Saturating-proportional-derivative-type global continuous control for the finite-time or (local) exponential stabilisation of mechanical systems with bounded inputs is achieved involving the desired conservative-force compensation. Far from what one could expect, the proposed controller is not a simple extension of the on-line compensation case but it rather proves to entail a closed-loop analysis with a considerably higher degree of complexity. This gives rise to more involved requirements to guarantee its successful performance and implementability. Interesting enough, the proposal even shows that actuators with higher power-supply capabilities than in the on-line compensation case are required. Other important analytical limitations are further overcome through the developed algorithm. Experimental tests on a two-degree-of-freedom robotic arm corroborate the efficiency of the proposed scheme.

This study is concerned with an adaptive event-triggered control problem for non-linear non-strict-feedback systems subject to actuator failures. For actuator failures, both total loss of effectiveness (TLOE) and partial loss of effectiveness (PLOE) are considered. The event-triggered mechanism is proposed in this study, which may influence measurement errors. Neural networks (NNs) are used to approximate unknown non-linear functions, and a neural observer is designed to estimate unknown state variables. Then a neural tracking controller is constructed to reduce the communication burden via backstepping technique. The new controller ensures that the output of the system reaches to the same trajectory with the reference signal, and it also guarantees the boundedness of all the closed-loop signals. Finally, a simulation example is used to testify the results.

In this study, a novel two-step model predictive control (MPC) for Hammerstein systems subject to norm-bounded disturbance is addressed. In the first step, the intermediate control law for the linear part of the system is posed as the solution to the unconstrained MPC problem that minimises a quadratic cost function over a given finite time, for which the solution is determined by a novel Riccati iterative equation. In the second step, the actual control move is obtained by solving non-linear algebraic equation group and desaturation. The quadratic boundedness technique is used to specify the stability for closed-loop system with norm-bounded disturbance, and the sufficient conditions for quadratic convergent of the system state are presented. Simulation results demonstrate the effectiveness of the proposed approach to this class of systems.

In this study, the authors consider the input-to-state stability of an ordinary differential equation (ODE)–heat cascade system with Dirichlet interconnection where the boundary control input is located at the right end of the heat equation and the disturbance is appeared as a non-homogeneous term in the ODE. Based on two backstepping transformations, they design a state feedback control law that guarantees the input-to-state stability of the closed-loop system. The well-posedness of the closed-loop system is presented by using the semi-group approach. Moreover, they design an output feedback control law by constructing an exponentially convergent observer. With the output feedback control, the input-to-state stability of the resulting closed-loop system is proven.

This study addresses the leader-tracking problem for linear multi-agent systems in the presence of both parameter model uncertainties and time-varying communication delays. To solve the robust output consensus problem, a delayed distributed proportional–integral–derivative control is proposed and the overall closed-loop stability is proven by exploiting the Lyapunov–Krasovskii theory. Delay-dependent robust stability conditions are given via linear matrix inequalities which allow the proper tuning of robust control gains. The effectiveness of the theoretical derivation is confirmed through a numerical analysis in the practical application domain of cooperative driving for connected vehicles.

In this study, the main concern lies in the development of a boundary control scheme for suppressing the vibration of an axially moving system under the influence of unknown disturbances. To that end, a boundary adaptive output feedback control is presented by merging Lyapunov theory, observer backstepping technique, high-gain observers and robust adaptive control theory to globally stabilise the vibration and compensate for the uncertainties of the system. Besides, the disturbance observer dynamics is developed to handle unknown boundary disturbance and the -modification is adopted to adjust the robustness of the system. Under the designed control, the uniformly bounded stability of the controlled system is achieved, and the state observer error exponentially converges to zero as time tends to infinity. Simulation results illustrate that the proposed control can stabilise the system with a better performance.

This study investigates the distributed control strategy for handling the time-invariant and time-varying formation tracking problem of a class of non-linear nonidentical leader–follower multi-agent systems with uncertainties and unknown external disturbances. Firstly, the neighbour-based distributed finite-time observers are proposed for the followers to estimate the position and velocity of the leader. Then, two novel distributed adaptive formation control algorithms based on the approximation capability of neural networks and sliding mode are developed. One can prove that, with the proposed observer and the control laws, all the followers will reach the desired time-invariant or time-varying formation tracking, and achieve the consistent velocity with the leader in spite of uncertainties and disturbances. Finally, illustrative simulation examples are given to verify the effectiveness of the obtained theoretical results.

Distributed cooperative consensus tracking problem for a class of uncertain multi-agent systems with a high-dimensional leader under a directed communication topology is concerned. Compared with related works, the dynamics of the leader agent is allowed to be different from those of the followers and may not be measured. Meanwhile, the dynamics of each follower is subject to un-modelled dynamics, unknown time-varying delays, as well as external disturbances, which makes the model more suitable in various practical applications. Based on the -matrix and Lyapunov–Krasovskii functional method, a distributed robust radial basis function neural network controller as well as a local observer is designed for each follower so as to guarantee the ultimate boundness of the tracking errors to the leader's output signals. By appropriately cutting down the neural network parameters to be updated online, the computational burden can be greatly reduced. The effectiveness of the proposed consensus approach is testified via a numerical example.

This study investigates the formation tracking control for second-order leader–follower multi-agent systems. To estimate the velocity and acceleration of the leader, distributed observers are constructed for each follower. Based on the observed states, the authors propose a novel distributed formation tracking control protocol and analyse two communication networks with and without communication delays. Then, they prove that each agent can follow the active leader closely and form the desired spatial pattern. Finally, simulation examples are provided to show the validity of the theoretical results.

