analysis, and experiments using a test vehicle.

Automated lane change controller design

Abstract: The primary focus of study in this paper is the background control theory for automated lane change maneuvers. We provide an analytic approach for the systematic development of controllers that will cause an autonomous vehicle to accomplish a smooth lane change suitable for use in an Automated Highway System. The design is motivated by the discontinuous availability of valid preview data from the sensing systems during lane-to-lane transitions. The task is accomplished by the generation of a virtual yaw reference and the utilization of a robust switching controller to generate steering commands that cause the vehicle to track that reference. In this way, the open loop lane change problem is converted into an equivalent virtual reference trajectory tracking problem. The approach considers optimality in elapsed time at an operating longitudinal velocity. Although the analysis is performed assuming that the road is straight, the generalization of the proposed algorithm to arbitrary road segments is rather straightforward. The outlined lane change algorithm has been implemented and tested on The Ohio State University test vehicles. Some of the experimental results are presented at the conclusion of this paper.

Robust nonlinear motion control of a helicopter

Abstract: We consider the problem of controlling the vertical motion of a nonlinear model of a helicopter, while stabilizing the lateral and horizontal position and maintaining a constant attitude. The reference to be tracked is given by a sum of a constant and a fixed number of sinusoidal signals, and it is assumed not to be available to the controller. This represents a possible situation in which the controller is required to synchronize the vehicle motion with that of an oscillating platform, such as the deck of a ship in high seas. We design a nonlinear controller which combines recent results on nonlinear adaptive output regulations and robust stabilization of systems in feedforward form by means of saturated controls. Simulation results show the effectiveness of the method and its ability to cope with uncertainties on the plant and actuator model.

Input-output linearization and zero-dynamics control of three-phase AC/DC voltage-source converters

Abstract: Nonlinear differential-geometric techniques are proposed for the design of a feedback controller for three-phase voltage-source pulsewidth modulation (PWM) AC/DC boost converters under a cascade control structure. The input-output linearizability of the system modeled in d-q synchronous reference frames is examined. The results lead to a decoupled d-q current control scheme. For the internal DC-bus voltage dynamics (zero dynamics), by considering the d-axis current command as control input and using a square transform on DC-bus voltage, it is shown that this remaining system contains an input memoryless nonlinearity of conic sector type and satisfies the conditions for a Lur'e plant. This suggests a new outer-loop control strategy to control the square of the DC output voltage, rather than DC output voltage itself, utilizing a simple proportional plus integral (PI) controller cascaded to the d-axis current loop. Since most results do not consider the control of zero dynamics, the strategy for the control of zero dynamics via cascade control structure may provide valuable insight for the design of input-output linearized systems in which zero dynamics contain some desired control variables. The absolute tracking concept for Lur'e plants is introduced to prove the global tracking capability with zero steady-state error of the voltage loop. The controlled PWM AC/DC converter has the features of global stability, fast (exponential) tracking of DC-bus voltage command with zero steady-state error, asymptotic rejection of load disturbance, robustness against parameter uncertainties and decoupled dynamical responses between d and q current loops. Also, measurement of load current is not required. All these features are confirmed via laboratory experiments on a 1.5 kVA PC-based controlled prototype.

On the robustness and performance of disturbance observers for second-order systems

Abstract: The disturbance observer (DOB) has been widely utilized for high-precision and high-speed motion control applications. In this note, we suggest the robustness measure of the DOB as a criterion to design the robust DOB systems. Also, we suggest its design guidelines especially for second-order systems. Experimental results for an optical disk drive system show the validity of design guidelines.

Helicopter trimming and tracking control using direct neural dynamic programming

Abstract: This paper advances a neural-network-based approximate dynamic programming control mechanism that can be applied to complex control problems such as helicopter flight control design. Based on direct neural dynamic programming (DNDP), an approximate dynamic programming methodology, the control system is tailored to learn to maneuver a helicopter. The paper consists of a comprehensive treatise of this DNDP-based tracking control framework and extensive simulation studies for an Apache helicopter. A trim network is developed and seamlessly integrated into the neural dynamic programming (NDP) controller as part of a baseline structure for controlling complex nonlinear systems such as a helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industrial scale nonlinear validated model of the Apache helicopter. This is probably the first time that an approximate dynamic programming methodology has been systematically applied to, and evaluated on, a complex, continuous state, multiple-input multiple-output nonlinear system with uncertainty. Though illustrated for helicopters, the DNDP control system framework should be applicable to general purpose tracking control.

Robust model predictive control for nonlinear discrete-time systems

Abstract: This paper describes a model predictive control (MPC) algorithm for the solution of a state-feedback robust control problem for discrete-time nonlinear systems. The control law is obtained through the solution of a finite-horizon dynamic game and guarantees robust stability in the face of a class of bounded disturbances and/or parameter uncertainties. A simulation example is reported to show the applicability of the method. Copyright © 2003 John Wiley & Sons, Ltd.

Active Structural Vibration Control: A Review

Abstract: In this paper we review essential aspects in- volved in the design of an active vibration control system. We present a generic procedure to the design process and give selective examples from the literature on relevant ma- terial. Together with examples of their applications, various topics are briefly introduced, such as structure modeling, model reduction, feedback control, feedforward control, con- trollability and observability, spillover, eigenstructure assign- ment (pole placement), coordinate coupling control, robust control, optimal control, state observers (estimators), intelli- gent structure and controller, adaptive control, active con- trol effects on the system, time delay, actuator-structure interaction, and optimal placement of actuators.

