Tracking Control Tasks Research Articles

Sliding-mode repetitive learning control with integral sliding-mode perturbation compensation

This paper proposes a sliding-mode repetitive learning control (SMRLC) scheme with an integral sliding-mode perturbation observer (ISMPO) for repetitive tracking control tasks. The three control strategies which are synthesized to yield excellent tracking performance are: (1) the pole-placement feedback control to specify the desired error dynamics; (2) ISMPO-based feedback compensation as the robust part; and (3) a feedforward learning component that refines the control to improve system performance through repetitive trials. The ISMPO-based feedback compensation ensures that there is only small tracking error during initial learning trials and enhances system insensitivity to exceptional and aperiodic disturbances. The feedforward learning compensation is updated according to a certain switching signal that is equivalent to the compensation error of the feedforward control, yielding fast convergence of the learning process from trial to trial. Experimental results demonstrate the feasibility of the proposed scheme.

Robust control of robotic manipulators based on integral sliding mode

For rigid body robot manipulators, the computed torque approach provides asymptotic stability for tracking control tasks. However, the state dependent matrices needed to complete the computed torque algorithm are normally unknown and possibly too complex for a real-time implementation. This paper proposes a simple controller with computed-torque-like structure enhanced by integral sliding mode, having pole-placement capability. For the reduction of the chattering effect generated by the sliding mode part, the integral sliding mode is posed as a perturbation estimator with quasi-continuous control action provided by an additional low-pass filter. The time-constant of the latter tunes the controller functionality between the perturbation compensation and a pure integral sliding mode control, as well as between chattering reduction and system robustness. A comparative simulation study between conventional sliding mode control, integral sliding mode control, and integral sliding mode in form of a perturbation estimator for a two-link robot arm validates the proposed design.

Sliding Mode Control

Sliding Mode Control

Neuro‐adaptive sliding‐mode tracking control of robot manipulators

AbstractIn this work, a new dynamical on‐line learning algorithm for robust model‐free neuro‐adaptive control of a class of nonlinear systems with uncertain dynamics is proposed and experimentally tested in order to evaluate its performance and practical feasibility in industrial settings. The control application studied is the trajectory tracking control task for the first three joints of an open architecture articulated robot manipulator. The control scheme makes use of variable structure systems theory and the feedback‐error‐learning concept. An inner sliding motion is established in terms of the neurocontroller parameters, aiming to lead the error in its control signal towards zero. The outer sliding motion bears on the system under control, the state tracking error vector of which is simultaneously driven towards the origin of the phase space. The existing relation between the two sliding motions is shown. Experimental results illustrate that the proposed neural‐network‐based controller possesses a remarkable learning capability to control complex dynamical systems, virtually without requiring a priori knowledge of the plant dynamics and laborious start‐up procedures. Copyright © 2007 John Wiley & Sons, Ltd.

On nonlinear H ∞ sliding mode control for a class of nonlinear cascade systems

In this work two main robust control strategies, the sliding mode control (SMC) and nonlinear H ∞ control, are integrated to function in a complementary manner for tracking control tasks. The SMC handles matched L ∞[0,∞) type system uncertainties with known bounding functions. H ∞ control deals with unmatched disturbances of L 2[0,∞) type where the upper-bound knowledge is not available. The new control method is designed for a class of nonlinear uncertain systems with two cascade subsystems. Nonlinear H ∞ control is applied to the first subsystem in the presence of unmatched disturbances. Through solving a Hamilton-Jacoby inequality, the nonlinear H ∞ control law for the first subsystem well defines a nonlinear switching surface. By virtue of nonlinear H ∞ control, the resulting sliding manifold in the sliding phase possesses the desired L 2 gain property and to a certain extend the optimality. Associated with the new switching surface, the SMC is applied to the second subsystem to accomplish the tracking task, and ensure the L 2 gain robustness in the reaching phase. Two illustrative examples are given to show the effectiveness of the proposed robust control scheme.

A NOVEL CONTROL STRUCTURE FOR DYNAMIC INVERSION AND TRACKING TASKS

A new structure for model inversion and tracking control tasks is introduced. It represents a two-degree of freedom controller, which unifies the principle of feedforward exact and high-gain feedback inversion, while preserving advantages of each.

On iterative learning from different tracking tasks in the presence of time-varying uncertainties.

In this paper, we introduce a new iterative learning control (ILC) method, which enables learning from different tracking control tasks. The proposed method overcomes the imitation of traditional ILC in that, the target trajectories of any two consecutive iterations can be completely different. For non-linear systems with time-varying and time-invariant parametric uncertainties, the new learning method works effectively to nullify the tracking error. To facilitate the learning control system design and analysis, in the paper we use a composite energy function (CEF) index, which consists of a positive scalar function and L2 norm of the function approximation error.

A new fuzzy logic learning control scheme for repetitive trajectory tracking problems

In this paper, we consider repeatable tracking control tasks using a new control approach—PD type fuzzy logic learning control (FLLC). FLLC integrates two main control strategies: fuzzy logic control as the basic control part and learning control as the refinement part. The new FLLC is constructed by simply adding an iterative learning mechanism to a fuzzy PD controller. The incorporation of the learning function into fuzzy PD controllers ensures exact tracking because it completely nullifies the effects of reference signal and periodic disturbances on the tracking error. Through the rigorous proof based on energy function and functional analysis, we show that the proposed FLLC system achieves the following novel properties: (1) the tracking error sequence converges uniformly to zero; (2) learning control sequence converges to the desired control profile almost everywhere.