In this study, a novel data-driven direct automatic tuning scheme is proposed for the general form of fixed-structure digital controllers of hybrid systems. The controller is directly auto-tuned while it interacts with the real plant; no modelling or plant identification procedure is involved. The auto-tuning problem is formulated as a controller parameter optimisation problem, which is solved using a quasi-Newton method. A new method for calculating the gradient of the objective function from closed-loop experiment data is proposed to reduce the sensitivity to the initial values of the parameters. Domain knowledge of controller design based on pole placement and internal model principle is introduced as a priori information to further improve the performance of the auto-tuning. Numerical simulations are conducted to demonstrate the effectiveness of the proposed scheme and performances of different objective functions are evaluated. The performance of the proposed scheme is compared with the standard relay feedback and iterative feedback tuning methods. Reliable controllers can be obtained automatically by using the proposed scheme; the only remaining task of the control engineer is to estimate the order of the plant by counting the number of independent energy storage elements.

This study investigates a switching control problem for a set of plants which share communication channels, and for which medium-access control is achieved based on contention-based protocols such as the binary exponential back-off algorithm. The medium-access procedure can be modelled by a Markov chain with completely or partially known transition probabilities. The entire networked system operates as a stochastic switching system (SSS). Based on the average dwell time technique, guaranteed cost control method, and SSS theory, sufficient conditions are derived to establish the relation between the mean-square exponential stability and the property of the Markov chain-driven medium-access control for the SSS. Depending on these conditions, the designed feedback controller can stabilise the SSS using the mean-square exponential and guarantee the desired robust performance. The proposed control framework is extended and applied to the cooperative adaptive cruise control of vehicles within the context of intelligent vehicle highway systems, which can reject the disturbance resulting from the fluctuating acceleration of preceding vehicles. Numerical simulations and experiments using laboratory-scale cars show the effectiveness of the proposed control method.

This study presents an output-feedback controller for a cart–pole system via coupled sliding-mode algorithms. Due to the fact that this system is underactuated, a special type of sliding surface is employed instead of using a transformation to the regular form. Then, a continuous sliding-mode controller is designed using the super-twisting algorithm so that robustness of the control scheme is guaranteed. Since the proposed controller requires the knowledge of the velocities, a homogeneous sliding-mode observer is introduced to estimate such velocities. Then, in order to improve the control effort, a variable gain super-twisting algorithm is also proposed. The closed-loop stability is also guaranteed by means of input-to-state stability properties and Lyapunov function approaches. All the results are validated by means of real-time experiments, providing also a comparison between the proposed controllers and a chattering attenuation approach.

This study investigates the non-fragile sampled-data guaranteed cost control problem for a bio-economic singular Markovian jump system that is represented by the Takagi–Sugeno fuzzy model. The main intention of this study is to design a non-fragile sampled-data controller for the considered model to handle the issue of tax fluctuations by means of showing that the closed-loop system is regular, impulse free and stochastically finite-time bounded. Sampled-data controller is the one where the continuous system is controlled by the digital control algorithms. By introducing a proper Lyapunov–Krasovskii functional and using linear matrix inequality (LMI) approach, a new set of criteria is obtained in terms of LMIs for achieving the required result. More precisely, by solving LMIs, an upper bound for the cost function can be obtained. Finally, a simulation result is given to illustrate the effectiveness of the proposed control design.

In this study, the authors propose a novel sampled-data fuzzy chaotic synchronisation scheme under the input constraints consideration. The sampled-data chaos synchronisation controller feedbacks the synchronisation error between the state vectors of both the drive chaotic system and the response chaotic system at a constant sampling period. The chaotic synchronisation is achieved by stabilising the synchronisation error dynamics based on the H-infinity criterion. Linear matrix inequality-based sufficient conditions for synchronising two identical chaotic systems are derived based on the newly proposed time-dependent fuzzy Lyapunov–Krasovskii functional. Unlike previous approaches, the modelling error term is fully addressed so as to enhance the synchronisation performance. Finally, the effectiveness of the proposed method is validated through a numerical example.

The internal model controller-proportional–integral–derivative (IMC-PID) is one of the simplest model based proportional–integral–derivative (PID) design methods available in the literature. In IMC-PID design, the three variable PID design problem is converted into the single variable design problem with the help of the internal model of the process and an internal model controller (IMC) filter. The IMC filter is used to control the robustness of the process and transient performance. The IMC filter, however, introduces additional phase lag in the process, due to which the stability of the process is compromised to maintain the transient response. In this study, a new indirect design approach of IMC-PID controller is proposed for first-order filter with dead time (FOPDT) and second-order filter with dead time (SOPDT) processes, in which the IMC-PID controller is designed for the pole-zero shifted process instead of original process and the process robustness is tuned with the help of the proposed shifting constant . The proposed design does not involve any IMC filter, which helps to achieve a better IAE and higher Phase Margin at desired Maximum Sensitivity. The proposed method is validated with the help of real-time experiment on water level control of Canonical Tank System and MATLAB simulation examples of the FOPDT and SOPDT processes.

This study addresses the problem of state estimation for discrete-time, linear time invariant systems subject to packet losses, which occur in specific regions of the state space. Most practical estimation problems are characterised by occurrences of loss of observation packets, which makes the packet arrival process a non-stationary statistic, making the analysis and design of such an estimator challenging. This estimation problem subject to state-dependent packet losses is formulated using a state-dependent hybrid measurement model and solved using the projection theorem-based approach to obtain minimum mean square error state estimates. By systematically utilising the a priori information of the regions where the packet loss is likely to occur, the proposed estimator takes the Kalman filter structure with the modified algebraic Riccati iteration for the error covariance matrix being stochastic due to the probabilistic packet arrival process. Finally, the proposed estimator is demonstrated using an illustrative two-dimensional aircraft tracking example with state-dependent packet loss and is shown to have improved performance over the baseline packet loss algorithm.