Positive polynomials and robust stabilization with fixed-order controllers

Abstract: Recent results on positive polynomials are used to obtain a convex inner approximation of the stability domain in the space of coefficients of a polynomial. An application to the design of fixed-order controllers robustly stabilizing a linear system subject to polytopic uncertainty is then proposed, based on linear matrix inequality optimization. The key ingredient in the design procedure resides in the choice of the central polynomial. Several numerical examples illustrate the relevance of the approach.

Robust H/sub /spl infin// filtering of linear systems with time-varying delay

Abstract: A robust delay-dependent H/sub /spl infin// filtering design is proposed for linear continuous systems with parameter uncertainty and time-varying delay. The resulting filter is of the general linear observer type and it guarantees that the induced L/sub 2/-norm of the system, relating the exogenous signals to the estimation error, is less than a prescribed level for all possible parameters that reside in a given polytope. Our design is based on the application of the descriptor model transformation and Park's inequality for the bounding of cross terms and is expected to be the least conservative as compared to existing design methods. A numerical example indeed demonstrates this advantage of the new filtering scheme.

Adaptive backstepping control and friction compensation for AC servo with inertia and load uncertainties

Abstract: An adaptive backstepping control with friction compensation scheme is presented. A third-order linear dynamic model is used for the AC motor control system design while the LuGre dynamic friction model with nonuniform friction force variations characterizes the friction force. Nonlinear adaptive control laws are designed to compensate the unknown system parameters and disturbances. System robustness and asymptotic position tracking performance are shown through simulation and experimental results.

An analysis and design of bilateral control based on disturbance observer

Abstract: In this paper, an analysis and design of bilateral control based on disturbance observer are discussed Poles of bilateral control go to the poles of position control and poles of force control respectively when disturbance observer gain is infinite Four-channel controller which is hybrid of position and force in the acceleration dimension based on disturbance observer can be divided into two modes: common and differential modes Position is controlled in differential mode space, force is controlled in common mode space Relationship between these spaces is revealed, design of bilateral control in the virtual space is proposed According to this method, design of bilateral control can be treated as position control and force control about one robot separately These theories are applied to the 6-degree-of-freedom industrial manipulator The numerical and experimental results show viability of the proposed method

A fixed-frequency quasi-sliding control algorithm: application to power inverters design by means of FPGA implementation

Abstract: In this paper, a fixed-frequency quasi-sliding control algorithm based on switching surface zero averaged dynamics (ZAD) is reported. This algorithm is applied to the design of a buck-based inverter, and implemented in a laboratory prototype by means of a field programmable gate array (FPGA), taking into account processing speed versus computational complexity trade-off. Three control laws, namely sliding control (SC), fixed-frequency quasi-sliding ZAD and PWM-based control have been experimentally tested to highlight the features of the proposed algorithm. According to the experimental results presented in the paper, the ZAD algorithm fulfills the requirement of fixed switching frequency and exhibits similar robustness properties in the presence of perturbations to those of sliding control mode.

Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems

Abstract: This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically; 2) online learning ability of uncertain MIMO nonlinear systems; 3) fast learning speed; 4) fast convergence of tracking errors; 5) adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; 6) robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed controller is superior.

Model predictive control of vehicle maneuvers with guaranteed completion time and robust feasibility

Abstract: A formulation for model predictive control is presented for application to vehicle maneuvering problems in which the target regions need not contain equilibrium points. Examples include a spacecraft rendezvous approach to a radial separation form the target and a UAV required to fly through several waypoints. Previous forms of MPC are not applicable to this class of problems because they are tailored to the control of plants about steady-state conditions. Mixed-integer linear programming is used to solve the trajectory optimizations, allowing the inclusion of non-convex avoidance constraints. Analytical proofs are given to show that the problem will always be completed in finite time and that, subject to initial feasibility, the optimization solved at each step will always be feasible in the presence of a bounded disturbance. The formulation is demonstrated in several simulations, including both aircraft and spacecraft, with extension to multiple vehicle programs.

A decentralized model reference adaptive variable structure controller for large-scale time-varying delay systems

Abstract: In this note, the problem of decentralized model reference adaptive variable structure control for a class of perturbed large-scale systems with varying time-delay interconnections is investigated. Based on the Lyapunov stability theorem, an adaptive variable structure control strategy for solving the robust tracking problem without the knowledge of upper bound of perturbations is developed. The use of adaptive technique is to adapt the unknown upper bound of perturbations so that the objective of globally asymptotical stability is achieved. Once the system enters the sliding manifold, the dynamics of controlled systems are insensitive to matching perturbations. Finally, an example is given to demonstrate the feasibility of the proposed control scheme.

Robust backstepping control for slew maneuver using nonlinear tracking function

Abstract: The backstepping control method provides useful control logic, especially for a cascaded system. Because spacecraft dynamics and kinematics form a cascaded system, the spacecraft slew maneuver problem can be solved using the backstepping control method. However, the simple linear backstepping controller may result in poor design: sluggish motion, trivial nonlinear term cancellation, and excessive control input. To overcome these defects, an effective backstepping control method using a nonlinear tracking function is proposed. The proposed backstepping control method is based on the redesign of the Lyapunov function and careful gain selections. To evaluate the effectiveness of the proposed method, numerical simulations including parameter uncertainties are performed. Simulation results demonstrate that the proposed backstepping controller can achieve the slew maneuver with shorter settling time and smaller peak control torque than existing methods.