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This article studies model reference adaptive control (MRAC) for a class of stochastic discrete time control systems with time delays in the control input. In particular, a unified fully probabilistic control framework is established to develop the solution to the MRAC, where the controller is the minimizer of the Kullback–Leibler divergence between the actual and desired joint probability density functions of the tracking error and the controller. The developed framework is quite general, where all the components within this framework, including the controller and system tracking error, are modeled using probabilistic models. The general solution for arbitrary probabilistic models of the framework components is first obtained and then demonstrated on a class of linear Gaussian systems with time delay in the main control input, thus obtaining the desired results. The contribution of this article is twofold. First, we develop a fully probabilistic design framework for MRAC, referred to as model reference fully probabilistic design, for stochastic dynamical systems. Second, we establish a systematic pedagogic procedure that is based on deriving explicit forms for the required predictive distributions for obtaining the causal form of the randomized controller when input delays are present. [ABSTRACT FROM AUTHOR]

Iterative learning control (ILC) can synthesize the feedforward control signal for the trajectory tracking control of a repetitive task, even when the system has strong nonlinear dynamics. This makes ILC be one of the most popular methods for trajectory tracking control. Restriction on a repetitive task, however, limits its application to multiple trajectories. This article proposes a neural-network-based ILC (NN-ILC) to deal with nonrepetitive tasks very effectively. A position-based ILC is designed to compensate the tracking error, based on which the multiple outputs of the ILC (ILC outputs) for multiple tasks are expressed as a function of the reference position, velocity, and acceleration. The proposed NN-ILC divides the ILC outputs of multiple tasks into two parts: the linear and nonlinear portions. The first part is expressed by a linear function, which is the linear portion of the function of the ILC outputs. The second part is expressed by a nonlinear function, which is estimated by complementary neural networks including a general neural network and a switching neural network. Finally, the two parts are combined and the ILC outputs of multiple tasks are expressed as a neural-network-based function. Two advantages of the proposed NN-ILC are emphasized. First, the ILC outputs of multiple tasks are compressed into a function by the proposed method, and thus, the memories can be saved. Second, in terms of generalizability, the neural-network-based function of the ILC outputs can easily predict position compensation for multiple tasks without extra iterative learning processes. Experimental results on a robot arm show that the proposed NN-ILC method can easily realize the ILC of multiple tasks. It can save memory comparing with the method of storing the data of multiple tasks and can predict the ILC output of any task, which can accelerate the iterative learning process. [ABSTRACT FROM AUTHOR]

Existing state-of-the-art frequency adaptation mechanisms of central pattern generators (CPGs) for robot locomotion control typically rely on correlation-based learning. They do not account for the tracking error that may occur between the actual system motion and CPG output, leading to the loss of precision, unwanted movement, inefficient energy locomotion, and in the worst cases, motor collapse. To overcome this problem, we developed online error-based learning for frequency adaptation of CPGs. The learning mechanism used for error reduction is a novel modification of the dual learner (DL) called dual integral learner (DIL). Being able to reduce tracking and steady-state errors, it can also perform fast and stable learning, adapting the CPG frequency to match the performance of robotic systems. Control parameters of the DIL are more straightforward for complex systems (like walking robots), compared to traditional correlation-based learning, since they correspond to error reduction. Due to its embedded memory, the DIL can relearn quickly and recover spontaneously from the previously learned parameters. All these features are not covered by the existing frequency adaptation mechanisms. We integrated the DIL into a neural CPG-based motor control system for use on different legged robots with various morphologies for evaluation. The results show that: 1) the DIL does not require precise adjustment of its parameters to fit specific robots; and 2) the DIL can automatically and quickly adapt the CPG frequency to the robots such that the entire trajectory of the CPG can be precisely followed with very low tracking and steady-state errors. Consequently, the robots can perform the desired movements with more energy-efficient locomotion compared to the state-of-the-art correlation-based learning mechanism called frequency adaptation through fast dynamical coupling (AFDC). In the future, the proposed error-based learning mechanism for fast online adaptation in robot motor control can be used as a basis for trajectory optimization, universal controllers, and other studies concerning the change of intrinsic or extrinsic parameters. [ABSTRACT FROM AUTHOR]

In this technical brief, the problem of modeling and tracking control for the manipulator with multi-revolute joints in random vibration environment is considered. By analyzing the effect of environment to the mass points and introducing an equivalent stochastic noise process, a stochastic Hamiltonian dynamic model is constructed to describe the motion of the manipulator. Based on the constructed model, a state feedback backstepping controller in vector form is designed such that the unique solution of the closed-loop system is bounded in probability, and the mean square of the tracking error converges to an arbitrarily small neighborhood of zero. [ABSTRACT FROM AUTHOR]

Introduction: Exposure to high +Gz acceleration forces on a centrifuge or in an aircraft can severely decrease cerebral blood perfusion and cause rapid C-induced loss of consciousness. However, milder acceleration may gradually reduce cerebral blood flow and affect cognitive function in subtler ways. This study used lower body negative pressure (LBNP) to mimic +Gz circulatory effects in order to study cerebral hemodynamics and brain function. Methods: Subjects were 15 healthy men, 19-21 yr of age. They were exposed to LBNP at two levels for 5 mm each separated by a 1 0-mm recovery period. The conditions were low (LO), -4.00 kPa (-30 mmHg) and high (HI), -6.67 kPa (-50 mmHg). Variables measured before, during, and after LBNP included cerebral blood flow velocity (CBFV) in the middle cerebral artery, blood oxygen saturation (SaO2), heart rate (HR), blood pressure, P300 of event-related EEC potentials, reaction time, and tracking error. Results: LO significantly reduced CBFV at 4 and 5 mm, increased HR, and decreased the amplitude of P300, but none of the other variables changed from baseline. In contrast, HI produced significant changes in most variables: CBFV decreased at 2 mm and then fell further at 4 and 5 mm, HR increased, and SaO2 decreased. Significant neurocognitive changes included increased latency and reduced amplitude of P300, slower reaction time, and greater tracking error. Conclusion: The higher level of LBNP used here reduced cerebral perfusion sufficiently to impair neurocognitive function. This model may be useful for further studies of these and other variables under closely controlled conditions. [ABSTRACT FROM AUTHOR]

Subject to stability constraints, tracking performance of a plug-in type repetitive control system with periodic inputs is explored in this note. An upper bound for the square Integral of the tracking error over one time period of periodic input signals is derived based on Fourier analysis. The magnitude and the decay rate of the tracking error can be estimated using the harmonics of the input signal within the designed bandwidth. Both computer simulations and experimental results are presented to illustrate the effectiveness of the proposed method. It is also shown that the tracking error can decay significantly in the first two time periods in the proposed repetitive control system if the required bandwidth conditions are satisfied. [ABSTRACT FROM AUTHOR]