Learning variable structure control approaches for repeatable tracking control tasks

In this paper, we consider repeatable tracking control tasks using a new control approach—learning variable structure control (LVSC). LVSC synthesizes two main control strategies: variable structure control (VSC) as the robust part and learning control as the intelligent part. The incorporation of the powerful learning function, by virtue of the internal model principle, completely nullifies the tracking error. The switching control mechanism on the other hand, retains the well appreciated properties of VSC, especially theinsensitivity to unstructured system uncertainties. Through a rigorous proof based on energy function and functional analysis, we show that the LVSC system achieves the following novel properties: (1) the tracking error sequence converges uniformly to zero; (2) the bounded learning control sequence converges to the equivalent control, i.e. the desired control profile almost everywhere; (3) the system state sequence and VSC control sequence are uniformly continuous. To address important practical considerations, the learning mechanism is implemented by means of Fourier series expansions, hence achieves better tracking performance.

A Gain Scheduled Sliding Mode Control Scheme Using Filtering Techniques With Applications to Multilink Robotic Manipulators

In this paper, a gain scheduled sliding mode control (SMC) scheme is proposed for tracking control tasks of multilink robotic manipulators. In the new scheme, filtering techniques play the key role in acquiring equivalent control signals and scheduling the switching control gain automatically. Once the system enters the sliding motion, two classes of low-pass filters are introduced to work concurrently for the purpose of acquiring equivalent control, reducing the switching gain effectively, and as a result eliminating chattering. By virtue of equivalent control theory, one class of filters is designed to capture the “average” profile of the switching quantity, which is in proportion to the desired control input. Meanwhile, another class of low-pass filters is added to scale down the gain of the switching control. The convergence property of the proposed control scheme is rigorously analyzed in time domain and the frequency domain knowledge can be easily incorporated into the construction of the two classes of filters. Excellent tracking performance is achieved with the direct manipulation of switching control input using filtering technology and with the integration of both time domain and frequency domain system knowledge in controller design. [S0022-0434(00)01604-X]

Fuzzy Human-Machine Collaborative Control of a Nonlinear Plant

A fuzzy self-learning human-machine collaborative controller is developed using fuzzy self-learning control and fuzzy mixing. The fuzzy self-learning controller learns to emulate a human operator in a tracking control task. The fuzzy mixer combines the human control actions and the fuzzy controller's actions according to a human performance pleasure and a measure of confidence related to the fuzzy controller's action. The fuzzy collaborative controller is applied to a nonlinear two-tank plant, assisting the human operator in a collaborative fashion, and achieving better results than either the human or the machine could achieve

A comparative study of on‐the‐road and simulated driving performance after nocturnal treatment with lormetazepam 1 mg and oxazepam 50 mg

AbstractThe main purpose of this study was to compare the sensitivity of a driving simulator test model (TS2) with a standard on‐the‐road driving test, after one night treatment with lormetazepam 1 mg, oxazepam 50 mg (as a verum) and placebo. The secondary purpose was to measure the effects of the intended drugs and placebo in the same subject sample, after two treatment nights in the morning and in the afternoon, on on‐the‐road driving performance. Eighteen healthy male volunteers received the three treatments (2 consecutive nights each) according to a double‐blind, three‐way crossover design. Time of administration was set at 22.00 hours each night. An on‐the‐road driving test and a simulator driving test were conducted in the morning following the first night. After the second treatment night, on‐the‐road driving tests were performed in the morning and in the afternoon. The on‐the‐road driving test consisted of operating an instrumental automobile over a 100 km highway circuit at a constant speed (90 km/h) and constant steady lateral position between the right lane boundaries. Primary performance measure was the SD of lateral position (SDLP). The simulator test consisted of repeatedly performing ‘curve‐following’ manoeuvres, which was the main tracking control task, while simultaneously reacting to secondary visual signs. Test parameters were the number of correctly executed manoeuvres (TC) and reaction time (RT). Oxazepam 50 mg seriously impaired, and lormetazepam 1 mg slightly impaired, on‐the‐road driving performance in the morning, both after the first and second treatment night. The drugs produced no significant effects in the afternoon test following the second night. In contrast with these results, neither oxazepam 50 mg nor lormetazepam 1 mg affected simulator tracking control after one night. No deterioration was found for reaction time. Correlational and multiple regression analyses were applied to determine relationships between SDLP, TC and RT. The major conclusion of this study was that the TS2 driving simulator test does not predict residual drug effects in the on‐the‐road driving test, and seems to be a less sensitive measure of sedative drug‐induced impairment in contrast to the on‐the‐road driving test.

Cardiac sympathetic and parasympathetic activity during self-regulation of heart period.

Fourteen subjects attempted to increase and 15 attempted to decrease cardiac interbeat interval (IBI) while being provided with biofeedback of IBI, T-wave vector magnitude (TWVM), or respiratory sinus arrhythmia (RSA). Subjects in both groups showed directional change in IBI relative to a tracking control task, but the three types of feedback did not differentially affect performance. Voluntary IBI increases were associated with significant increases in TWVM and RSA, and voluntary IBI decreases were associated with significant reductions in RSA and nonsignificant reductions in TWVM. This pattern of results suggests that alterations in cardiac vagal tone are involved in voluntary IBI increase and decrease tasks. The results also suggest a role for cardiac sympathetic nervous activity in voluntary IBI increase. The role of changing respiration cycle period was also investigated.